{"category":"coding","cohort":{"bank_hash":"8ad4dea4fe0b53a67184c305f645a228a1a0d48425a7787bae5990c7e7fd023e","created":"2026-10-04T08:20:14.082456+00:00","id":"d94021048cfa4a0196430cb74c658bca830a3ee6cc8b49b0fb89b27e6d317915","initiated_trials":108,"protocol_hash":"06e5a0f38a9ad3b58d5afad4822ef747debe2a458340a5e752ea6d016aaa7f52","required_trials":108,"status":"complete"},"disclosure":"No supplied credits or affiliate influence recorded. 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If weights empty return []; if all zero return zeros.","raw_response_hash":"51e2a94d0ae913673032e04ba0cb05cc28ae9c383df34fd6439ca53dd138c784","repetition":1,"request_hash":"4659aff99a17a97c2868b5490fbcad68a5030a71fd1f0f9c82bf8fce6945cf69","request_id":"req_701ae5a81ac34e9a8a42bdaed6c9a45e","sent_at":"2026-10-04T16:44:30.663811+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-allocate","trial_id":"48aea89110fa47b6d4285c7740f436cfc31e1844f101883c91ec755d41c3c79c","usage":{"input_tokens":145,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":138,"output_tokens_details":{"reasoning_tokens":0},"total_tokens":283}},{"answer":"def solve(data):\n    weights = data[\"weights\"]\n    total = data[\"total\"]\n    weight_sum = sum(weights)\n\n    if not weights:\n        return []\n    if weight_sum == 0:\n        return [0] * len(weights)\n\n    allocations = []\n    remainders = []\n    for weight in weights:\n        allocation, remainder = divmod(total * weight, weight_sum)\n        allocations.append(allocation)\n        remainders.append(remainder)\n\n    leftover = total - sum(allocations)\n    order = sorted(\n        range(len(weights)),\n        key=lambda i: (-remainders[i], i)\n    )\n    for i in order[:leftover]:\n        allocations[i] += 1\n\n    return allocations","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.0024,"finished_at":"2026-10-04T16:46:44.980479+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"84d9d7248f341f37059793a19aaf907811bdf034442adc5bea394f5dd28fc1ee","latency_seconds":5.470152,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {total:nonnegative integer,weights:[nonnegative integer,...]}. Allocate proportionally using largest remainders: floor each share, then give leftover cents to largest fractional remainders, ties to lower index. If weights empty return []; if all zero return zeros.","raw_response_hash":"58d23d6842169bd960e52f264074a97b1289340be52de7c2e2b6021f2dc99d80","repetition":2,"request_hash":"4659aff99a17a97c2868b5490fbcad68a5030a71fd1f0f9c82bf8fce6945cf69","request_id":"req_bbfc4072e61847db8e3b81421aca6dce","sent_at":"2026-10-04T16:46:39.510327+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-allocate","trial_id":"0403478099271e8f455a824e1eb3260831af413eed42e95c534baf292a56c0a4","usage":{"input_tokens":145,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":211,"output_tokens_details":{"reasoning_tokens":57},"total_tokens":356}},{"answer":"def solve(data):\n    total = data[\"total\"]\n    weights = data[\"weights\"]\n    weight_sum = sum(weights)\n    if not weight_sum:\n        return [0] * len(weights)\n\n    shares = [divmod(total * weight, weight_sum) for weight in weights]\n    result = [share[0] for share in shares]\n    leftover = total - sum(result)\n    order = sorted(range(len(weights)), key=lambda i: (-shares[i][1], i))\n    for i in order[:leftover]:\n        result[i] += 1\n    return result","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.00152,"finished_at":"2026-10-04T17:00:42.869875+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"84d9d7248f341f37059793a19aaf907811bdf034442adc5bea394f5dd28fc1ee","latency_seconds":3.033686,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {total:nonnegative integer,weights:[nonnegative integer,...]}. Allocate proportionally using largest remainders: floor each share, then give leftover cents to largest fractional remainders, ties to lower index. If weights empty return []; if all zero return zeros.","raw_response_hash":"9e34c4f10a742ae1970424859b539f148b90a4564bb310ca7457ff7aa3df31da","repetition":3,"request_hash":"4659aff99a17a97c2868b5490fbcad68a5030a71fd1f0f9c82bf8fce6945cf69","request_id":"req_6ba6fcc297ca417fb03c3f46264ebea0","sent_at":"2026-10-04T17:00:39.836189+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-allocate","trial_id":"15c625dad17d352d50492be5456f87406abb78a9ca14d4b2fa3dc5705d337465","usage":{"input_tokens":145,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":123,"output_tokens_details":{"reasoning_tokens":0},"total_tokens":268}},{"answer":"import csv\nimport io\nimport re\n\n\ndef solve(data):\n    result = {}\n    reader = csv.reader(io.StringIO(data, newline=\"\"))\n    header = next(reader, None)\n    if header is None:\n        return result\n    names = [h.strip().lower() for h in header]\n    try:\n        ci = names.index(\"customer\")\n        ai = names.index(\"cents\")\n    except ValueError:\n        return result\n    pattern = re.compile(r\"[+-]?[0-9]+\")\n    for row in reader:\n        if not row:\n            continue\n        if ci >= len(row) or ai >= len(row):\n            continue\n        customer = row[ci]\n        if customer.strip() == \"\":\n            continue\n        raw = row[ai].strip()\n        if not pattern.fullmatch(raw):\n            continue\n        result[customer] = result.get(customer, 0) + int(raw)\n    return result","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.003248,"finished_at":"2026-10-04T16:41:50.860803+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"11e981b8633f2f299f5728fa32f09956cf5ab17b7d16ab4d74778b1fd8c8e829","latency_seconds":2.767953,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is CSV text with header customer,cents, including quoted fields. Ignore rows with a blank customer or cents that is not a signed base-10 integer. Sum by customer and return a dictionary, including zero totals. Preserve customer spelling.","raw_response_hash":"e478f72efd901bba4a440b9b2a099331ed323f92519567d4819308f1f8e2741d","repetition":1,"request_hash":"5ea1f0dc30afd91d2c5284b434b5704e43bc065e84e313037664158f1a7b7e45","request_id":"req_011CfhXhi5Bhgn74Buo79UqP","sent_at":"2026-10-04T16:41:48.092850+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-csv","trial_id":"fb26ad9ba1e32410731e8239302937c444db0e2d275bc0537e28c99c6e1a6fb5","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":204,"output_tokens":284,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"import csv\nimport io\nimport re\n\n\ndef solve(data):\n    totals = {}\n    reader = csv.reader(io.StringIO(data, newline=\"\"))\n    header = None\n    ci = 0\n    vi = 1\n    pattern = re.compile(r\"[+-]?[0-9]+\")\n    for row in reader:\n        if header is None:\n            if not row:\n                continue\n            header = [h.strip().lstrip(\"\\ufeff\").lower() for h in row]\n            if \"customer\" in header and \"cents\" in header:\n                ci = header.index(\"customer\")\n                vi = header.index(\"cents\")\n            else:\n                ci = 0\n                vi = 1\n            continue\n        if not row:\n            continue\n        if len(row) <= max(ci, vi):\n            continue\n        name = row[ci]\n        val = row[vi].strip()\n        if name.strip() == \"\":\n            continue\n        if not pattern.fullmatch(val):\n            continue\n        n = int(val)\n        totals[name] = totals.get(name, 0) + n\n    return totals","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.003718,"finished_at":"2026-10-04T16:48:53.563878+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"11e981b8633f2f299f5728fa32f09956cf5ab17b7d16ab4d74778b1fd8c8e829","latency_seconds":2.943939,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is CSV text with header customer,cents, including quoted fields. Ignore rows with a blank customer or cents that is not a signed base-10 integer. Sum by customer and return a dictionary, including zero totals. Preserve customer spelling.","raw_response_hash":"62b8df23705dea1bd3d181f5b4cfcc562a05837ddb4934e254278aeeb618d69a","repetition":2,"request_hash":"5ea1f0dc30afd91d2c5284b434b5704e43bc065e84e313037664158f1a7b7e45","request_id":"req_011CfhYErUpG65ZvzoNGM8CF","sent_at":"2026-10-04T16:48:50.619939+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-csv","trial_id":"43c60304bd72a9c96d99641fd9e2199d1342a22ca1e869cc408b54d2e583e550","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":204,"output_tokens":331,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"import csv\nimport io\nimport re\n\ndef solve(data):\n    result = {}\n    reader = csv.reader(io.StringIO(data, newline=\"\"))\n    header = None\n    for row in reader:\n        if not row or all(not c.strip() for c in row):\n            continue\n        header = [c.strip().lower() for c in row]\n        break\n    if header is None:\n        return result\n    try:\n        ci = header.index(\"customer\")\n        ai = header.index(\"cents\")\n    except ValueError:\n        return result\n    pat = re.compile(r\"[+-]?[0-9]+\")\n    for row in reader:\n        if len(row) <= max(ci, ai):\n            continue\n        name = row[ci]\n        if not name.strip():\n            continue\n        s = row[ai].strip()\n        if not pat.fullmatch(s):\n            continue\n        result[name] = result.get(name, 0) + int(s)\n    return result","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.003288,"finished_at":"2026-10-04T16:58:52.224876+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"11e981b8633f2f299f5728fa32f09956cf5ab17b7d16ab4d74778b1fd8c8e829","latency_seconds":2.812376,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. 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Preserve customer spelling.","raw_response_hash":"fa1b11a66ee95ff9ecb86c827606587a107be33e92a987da5d6ce7720905be8f","repetition":3,"request_hash":"5ea1f0dc30afd91d2c5284b434b5704e43bc065e84e313037664158f1a7b7e45","request_id":"req_011CfhZ118Br3s7eMpuPLTJU","sent_at":"2026-10-04T16:58:49.412500+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-csv","trial_id":"35dc1519e11cc884b25778fe499763a707bc3c3b4ba1f6fc3614403e9de0a715","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":204,"output_tokens":288,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"import csv\nimport io\nimport re\n\ndef solve(data):\n    totals = {}\n    reader = csv.DictReader(io.StringIO(data))\n    for row in reader:\n        customer = row.get('customer')\n        cents = row.get('cents')\n        if customer is None or customer.strip() == '':\n            continue\n        if cents is None or not re.fullmatch(r'[+-]?[0-9]+', cents):\n            continue\n        totals[customer] = totals.get(customer, 0) + int(cents)\n    return totals","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.017066,"finished_at":"2026-10-04T16:42:11.470033+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":false}],"passed":false,"reason":"checked"},"input_hash":"11e981b8633f2f299f5728fa32f09956cf5ab17b7d16ab4d74778b1fd8c8e829","latency_seconds":20.550039,"model_id":"gemini-3.8-flash","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is CSV text with header customer,cents, including quoted fields. Ignore rows with a blank customer or cents that is not a signed base-10 integer. Sum by customer and return a dictionary, including zero totals. Preserve customer spelling.","raw_response_hash":"09b4b49f9ad4e5c9ca030197e9b7d6b317daea52231aa352ebb2ecd13fbea86e","repetition":1,"request_hash":"4f7893bb9a174576c03844f8f27497d9b0a27bec040af0e849df1cc4736e35f5","request_id":null,"sent_at":"2026-10-04T16:41:50.919994+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-csv","trial_id":"f40face739e5dab954ab966b82e7ee33a77a2ab5c16229fef59bf3b1ee62c77f","usage":{"candidatesTokenCount":130,"promptTokenCount":134,"promptTokensDetails":[{"modality":"TEXT","tokenCount":134}],"serviceTier":"standard","thoughtsTokenCount":4394,"totalTokenCount":4658}},{"answer":"\" - no markdown, only code.\n- \"no custom classes, private/dunder attributes, globals/nonlocals, or async.\"\n- \"Preserve customer spelling.\"\n\nLet's write down the function clearly:\n\n```python\nimport csv\nimport io\nimport re\n\ndef solve(data):\n    reader = csv.reader(io.StringIO(data))\n    try:\n        header = next(reader)\n    except StopIteration:\n        return {}\n    \n    header_map = {col.strip().lower(): i for i, col in enumerate(header)}\n    cust_idx = header_map.get('customer')\n    cents_idx = header_map.get('cents')\n    if cust_idx is None or cents_idx is None:\n        return {}\n    \n    pattern = re.compile(r'^[+-]?\\d+$')\n    totals = {}\n    \n    for row in reader:\n        if len(row) <= max(cust_idx, cents_idx):\n            continue\n        cust = row[cust_idx]\n        cents = row[cents_idx]\n        if not cust or not cust.strip():\n            continue\n        if not pattern.match(cents):\n            continue\n        totals[cust] = totals.get(cust, 0) + int(cents)\n        \n    return totals\n```\n\nEverything looks solid and directly adheres to all instructions.import csv\nimport io\nimport re\n\ndef solve","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.030802,"finished_at":"2026-10-04T16:49:32.869422+00:00","grade":{"checks":[{"case":1,"passed":false},{"case":2,"passed":false},{"case":3,"passed":false}],"passed":false,"reason":"execution_rejected"},"input_hash":"11e981b8633f2f299f5728fa32f09956cf5ab17b7d16ab4d74778b1fd8c8e829","latency_seconds":39.250278,"model_id":"gemini-3.8-flash","output_cap_reached":true,"prompt":"Implement solve(data) in Python 3.9. 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Preserve customer spelling.","raw_response_hash":"5519788804c8d62e63735b206ff220745c6732539dbd07c243069da7c8a438ba","repetition":2,"request_hash":"4f7893bb9a174576c03844f8f27497d9b0a27bec040af0e849df1cc4736e35f5","request_id":"dYPCarS-LteG-8YP4LCuKA","sent_at":"2026-10-04T16:48:53.619144+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-csv","trial_id":"bb5c629c96f271eee5aacfb5ed3c15dac516dd972c49c4e2de0e47aab9454c9d","usage":{"candidatesTokenCount":325,"promptTokenCount":134,"promptTokensDetails":[{"modality":"TEXT","tokenCount":134}],"serviceTier":"standard","thoughtsTokenCount":7862,"totalTokenCount":8321}},{"answer":"import csv\nimport io\nimport re\n\ndef solve(data):\n    if not data:\n        return {}\n    data = data.lstrip(\"\\ufeff\")\n    reader = csv.DictReader(io.StringIO(data))\n    totals = {}\n    pattern = re.compile(r\"[+-]?[0-9]+\")\n    for row in reader:\n        customer = row.get(\"customer\")\n        cents = row.get(\"cents\")\n        if customer is None or cents is None:\n            continue\n        if not customer.strip():\n            continue\n        if not pattern.fullmatch(cents):\n            continue\n        totals[customer] = totals.get(customer, 0) + int(cents)\n    return totals","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.017688,"finished_at":"2026-10-04T16:58:39.132723+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":false}],"passed":false,"reason":"checked"},"input_hash":"11e981b8633f2f299f5728fa32f09956cf5ab17b7d16ab4d74778b1fd8c8e829","latency_seconds":21.476253,"model_id":"gemini-3.8-flash","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is CSV text with header customer,cents, including quoted fields. Ignore rows with a blank customer or cents that is not a signed base-10 integer. Sum by customer and return a dictionary, including zero totals. Preserve customer spelling.","raw_response_hash":"29b27643884ff3a5f693aa9075209210e2f3782f1c01e0f82c83f2f0ff373584","repetition":3,"request_hash":"4f7893bb9a174576c03844f8f27497d9b0a27bec040af0e849df1cc4736e35f5","request_id":null,"sent_at":"2026-10-04T16:58:17.656470+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-csv","trial_id":"a91b5ea3dd69b820d1f0f46a007731a85af5e801a6961f4c656f7fb76c3f41b4","usage":{"candidatesTokenCount":164,"promptTokenCount":134,"promptTokensDetails":[{"modality":"TEXT","tokenCount":134}],"serviceTier":"standard","thoughtsTokenCount":4526,"totalTokenCount":4824}},{"answer":"def solve(data):\n    import csv\n    import io\n    import re\n\n    totals = {}\n    for row in csv.DictReader(io.StringIO(data)):\n        customer = row.get(\"customer\")\n        cents = row.get(\"cents\")\n\n        if customer is None or not customer.strip() or cents is None:\n            continue\n\n        cents = cents.strip()\n        if re.fullmatch(r\"[+-]?[0-9]+\", cents) is None:\n            continue\n\n        totals[customer] = totals.get(customer, 0) + int(cents)\n\n    return totals","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.003092,"finished_at":"2026-10-04T16:41:48.034658+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"11e981b8633f2f299f5728fa32f09956cf5ab17b7d16ab4d74778b1fd8c8e829","latency_seconds":7.38027,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is CSV text with header customer,cents, including quoted fields. Ignore rows with a blank customer or cents that is not a signed base-10 integer. Sum by customer and return a dictionary, including zero totals. 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Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is CSV text with header customer,cents, including quoted fields. Ignore rows with a blank customer or cents that is not a signed base-10 integer. Sum by customer and return a dictionary, including zero totals. 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Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a list of {id, version, value}. Keep the highest version for each id; on equal version keep the last occurrence. Return the retained objects sorted lexicographically by id. 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Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a list of {id, version, value}. Keep the highest version for each id; on equal version keep the last occurrence. Return the retained objects sorted lexicographically by id. 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Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a list of {id, version, value}. Keep the highest version for each id; on equal version keep the last occurrence. Return the retained objects sorted lexicographically by id. 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Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a list of {id, version, value}. Keep the highest version for each id; on equal version keep the last occurrence. Return the retained objects sorted lexicographically by id. 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Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a list of {id, version, value}. Keep the highest version for each id; on equal version keep the last occurrence. Return the retained objects sorted lexicographically by id. 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Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a list of {id, version, value}. Keep the highest version for each id; on equal version keep the last occurrence. Return the retained objects sorted lexicographically by id. 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Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a list of {id, version, value}. Keep the highest version for each id; on equal version keep the last occurrence. Return the retained objects sorted lexicographically by id. 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Return null for any cycle.","raw_response_hash":"d958b766bf0959c1b13916666c7a8d7744db8fe3241e257a1e50c2ab638c81f6","repetition":3,"request_hash":"fbba9a1efe979b68c79b87c6d7a20a76b3ec421f4871ba4574106cbd606c6120","request_id":null,"sent_at":"2026-10-04T17:00:11.800050+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-dependencies","trial_id":"129bb383d7f606c28e98a266a1ba44af55e652b47b71a3c29378b78da5686681","usage":{"candidatesTokenCount":238,"promptTokenCount":128,"promptTokensDetails":[{"modality":"TEXT","tokenCount":128}],"serviceTier":"standard","thoughtsTokenCount":1684,"totalTokenCount":2050}},{"answer":"def solve(data):\n    import heapq\n\n    graph = {}\n    indegree = {}\n\n    for job, prerequisites in data.items():\n        graph.setdefault(job, set())\n        indegree.setdefault(job, 0)\n\n        for prerequisite in set(prerequisites):\n            graph.setdefault(prerequisite, set()).add(job)\n            indegree.setdefault(prerequisite, 0)\n            indegree[job] += 1\n\n    ready = [name for name, count in indegree.items() if count == 0]\n    heapq.heapify(ready)\n    order = []\n\n    while ready:\n        name = heapq.heappop(ready)\n        order.append(name)\n\n        for dependent in graph[name]:\n            indegree[dependent] -= 1\n            if indegree[dependent] == 0:\n                heapq.heappush(ready, dependent)\n\n    return order if len(order) == len(indegree) else None","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.004386,"finished_at":"2026-10-04T16:43:56.890870+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"eba966ed10eb210d7939149d49800f96df0b81c38680e8d4e93ef7476e4e14ee","latency_seconds":9.496568,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a dict mapping each named job to prerequisite names. Include names referenced only as prerequisites. Return the lexicographically smallest valid topological order, choosing the smallest ready name at every step. Return null for any cycle.","raw_response_hash":"d015f103071114b10b1090aa9e0a24ef170f44dd66c33267215cf343a85141fe","repetition":1,"request_hash":"77a81714a9dc4ca5f87ee59c066db69ada4883c78afa1ecc10256272654b2ac2","request_id":"req_44d4b0230a60440bb0851f98f6783deb","sent_at":"2026-10-04T16:43:47.394302+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-dependencies","trial_id":"063988c11135831701e57ef37ad8b86323fce447dec1a49f4c8773fb09846443","usage":{"input_tokens":133,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":412,"output_tokens_details":{"reasoning_tokens":216},"total_tokens":545}},{"answer":"import heapq\n\n\ndef solve(data):\n    nodes = set(data)\n    for prerequisites in data.values():\n        nodes.update(prerequisites)\n\n    indegree = {name: 0 for name in nodes}\n    dependents = {name: [] for name in nodes}\n\n    for job, prerequisites in data.items():\n        for prerequisite in set(prerequisites):\n            dependents[prerequisite].append(job)\n            indegree[job] += 1\n\n    ready = [name for name in nodes if indegree[name] == 0]\n    heapq.heapify(ready)\n    order = []\n\n    while ready:\n        name = heapq.heappop(ready)\n        order.append(name)\n        for job in dependents[name]:\n            indegree[job] -= 1\n            if indegree[job] == 0:\n                heapq.heappush(ready, job)\n\n    return order if len(order) == len(nodes) else None","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.002276,"finished_at":"2026-10-04T16:48:29.986269+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"eba966ed10eb210d7939149d49800f96df0b81c38680e8d4e93ef7476e4e14ee","latency_seconds":4.247509,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a dict mapping each named job to prerequisite names. Include names referenced only as prerequisites. Return the lexicographically smallest valid topological order, choosing the smallest ready name at every step. Return null for any cycle.","raw_response_hash":"fe0bcb4cecd5db3f563c68e212d7696bf90e425bc6d7a08cb1fc7ac03cebe62e","repetition":2,"request_hash":"77a81714a9dc4ca5f87ee59c066db69ada4883c78afa1ecc10256272654b2ac2","request_id":"req_60c3ca1cf66348df8d7973da825ef2f8","sent_at":"2026-10-04T16:48:25.738760+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-dependencies","trial_id":"e7ccf1baafc28693da644b9581105f0eb22fd8a1ca2b12b4711b15126eca72c1","usage":{"input_tokens":133,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":201,"output_tokens_details":{"reasoning_tokens":0},"total_tokens":334}},{"answer":"def solve(data):\n    import heapq\n\n    names = set(data)\n    for prerequisites in data.values():\n        names.update(prerequisites)\n\n    dependents = {name: [] for name in names}\n    indegree = {name: 0 for name in names}\n\n    for job, prerequisites in data.items():\n        for prerequisite in set(prerequisites):\n            dependents[prerequisite].append(job)\n            indegree[job] += 1\n\n    ready = [name for name in names if indegree[name] == 0]\n    heapq.heapify(ready)\n    order = []\n\n    while ready:\n        name = heapq.heappop(ready)\n        order.append(name)\n        for job in dependents[name]:\n            indegree[job] -= 1\n            if indegree[job] == 0:\n                heapq.heappush(ready, job)\n\n    return order if len(order) == len(names) else None","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.003286,"finished_at":"2026-10-04T17:00:27.389737+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"eba966ed10eb210d7939149d49800f96df0b81c38680e8d4e93ef7476e4e14ee","latency_seconds":6.91948,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a dict mapping each named job to prerequisite names. Include names referenced only as prerequisites. Return the lexicographically smallest valid topological order, choosing the smallest ready name at every step. Return null for any cycle.","raw_response_hash":"ec2cc3fc9c2ad0d1a2a9cc779bddf10fbd84691d4c5eefa61c6de57ece651900","repetition":3,"request_hash":"77a81714a9dc4ca5f87ee59c066db69ada4883c78afa1ecc10256272654b2ac2","request_id":"req_f66292c4499c44dc8e7da8643c504c2e","sent_at":"2026-10-04T17:00:20.470257+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-dependencies","trial_id":"7f84a2596e81fb042eea22306bfcec13a5c21bfacebf4acaf8c56080e1e1dad9","usage":{"input_tokens":133,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":302,"output_tokens_details":{"reasoning_tokens":98},"total_tokens":435}},{"answer":"def solve(data):\n    lots = data.get(\"lots\", []) or []\n    sold = data.get(\"sold\", 0) or 0\n    to_sell = sold\n    remaining_units = 0\n    remaining_value = 0\n    for lot in lots:\n        q = lot[0]\n        p = lot[1]\n        take = q if q < to_sell else to_sell\n        to_sell -= take\n        left = q - take\n        remaining_units += left\n        remaining_value += left * p\n    return {\n        \"remaining_units\": remaining_units,\n        \"remaining_value_cents\": remaining_value,\n        \"unfilled_units\": to_sell,\n    }","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.002498,"finished_at":"2026-10-04T16:45:23.156717+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"3412624061af6ffef7ce82740b6ca4bd6e5f357f6e0927d8f03013fb436d02ee","latency_seconds":2.136341,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {lots:[[quantity,unit_cents],...], sold:nonnegative integer}. Sell in lot order. Return {remaining_units,remaining_value_cents,unfilled_units}. All quantities/prices nonnegative; unfilled is sales beyond available inventory.","raw_response_hash":"efcb8b2797f572372602aa2f07bf9baa594d6cde25c427bee504b1c4c6238f0b","repetition":1,"request_hash":"d331770699b9ec2f4bd1eb07058b0974bb7e8bd56d70a38c5ec4ee525fe85ae3","request_id":"req_011CfhXyQXBxqWMfnFfM37r1","sent_at":"2026-10-04T16:45:21.020376+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-fifo","trial_id":"6cbaa95c1ceeb8e7f8d48f9c55c4de19f07637da066d62a781a45e8ecfe68e2d","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":219,"output_tokens":206,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"def solve(data):\n    lots = data.get(\"lots\", []) or []\n    sold = data.get(\"sold\", 0) or 0\n    to_sell = sold\n    remaining_units = 0\n    remaining_value = 0\n    for lot in lots:\n        q = lot[0]\n        p = lot[1]\n        take = q if q < to_sell else to_sell\n        to_sell -= take\n        left = q - take\n        remaining_units += left\n        remaining_value += left * p\n    return {\n        \"remaining_units\": remaining_units,\n        \"remaining_value_cents\": remaining_value,\n        \"unfilled_units\": to_sell,\n    }","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.002498,"finished_at":"2026-10-04T16:55:36.646568+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"3412624061af6ffef7ce82740b6ca4bd6e5f357f6e0927d8f03013fb436d02ee","latency_seconds":2.17419,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {lots:[[quantity,unit_cents],...], sold:nonnegative integer}. Sell in lot order. Return {remaining_units,remaining_value_cents,unfilled_units}. All quantities/prices nonnegative; unfilled is sales beyond available inventory.","raw_response_hash":"e006f5cc04831f21c2d2a4a374181906a19da4715f95c633bc64f573a5f75df0","repetition":2,"request_hash":"d331770699b9ec2f4bd1eb07058b0974bb7e8bd56d70a38c5ec4ee525fe85ae3","request_id":"req_011CfhYkd5kMfboxYQRvQhut","sent_at":"2026-10-04T16:55:34.472378+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-fifo","trial_id":"4f4c93d48efe1f0173d1c8ae6b262ec23364ef4cfa8a19e5999715c7d102ba36","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":219,"output_tokens":206,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"def solve(data):\n    lots = data.get(\"lots\", []) or []\n    sold = data.get(\"sold\", 0) or 0\n    to_sell = sold\n    remaining_units = 0\n    remaining_value = 0\n    for lot in lots:\n        q = lot[0]\n        p = lot[1]\n        take = q if q < to_sell else to_sell\n        to_sell -= take\n        left = q - take\n        remaining_units += left\n        remaining_value += left * p\n    return {\n        \"remaining_units\": remaining_units,\n        \"remaining_value_cents\": remaining_value,\n        \"unfilled_units\": to_sell,\n    }","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.002498,"finished_at":"2026-10-04T16:56:43.255333+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"3412624061af6ffef7ce82740b6ca4bd6e5f357f6e0927d8f03013fb436d02ee","latency_seconds":2.154002,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {lots:[[quantity,unit_cents],...], sold:nonnegative integer}. Sell in lot order. Return {remaining_units,remaining_value_cents,unfilled_units}. All quantities/prices nonnegative; unfilled is sales beyond available inventory.","raw_response_hash":"35ece2cd4c3d54b28f5d6d4234427e5d67c441b9ae0135da50a4949a8b17b3d6","repetition":3,"request_hash":"d331770699b9ec2f4bd1eb07058b0974bb7e8bd56d70a38c5ec4ee525fe85ae3","request_id":"req_011CfhYqXvpMVZ3zMejpYKgJ","sent_at":"2026-10-04T16:56:41.101331+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-fifo","trial_id":"e5937450e591a015c5bab60ebad86e86761a03c31b60d72f651df53c485a678e","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":219,"output_tokens":206,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"def solve(data):\n    lots = data.get('lots', [])\n    to_sell = data.get('sold', 0)\n    rem_units = 0\n    rem_value = 0\n    for q, p in lots:\n        if to_sell >= q:\n            to_sell -= q\n        elif to_sell > 0:\n            left_q = q - to_sell\n            to_sell = 0\n            rem_units += left_q\n            rem_value += left_q * p\n        else:\n            rem_units += q\n            rem_value += q * p\n    return {\n        'remaining_units': rem_units,\n        'remaining_value_cents': rem_value,\n        'unfilled_units': to_sell\n    }","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.004009,"finished_at":"2026-10-04T16:45:26.820080+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"3412624061af6ffef7ce82740b6ca4bd6e5f357f6e0927d8f03013fb436d02ee","latency_seconds":3.598271,"model_id":"gemini-3.8-flash","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {lots:[[quantity,unit_cents],...], sold:nonnegative integer}. Sell in lot order. Return {remaining_units,remaining_value_cents,unfilled_units}. All quantities/prices nonnegative; unfilled is sales beyond available inventory.","raw_response_hash":"f82392962723b28fcf685f90e3a9a94b92a312e530597b0292c8f7e57ca0e186","repetition":1,"request_hash":"3bdfe2d22d0a0dd0c86341564db9024d5f3ca666e5ad4f385309593b044e0ec7","request_id":null,"sent_at":"2026-10-04T16:45:23.221809+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-fifo","trial_id":"db7d7f17a9c5e021e1e1bb94a55273fb8ed596c501cc3385ce4134b3e5e6145a","usage":{"candidatesTokenCount":183,"promptTokenCount":140,"promptTokensDetails":[{"modality":"TEXT","tokenCount":140}],"serviceTier":"standard","thoughtsTokenCount":858,"totalTokenCount":1181}},{"answer":"def solve(data):\n    lots = data.get('lots', [])\n    sold = data.get('sold', 0)\n    \n    to_sell = sold\n    remaining_units = 0\n    remaining_value_cents = 0\n    \n    for qty, unit_cents in lots:\n        if to_sell > 0:\n            take = min(qty, to_sell)\n            qty_left = qty - take\n            to_sell -= take\n        else:\n            qty_left = qty\n        remaining_units += qty_left\n        remaining_value_cents += qty_left * unit_cents\n        \n    return {\n        'remaining_units': remaining_units,\n        'remaining_value_cents': remaining_value_cents,\n        'unfilled_units': to_sell\n    }","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.004422,"finished_at":"2026-10-04T16:55:41.546378+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"3412624061af6ffef7ce82740b6ca4bd6e5f357f6e0927d8f03013fb436d02ee","latency_seconds":4.847272,"model_id":"gemini-3.8-flash","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {lots:[[quantity,unit_cents],...], sold:nonnegative integer}. Sell in lot order. Return {remaining_units,remaining_value_cents,unfilled_units}. All quantities/prices nonnegative; unfilled is sales beyond available inventory.","raw_response_hash":"c472def785e4e9b89ad43f5ab3da3261df65b187980ec3a26b9b1f267d99e5ad","repetition":2,"request_hash":"3bdfe2d22d0a0dd0c86341564db9024d5f3ca666e5ad4f385309593b044e0ec7","request_id":null,"sent_at":"2026-10-04T16:55:36.699106+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-fifo","trial_id":"4d278cc8b3a19f96355e6fbc68fbda0079b5d0e8e0b8887573cd2721246d3805","usage":{"candidatesTokenCount":188,"promptTokenCount":140,"promptTokensDetails":[{"modality":"TEXT","tokenCount":140}],"serviceTier":"standard","thoughtsTokenCount":963,"totalTokenCount":1291}},{"answer":"def solve(data):\n    lots = data.get(\"lots\", [])\n    remaining_to_sell = data.get(\"sold\", 0)\n    \n    remaining_units = 0\n    remaining_value_cents = 0\n    \n    for lot in lots:\n        qty, price = lot[0], lot[1]\n        if remaining_to_sell > 0:\n            take = min(qty, remaining_to_sell)\n            qty -= take\n            remaining_to_sell -= take\n        remaining_units += qty\n        remaining_value_cents += qty * price\n        \n    return {\n        \"remaining_units\": remaining_units,\n        \"remaining_value_cents\": remaining_value_cents,\n        \"unfilled_units\": remaining_to_sell\n    }","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.003863,"finished_at":"2026-10-04T16:56:37.896686+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"3412624061af6ffef7ce82740b6ca4bd6e5f357f6e0927d8f03013fb436d02ee","latency_seconds":4.579124,"model_id":"gemini-3.8-flash","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {lots:[[quantity,unit_cents],...], sold:nonnegative integer}. Sell in lot order. Return {remaining_units,remaining_value_cents,unfilled_units}. All quantities/prices nonnegative; unfilled is sales beyond available inventory.","raw_response_hash":"84a076c619b4c33821a216aa082cd485d07dc02220f87a6be30d2223d67dc47c","repetition":3,"request_hash":"3bdfe2d22d0a0dd0c86341564db9024d5f3ca666e5ad4f385309593b044e0ec7","request_id":null,"sent_at":"2026-10-04T16:56:33.317562+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-fifo","trial_id":"62a4db3a52e1d03bfd1873c2a43ec0e6fd3d07cbabb9865d3fd85dde05adceca","usage":{"candidatesTokenCount":182,"promptTokenCount":140,"promptTokensDetails":[{"modality":"TEXT","tokenCount":140}],"serviceTier":"standard","thoughtsTokenCount":820,"totalTokenCount":1142}},{"answer":"def solve(data):\n    to_sell = data[\"sold\"]\n    remaining_units = 0\n    remaining_value_cents = 0\n\n    for quantity, unit_cents in data[\"lots\"]:\n        sold_from_lot = min(quantity, to_sell)\n        to_sell -= sold_from_lot\n        remaining = quantity - sold_from_lot\n        remaining_units += remaining\n        remaining_value_cents += remaining * unit_cents\n\n    return {\n        \"remaining_units\": remaining_units,\n        \"remaining_value_cents\": remaining_value_cents,\n        \"unfilled_units\": to_sell,\n    }","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.001552,"finished_at":"2026-10-04T16:45:20.962872+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"3412624061af6ffef7ce82740b6ca4bd6e5f357f6e0927d8f03013fb436d02ee","latency_seconds":3.478877,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {lots:[[quantity,unit_cents],...], sold:nonnegative integer}. Sell in lot order. Return {remaining_units,remaining_value_cents,unfilled_units}. All quantities/prices nonnegative; unfilled is sales beyond available inventory.","raw_response_hash":"b1ad555dd07f277c09d65ea15e0705a9ff34a262fd1f870708ce054014b7c928","repetition":1,"request_hash":"694d7f7c9fc897d6840b4d95ac6f5aa2abb4a859968ed6e791ad6b0b8b9f0f7a","request_id":"req_6c05b94a88d04c198a380b167f788d8b","sent_at":"2026-10-04T16:45:17.483995+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-fifo","trial_id":"096cfd75d84cbbcdef7eae181c3a85e5b6e7990e8d7ec2c843ad8c38c69fda80","usage":{"input_tokens":141,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":127,"output_tokens_details":{"reasoning_tokens":0},"total_tokens":268}},{"answer":"def solve(data):\n    unfilled = data[\"sold\"]\n    remaining_units = 0\n    remaining_value_cents = 0\n\n    for quantity, unit_cents in data[\"lots\"]:\n        sold_from_lot = min(quantity, unfilled)\n        unfilled -= sold_from_lot\n        remaining = quantity - sold_from_lot\n        remaining_units += remaining\n        remaining_value_cents += remaining * unit_cents\n\n    return {\n        \"remaining_units\": remaining_units,\n        \"remaining_value_cents\": remaining_value_cents,\n        \"unfilled_units\": unfilled,\n    }","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.001552,"finished_at":"2026-10-04T16:55:44.723219+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"3412624061af6ffef7ce82740b6ca4bd6e5f357f6e0927d8f03013fb436d02ee","latency_seconds":3.102525,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {lots:[[quantity,unit_cents],...], sold:nonnegative integer}. Sell in lot order. Return {remaining_units,remaining_value_cents,unfilled_units}. All quantities/prices nonnegative; unfilled is sales beyond available inventory.","raw_response_hash":"dbedf4c8d19bb0d7450963b25313684c022cd82f033c8192ff9e664ff951952c","repetition":2,"request_hash":"694d7f7c9fc897d6840b4d95ac6f5aa2abb4a859968ed6e791ad6b0b8b9f0f7a","request_id":"req_b1ee0c151ec64588a60f8b70bc58ea0a","sent_at":"2026-10-04T16:55:41.620694+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-fifo","trial_id":"2b70d011b10aa90cde703b0bf851c29848364dc08fae79d46d0414ad479918f4","usage":{"input_tokens":141,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":127,"output_tokens_details":{"reasoning_tokens":0},"total_tokens":268}},{"answer":"def solve(data):\n    unsold_demand = data[\"sold\"]\n    remaining_units = 0\n    remaining_value_cents = 0\n\n    for quantity, unit_cents in data[\"lots\"]:\n        sold_from_lot = min(quantity, unsold_demand)\n        unsold_demand -= sold_from_lot\n        remaining = quantity - sold_from_lot\n        remaining_units += remaining\n        remaining_value_cents += remaining * unit_cents\n\n    return {\n        \"remaining_units\": remaining_units,\n        \"remaining_value_cents\": remaining_value_cents,\n        \"unfilled_units\": unsold_demand,\n    }","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.001632,"finished_at":"2026-10-04T16:56:41.043158+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"3412624061af6ffef7ce82740b6ca4bd6e5f357f6e0927d8f03013fb436d02ee","latency_seconds":3.088231,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {lots:[[quantity,unit_cents],...], sold:nonnegative integer}. Sell in lot order. Return {remaining_units,remaining_value_cents,unfilled_units}. All quantities/prices nonnegative; unfilled is sales beyond available inventory.","raw_response_hash":"5e982caf41f0faa83e14db72d645d455ccea5fc3504ed2c2d5a174f269996b63","repetition":3,"request_hash":"694d7f7c9fc897d6840b4d95ac6f5aa2abb4a859968ed6e791ad6b0b8b9f0f7a","request_id":"req_42fa6774d99c42c2a9352febba3730ca","sent_at":"2026-10-04T16:56:37.954927+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-fifo","trial_id":"b0a83e6113a59817f325d72fa31c147f16db6baf945bc5b7b67e9f24397a2d44","usage":{"input_tokens":141,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":135,"output_tokens_details":{"reasoning_tokens":0},"total_tokens":276}},{"answer":"def solve(data):\n    # Keep only non-empty intervals, as fresh (start, end) pairs, so the input is never modified.\n    items = []\n    for iv in data:\n        s, e = iv[0], iv[1]\n        if s < e:\n            items.append((s, e))\n    items.sort()\n    result = []\n    for s, e in items:\n        # Merge when the new interval overlaps or touches the previous one.\n        if result and s <= result[-1][1]:\n            if e > result[-1][1]:\n                result[-1][1] = e\n        else:\n            result.append([s, e])\n    return result","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.002296,"finished_at":"2026-10-04T16:44:56.141134+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"6f8debc65e08962df3242c7c8e1e5e9b51e8e7ced2c26515d7afcaee74ab21a4","latency_seconds":2.212327,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a list of [start,end] integer half-open intervals, with start<=end. Drop empty intervals; merge overlapping or touching intervals. Return sorted merged intervals without mutating input.","raw_response_hash":"eda9bc46aac0a5d3b279afe6c93a6f8926cc4bf6a09c9edd13073993659a0550","repetition":1,"request_hash":"c83b39ab86ce28f8abf14b77104bc0d3bf4231998924d8309df9c91ce541c5df","request_id":"req_011CfhXwQcM1bUNzzsBVhFbb","sent_at":"2026-10-04T16:44:53.928807+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-intervals","trial_id":"83b6e11965bbf78003cf2fcfd8636f8cd9bff566d65180f6e58395d2c9add15b","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":188,"output_tokens":192,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"def solve(data):\n    intervals = []\n    for item in data:\n        s, e = item[0], item[1]\n        if s < e:\n            intervals.append((s, e))\n    intervals.sort()\n    result = []\n    for s, e in intervals:\n        if result and s <= result[-1][1]:\n            if e > result[-1][1]:\n                result[-1][1] = e\n        else:\n            result.append([s, e])\n    return result","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.001756,"finished_at":"2026-10-04T16:48:31.901658+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"6f8debc65e08962df3242c7c8e1e5e9b51e8e7ced2c26515d7afcaee74ab21a4","latency_seconds":1.860347,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a list of [start,end] integer half-open intervals, with start<=end. Drop empty intervals; merge overlapping or touching intervals. Return sorted merged intervals without mutating input.","raw_response_hash":"2ec167120eb8d956f8aabf79863a93802a47f38e835a1bb37f0cbfb1899b0e54","repetition":2,"request_hash":"c83b39ab86ce28f8abf14b77104bc0d3bf4231998924d8309df9c91ce541c5df","request_id":"req_011CfhYDLXEKwUpR5SinJh6D","sent_at":"2026-10-04T16:48:30.041311+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-intervals","trial_id":"da8454fda4da62c7af44df7ec02822e91595af31f432aa72041bd75a3accdf03","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":188,"output_tokens":138,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"def solve(data):\n    intervals = []\n    for item in data:\n        s, e = item[0], item[1]\n        if s < e:\n            intervals.append((s, e))\n    intervals.sort()\n    result = []\n    for s, e in intervals:\n        if result and s <= result[-1][1]:\n            if e > result[-1][1]:\n                result[-1][1] = e\n        else:\n            result.append([s, e])\n    return result","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.001756,"finished_at":"2026-10-04T16:56:53.405656+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"6f8debc65e08962df3242c7c8e1e5e9b51e8e7ced2c26515d7afcaee74ab21a4","latency_seconds":1.759265,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. 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Return sorted merged intervals without mutating input.","raw_response_hash":"81105048b571652668e8e7620c99c7f34e4b48314b1e02e80fec168097eab0ab","repetition":3,"request_hash":"c83b39ab86ce28f8abf14b77104bc0d3bf4231998924d8309df9c91ce541c5df","request_id":"req_011CfhYrK8fcdD19TppVU8wY","sent_at":"2026-10-04T16:56:51.646391+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-intervals","trial_id":"b52df41b0c238917a6fd441a34a1cc33923212b7f842731f23f3f71a04b1242b","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":188,"output_tokens":138,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"def solve(data):\n    valid_intervals = [list(interval) for interval in data if interval[0] < interval[1]]\n    if not valid_intervals:\n        return []\n    valid_intervals.sort(key=lambda x: (x[0], x[1]))\n    merged = [valid_intervals[0]]\n    for current in valid_intervals[1:]:\n        last = merged[-1]\n        if current[0] <= last[1]:\n            if current[1] > last[1]:\n                last[1] = current[1]\n        else:\n            merged.append(current)\n    return merged","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.003581,"finished_at":"2026-10-04T16:45:00.571014+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"6f8debc65e08962df3242c7c8e1e5e9b51e8e7ced2c26515d7afcaee74ab21a4","latency_seconds":4.370509,"model_id":"gemini-3.8-flash","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a list of [start,end] integer half-open intervals, with start<=end. Drop empty intervals; merge overlapping or touching intervals. Return sorted merged intervals without mutating input.","raw_response_hash":"97b30f32800d14dbe31546c2db13cd783cb11ee2b270e1e64b01b8f298fb0e81","repetition":1,"request_hash":"ed058b0df25f2f9658365a64fe7ba675b403ed50f72059f321fc43d52dfe34f0","request_id":null,"sent_at":"2026-10-04T16:44:56.200505+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-intervals","trial_id":"38298d954ae80a554370bf2c273a3412edb118201ade78da98fb2ba69cf9fe20","usage":{"candidatesTokenCount":145,"promptTokenCount":124,"promptTokensDetails":[{"modality":"TEXT","tokenCount":124}],"serviceTier":"standard","thoughtsTokenCount":785,"totalTokenCount":1054}},{"answer":"def solve(data):\n    valid = [[iv[0], iv[1]] for iv in data if iv[0] < iv[1]]\n    if not valid:\n        return []\n    valid.sort(key=lambda x: (x[0], x[1]))\n    merged = [valid[0]]\n    for iv in valid[1:]:\n        if iv[0] <= merged[-1][1]:\n            if iv[1] > merged[-1][1]:\n                merged[-1][1] = iv[1]\n        else:\n            merged.append(iv)\n    return merged","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.004702,"finished_at":"2026-10-04T16:48:36.907992+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"6f8debc65e08962df3242c7c8e1e5e9b51e8e7ced2c26515d7afcaee74ab21a4","latency_seconds":4.958646,"model_id":"gemini-3.8-flash","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a list of [start,end] integer half-open intervals, with start<=end. Drop empty intervals; merge overlapping or touching intervals. 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Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a list of [start,end] integer half-open intervals, with start<=end. Drop empty intervals; merge overlapping or touching intervals. 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Here is the corrected final code:\n\nimport math\n\ndef solve(data):\n    values = data.get(\"values\") if isinstance(data, dict) else None\n    if not values:\n        return None\n    p = data.get(\"p\", 0)\n    s = sorted(values)\n    n = len(s)\n    rank = math.ceil(p / 100 * n)\n    if rank < 1:\n        rank = 1\n    if rank > n:\n        rank = n\n    return s[rank - 1]","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.003286,"finished_at":"2026-10-04T16:40:45.398731+00:00","grade":{"checks":[{"case":1,"passed":false},{"case":2,"passed":false},{"case":3,"passed":false},{"case":4,"passed":false},{"case":5,"passed":false}],"passed":false,"reason":"execution_rejected"},"input_hash":"b0fa322be8af0ed75547851efc2ef7c547dcea20a804aae5e19b14bdf3547fe8","latency_seconds":2.761334,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. 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Here is the complete code:\n\nimport math\n\ndef solve(data):\n    values = data.get(\"values\") if isinstance(data, dict) else None\n    if not values:\n        return None\n    p = data.get(\"p\", 0)\n    s = sorted(values)\n    n = len(s)\n    rank = math.ceil(p / 100 * n)\n    if rank < 1:\n        rank = 1\n    if rank > n:\n        rank = n\n    return s[rank - 1]","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.003286,"finished_at":"2026-10-04T16:47:42.498463+00:00","grade":{"checks":[{"case":1,"passed":false},{"case":2,"passed":false},{"case":3,"passed":false},{"case":4,"passed":false},{"case":5,"passed":false}],"passed":false,"reason":"execution_rejected"},"input_hash":"b0fa322be8af0ed75547851efc2ef7c547dcea20a804aae5e19b14bdf3547fe8","latency_seconds":2.567757,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. 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Return a list of booleans.","raw_response_hash":"5150471b84f74636f3d61571e1ff392afdbd83a4c15f4b5ce778f985231e8469","repetition":2,"request_hash":"c7e3d659aab4abde4c9efed1987824e7d37d1c7a927e0df85fa2ee8833c88ffd","request_id":null,"sent_at":"2026-10-04T16:46:53.571110+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-rate","trial_id":"93e70bdb9e2cea24be9293a35c52722eb5cda9e609f1fe6b00733e35d2af6127","usage":{"candidatesTokenCount":149,"promptTokenCount":154,"promptTokensDetails":[{"modality":"TEXT","tokenCount":154}],"serviceTier":"standard","thoughtsTokenCount":1253,"totalTokenCount":1556}},{"answer":"import collections\n\n\ndef solve(data):\n    limit = data[\"limit\"]\n    window = data[\"window\"]\n    events = data[\"events\"]\n\n    user_history = collections.defaultdict(collections.deque)\n    result = []\n\n    for event in events:\n        user = event[\"user\"]\n        t = event[\"time\"]\n        dq = user_history[user]\n        cutoff = t - window\n\n        while dq and dq[0] <= cutoff:\n            dq.popleft()\n\n        if len(dq) < limit:\n            dq.append(t)\n            result.append(True)\n        else:\n            result.append(False)\n\n    return result","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.005392,"finished_at":"2026-10-04T16:59:48.787886+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"00c2c19a35af1159a369be889e6603483e4eecc1d3bbc1aa3f6a04417e4691c3","latency_seconds":5.729501,"model_id":"gemini-3.8-flash","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {limit:positive integer,window:positive integer,events:[{user,time},...]}. Times are nondecreasing integer seconds. Process in input order. Accept only when fewer than limit previously accepted events for that user have times strictly greater than time-window. Rejections do not consume allowance. Return a list of booleans.","raw_response_hash":"3317a29c994a24a40227dec7a26d607648559949585d2887e910b06a27a4b378","repetition":3,"request_hash":"c7e3d659aab4abde4c9efed1987824e7d37d1c7a927e0df85fa2ee8833c88ffd","request_id":null,"sent_at":"2026-10-04T16:59:43.058385+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-rate","trial_id":"19e1acf353abe2881eac6108edf475acee1286164589e831942e660b4df2a1e3","usage":{"candidatesTokenCount":153,"promptTokenCount":154,"promptTokensDetails":[{"modality":"TEXT","tokenCount":154}],"serviceTier":"standard","thoughtsTokenCount":1254,"totalTokenCount":1561}},{"answer":"def solve(data):\n    from collections import deque\n\n    limit = data[\"limit\"]\n    window = data[\"window\"]\n    accepted = {}\n    result = []\n\n    for event in data[\"events\"]:\n        user = event[\"user\"]\n        time = event[\"time\"]\n        cutoff = time - window\n\n        if user not in accepted:\n            accepted[user] = deque()\n        times = accepted[user]\n\n        while times and times[0] <= cutoff:\n            times.popleft()\n\n        allowed = len(times) < limit\n        result.append(allowed)\n        if allowed:\n            times.append(time)\n\n    return result","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.001594,"finished_at":"2026-10-04T16:42:29.026462+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"00c2c19a35af1159a369be889e6603483e4eecc1d3bbc1aa3f6a04417e4691c3","latency_seconds":3.027498,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {limit:positive integer,window:positive integer,events:[{user,time},...]}. Times are nondecreasing integer seconds. Process in input order. Accept only when fewer than limit previously accepted events for that user have times strictly greater than time-window. Rejections do not consume allowance. Return a list of booleans.","raw_response_hash":"c50105ee1ee6b12f3a9ce460d9ea5c5a55059e63dda298e7344ffde063282a08","repetition":1,"request_hash":"9bc643af0b04f4669ef6fc7f3333f2df638d0d34b42c4141b30f68003e963dad","request_id":"req_fc8a846b0756468bba40626205233ebd","sent_at":"2026-10-04T16:42:25.998964+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-rate","trial_id":"c9d3c374f7272f9b1685d09cd70fa3a8c9dc5439d25c48985fb04892ec884268","usage":{"input_tokens":157,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":128,"output_tokens_details":{"reasoning_tokens":0},"total_tokens":285}},{"answer":"from collections import deque\n\ndef solve(data):\n    limit = data[\"limit\"]\n    window = data[\"window\"]\n    histories = {}\n    results = []\n\n    for event in data[\"events\"]:\n        user = event[\"user\"]\n        time = event[\"time\"]\n        history = histories.setdefault(user, deque())\n        cutoff = time - window\n\n        while history and history[0] <= cutoff:\n            history.popleft()\n\n        accepted = len(history) < limit\n        results.append(accepted)\n        if accepted:\n            history.append(time)\n\n    return results","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.001474,"finished_at":"2026-10-04T16:47:02.473474+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"00c2c19a35af1159a369be889e6603483e4eecc1d3bbc1aa3f6a04417e4691c3","latency_seconds":2.950725,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {limit:positive integer,window:positive integer,events:[{user,time},...]}. Times are nondecreasing integer seconds. Process in input order. Accept only when fewer than limit previously accepted events for that user have times strictly greater than time-window. Rejections do not consume allowance. Return a list of booleans.","raw_response_hash":"997ee89036b09d07c424ffd61d5028a35535f7853c2cbf45eb093988f70cfd04","repetition":2,"request_hash":"9bc643af0b04f4669ef6fc7f3333f2df638d0d34b42c4141b30f68003e963dad","request_id":"req_6d4e865493f84f809957015e7eaa9ad0","sent_at":"2026-10-04T16:46:59.522749+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-rate","trial_id":"2e18cbdf5b7a0ef3630182a6e02ef21895c37f81958da3b4c9f20e211a1e64dc","usage":{"input_tokens":157,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":116,"output_tokens_details":{"reasoning_tokens":0},"total_tokens":273}},{"answer":"from collections import deque\n\ndef solve(data):\n    limit = data[\"limit\"]\n    window = data[\"window\"]\n    accepted_by_user = {}\n    result = []\n\n    for event in data[\"events\"]:\n        user = event[\"user\"]\n        time = event[\"time\"]\n        queue = accepted_by_user.setdefault(user, deque())\n        cutoff = time - window\n\n        while queue and queue[0] <= cutoff:\n            queue.popleft()\n\n        accepted = len(queue) < limit\n        result.append(accepted)\n        if accepted:\n            queue.append(time)\n\n    return result","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.001514,"finished_at":"2026-10-04T16:59:52.780383+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"00c2c19a35af1159a369be889e6603483e4eecc1d3bbc1aa3f6a04417e4691c3","latency_seconds":3.935822,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {limit:positive integer,window:positive integer,events:[{user,time},...]}. Times are nondecreasing integer seconds. Process in input order. Accept only when fewer than limit previously accepted events for that user have times strictly greater than time-window. Rejections do not consume allowance. Return a list of booleans.","raw_response_hash":"b1165d91e4fbb9bc8e58b2db79998eddf5b9f8169d0da8122d9da03832c4d1d6","repetition":3,"request_hash":"9bc643af0b04f4669ef6fc7f3333f2df638d0d34b42c4141b30f68003e963dad","request_id":"req_2b508b7f145b4c04a344f1a96d153ee5","sent_at":"2026-10-04T16:59:48.844561+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-rate","trial_id":"003bb7f598674a6f2031eb21ef1e23475acd2d91e1593156733a48016b0942df","usage":{"input_tokens":157,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":120,"output_tokens_details":{"reasoning_tokens":0},"total_tokens":277}},{"answer":"def solve(data):\n    if isinstance(data, str):\n        import json\n        data = json.loads(data)\n    totals = {}\n    for item in (data.get(\"invoices\") or []):\n        k = str(item[\"id\"])\n        totals[k] = totals.get(k, 0) + int(item[\"cents\"])\n    for item in (data.get(\"payments\") or []):\n        k = str(item[\"id\"])\n        totals[k] = totals.get(k, 0) - int(item[\"cents\"])\n    return {k: totals[k] for k in sorted(totals) if totals[k] != 0}","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.002238,"finished_at":"2026-10-04T16:43:43.410936+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"2c53b6dd2ebfb8e467e4e68491c45ba8012d15bdfcd3c22556c987d374620af5","latency_seconds":2.130789,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input has invoices and payments lists of {id, cents}. Sum amounts by id; payments subtract from invoices. Return a dict of nonzero outstanding amounts sorted by id, including negative overpayments. IDs are strings. Empty lists are valid.","raw_response_hash":"0123937e41784fa1a7edf9fb7f467795d25ca67d66ad0c1a697560c3f020802d","repetition":1,"request_hash":"0894e565f0743af4ce13b026c87d23ff5b9541265714fe81668616419d7bd0a2","request_id":"req_011CfhXr3xUnByL6FYWd6wYq","sent_at":"2026-10-04T16:43:41.280147+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-reconcile","trial_id":"9694da83aa1db987eb86e47d3a27779751ffa0eb17115c2f6d880933e1e7d9b4","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":209,"output_tokens":182,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"def solve(data):\n    if isinstance(data, str):\n        import json\n        data = json.loads(data)\n    totals = {}\n    for item in (data.get(\"invoices\") or []):\n        k = str(item[\"id\"])\n        totals[k] = totals.get(k, 0) + int(item[\"cents\"])\n    for item in (data.get(\"payments\") or []):\n        k = str(item[\"id\"])\n        totals[k] = totals.get(k, 0) - int(item[\"cents\"])\n    return {k: totals[k] for k in sorted(totals) if totals[k] != 0}","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.002238,"finished_at":"2026-10-04T16:48:41.896001+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"2c53b6dd2ebfb8e467e4e68491c45ba8012d15bdfcd3c22556c987d374620af5","latency_seconds":1.956062,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input has invoices and payments lists of {id, cents}. Sum amounts by id; payments subtract from invoices. Return a dict of nonzero outstanding amounts sorted by id, including negative overpayments. IDs are strings. Empty lists are valid.","raw_response_hash":"e871a73640a8494693d289bfb0ae767b327cb249f5e5344375a28643acd3ded1","repetition":2,"request_hash":"0894e565f0743af4ce13b026c87d23ff5b9541265714fe81668616419d7bd0a2","request_id":"req_011CfhYE4nCigQVpj4NYuo38","sent_at":"2026-10-04T16:48:39.939939+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-reconcile","trial_id":"1e40f912a2a0e5ada8e12f367f3a7777ebfb04fada9e2c85c42ac6eb866a4f3e","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":209,"output_tokens":182,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"def solve(data):\n    if isinstance(data, str):\n        import json\n        data = json.loads(data)\n    totals = {}\n    for item in data.get(\"invoices\", []) or []:\n        k = str(item[\"id\"])\n        totals[k] = totals.get(k, 0) + int(item[\"cents\"])\n    for item in data.get(\"payments\", []) or []:\n        k = str(item[\"id\"])\n        totals[k] = totals.get(k, 0) - int(item[\"cents\"])\n    return {k: totals[k] for k in sorted(totals) if totals[k] != 0}","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.002258,"finished_at":"2026-10-04T16:58:17.593391+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"2c53b6dd2ebfb8e467e4e68491c45ba8012d15bdfcd3c22556c987d374620af5","latency_seconds":2.155048,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input has invoices and payments lists of {id, cents}. Sum amounts by id; payments subtract from invoices. Return a dict of nonzero outstanding amounts sorted by id, including negative overpayments. IDs are strings. Empty lists are valid.","raw_response_hash":"a41921995cf9cbdc72bb2536f70cba9c17ddd25e1c92268ee6b004b032fcb4d4","repetition":3,"request_hash":"0894e565f0743af4ce13b026c87d23ff5b9541265714fe81668616419d7bd0a2","request_id":"req_011CfhYxVJGDPQRRHgUswpYs","sent_at":"2026-10-04T16:58:15.438343+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-reconcile","trial_id":"366282713801d1f49aca61f53d503673b87e5fef040bd70c57c6dd615b3a7f42","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":209,"output_tokens":184,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"def solve(data):\n    balances = {}\n    for inv in data.get(\"invoices\", []):\n        balances[inv[\"id\"]] = balances.get(inv[\"id\"], 0) + inv[\"cents\"]\n    for pmt in data.get(\"payments\", []):\n        balances[pmt[\"id\"]] = balances.get(pmt[\"id\"], 0) - pmt[\"cents\"]\n    return {k: balances[k] for k in sorted(balances) if balances[k] != 0}","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.003444,"finished_at":"2026-10-04T16:43:47.330002+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"2c53b6dd2ebfb8e467e4e68491c45ba8012d15bdfcd3c22556c987d374620af5","latency_seconds":3.852831,"model_id":"gemini-3.8-flash","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input has invoices and payments lists of {id, cents}. Sum amounts by id; payments subtract from invoices. Return a dict of nonzero outstanding amounts sorted by id, including negative overpayments. IDs are strings. Empty lists are valid.","raw_response_hash":"5d5ed27782e4c3b5b1dd9e2e74d8f8cf1eaf90b50630ccc50785bfb26fd7a299","repetition":1,"request_hash":"5d992ee23ab27b7a29b5f4b10f898cd3e4a5516878020d9e5241b96f4ebf4f20","request_id":null,"sent_at":"2026-10-04T16:43:43.477171+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-reconcile","trial_id":"61450bb5951d2deb2e5a2e0eb55fde85f3df4c2d2d06d7a759c8b6f275734e14","usage":{"candidatesTokenCount":117,"promptTokenCount":132,"promptTokensDetails":[{"modality":"TEXT","tokenCount":132}],"serviceTier":"standard","thoughtsTokenCount":775,"totalTokenCount":1024}},{"answer":"import collections\n\ndef solve(data):\n    balances = collections.defaultdict(int)\n    for item in data.get('invoices', []):\n        balances[item['id']] += item['cents']\n    for item in data.get('payments', []):\n        balances[item['id']] -= item['cents']\n    return {k: balances[k] for k in sorted(balances) if balances[k] != 0}","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.003726,"finished_at":"2026-10-04T16:48:46.346704+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"2c53b6dd2ebfb8e467e4e68491c45ba8012d15bdfcd3c22556c987d374620af5","latency_seconds":4.388984,"model_id":"gemini-3.8-flash","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input has invoices and payments lists of {id, cents}. Sum amounts by id; payments subtract from invoices. Return a dict of nonzero outstanding amounts sorted by id, including negative overpayments. IDs are strings. Empty lists are valid.","raw_response_hash":"b4134a1141371f6b76cce4a783ee093d5bc4defac5c7560c4a592b564e6ac7f1","repetition":2,"request_hash":"5d992ee23ab27b7a29b5f4b10f898cd3e4a5516878020d9e5241b96f4ebf4f20","request_id":null,"sent_at":"2026-10-04T16:48:41.957720+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-reconcile","trial_id":"2918789ec642c928395df45704f7a7794967962416ccb655a7d79738cbf9bae0","usage":{"candidatesTokenCount":98,"promptTokenCount":132,"promptTokensDetails":[{"modality":"TEXT","tokenCount":132}],"serviceTier":"standard","thoughtsTokenCount":869,"totalTokenCount":1099}},{"answer":"def solve(data):\n    balances = {}\n    for inv in data.get('invoices', []):\n        balances[inv['id']] = balances.get(inv['id'], 0) + inv['cents']\n    for pmt in data.get('payments', []):\n        balances[pmt['id']] = balances.get(pmt['id'], 0) - pmt['cents']\n    return {k: balances[k] for k in sorted(balances) if balances[k] != 0}","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.004048,"finished_at":"2026-10-04T16:58:12.251266+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"2c53b6dd2ebfb8e467e4e68491c45ba8012d15bdfcd3c22556c987d374620af5","latency_seconds":5.082665,"model_id":"gemini-3.8-flash","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input has invoices and payments lists of {id, cents}. Sum amounts by id; payments subtract from invoices. Return a dict of nonzero outstanding amounts sorted by id, including negative overpayments. IDs are strings. Empty lists are valid.","raw_response_hash":"f84d76ddaa3dfc4426d34f5434fcef415437c7e7236010260487b157d957dc27","repetition":3,"request_hash":"5d992ee23ab27b7a29b5f4b10f898cd3e4a5516878020d9e5241b96f4ebf4f20","request_id":null,"sent_at":"2026-10-04T16:58:07.168601+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-reconcile","trial_id":"60873e7dfccbfcf6f6848135733d9bb2b66d15f7b83c9c5e0bcc754711d0f068","usage":{"candidatesTokenCount":117,"promptTokenCount":132,"promptTokensDetails":[{"modality":"TEXT","tokenCount":132}],"serviceTier":"standard","thoughtsTokenCount":936,"totalTokenCount":1185}},{"answer":"def solve(data):\n    totals = {}\n    for invoice in data[\"invoices\"]:\n        key = invoice[\"id\"]\n        totals[key] = totals.get(key, 0) + invoice[\"cents\"]\n    for payment in data[\"payments\"]:\n        key = payment[\"id\"]\n        totals[key] = totals.get(key, 0) - payment[\"cents\"]\n    return {key: totals[key] for key in sorted(totals) if totals[key] != 0}","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.001302,"finished_at":"2026-10-04T16:43:41.208432+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"2c53b6dd2ebfb8e467e4e68491c45ba8012d15bdfcd3c22556c987d374620af5","latency_seconds":2.785246,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. 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Empty lists are valid.","raw_response_hash":"ccc0d2f010f811c47b6e71f4743df6b961b303965db83cadffce27ace005d8db","repetition":1,"request_hash":"d963c6e24d2a1428d18737ee9f3d5f1ce7f9001da88bf6df3a25fc41b6372cc1","request_id":"req_ffaeda10c52b4f43992f24eb124ca312","sent_at":"2026-10-04T16:43:38.423186+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-reconcile","trial_id":"9076a747f18e04bd3ce1bef365e728bb4fb6ebdfe19752fba1e7e3da112069b1","usage":{"input_tokens":136,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":103,"output_tokens_details":{"reasoning_tokens":0},"total_tokens":239}},{"answer":"def solve(data):\n    totals = {}\n    for invoice in data[\"invoices\"]:\n        identifier = invoice[\"id\"]\n        totals[identifier] = totals.get(identifier, 0) + invoice[\"cents\"]\n    for payment in data[\"payments\"]:\n        identifier = payment[\"id\"]\n        totals[identifier] = totals.get(identifier, 0) - payment[\"cents\"]\n    return {\n        identifier: totals[identifier]\n        for identifier in sorted(totals)\n        if totals[identifier] != 0\n    }","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.001392,"finished_at":"2026-10-04T16:48:50.567130+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"2c53b6dd2ebfb8e467e4e68491c45ba8012d15bdfcd3c22556c987d374620af5","latency_seconds":4.172123,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input has invoices and payments lists of {id, cents}. Sum amounts by id; payments subtract from invoices. Return a dict of nonzero outstanding amounts sorted by id, including negative overpayments. IDs are strings. 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Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input has invoices and payments lists of {id, cents}. Sum amounts by id; payments subtract from invoices. Return a dict of nonzero outstanding amounts sorted by id, including negative overpayments. IDs are strings. 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Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is arbitrary JSON. Recursively remove dictionary keys whose lowercase spelling equals password, token or secret. 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Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is arbitrary JSON. Recursively remove dictionary keys whose lowercase spelling equals password, token or secret. 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Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is arbitrary JSON. Recursively remove dictionary keys whose lowercase spelling equals password, token or secret. 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Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is arbitrary JSON. Recursively remove dictionary keys whose lowercase spelling equals password, token or secret. Preserve other values and list order; never mutate the original.","raw_response_hash":"6b8e618317182d43a26c1e744d00201c9d3f5b82f2e818f55c1f1225bf6afbe8","repetition":1,"request_hash":"9811f24aa011209125ab1c3ece7e9d564847cc4f8acc3e87dbd887547038dc9f","request_id":"req_21b4c90db85a46ebb34784e4daf5a81e","sent_at":"2026-10-04T16:42:11.522359+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-redact","trial_id":"2caa62a53dd86e5e2d16a4e2b76be47385785a8bfcb7443d514d3a998b55a094","usage":{"input_tokens":120,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":205,"output_tokens_details":{"reasoning_tokens":120},"total_tokens":325}},{"answer":"def solve(data):\n    if isinstance(data, dict):\n        return {\n            key: solve(value)\n            for key, value in data.items()\n            if key.lower() not in (\"password\", \"token\", \"secret\")\n        }\n    if isinstance(data, list):\n        return [solve(value) for value in data]\n    return data","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.00221,"finished_at":"2026-10-04T16:48:07.554821+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"b7bc15dcaf3c57c7db244a0242f1840f2fd97e1015c98ec698c1a6342457f677","latency_seconds":6.115379,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is arbitrary JSON. Recursively remove dictionary keys whose lowercase spelling equals password, token or secret. Preserve other values and list order; never mutate the original.","raw_response_hash":"a60ccf7e9d7e2374d7163331c07cf64984f5ad338ca58ccab1a8d76f4741b6d0","repetition":2,"request_hash":"9811f24aa011209125ab1c3ece7e9d564847cc4f8acc3e87dbd887547038dc9f","request_id":"req_d0c3358d570c49c1a8abc260defc66a4","sent_at":"2026-10-04T16:48:01.439442+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-redact","trial_id":"16597d276ea329160b7832c644f578a1bd745e5d7190db037b3202be332ba5d2","usage":{"input_tokens":120,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":197,"output_tokens_details":{"reasoning_tokens":123},"total_tokens":317}},{"answer":"def solve(data):\n    if isinstance(data, dict):\n        return {\n            key: solve(value)\n            for key, value in data.items()\n            if key.lower() not in (\"password\", \"token\", \"secret\")\n        }\n    if isinstance(data, list):\n        return [solve(value) for value in data]\n    return data","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.00244,"finished_at":"2026-10-04T17:01:09.035687+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"b7bc15dcaf3c57c7db244a0242f1840f2fd97e1015c98ec698c1a6342457f677","latency_seconds":6.991801,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is arbitrary JSON. Recursively remove dictionary keys whose lowercase spelling equals password, token or secret. Preserve other values and list order; never mutate the original.","raw_response_hash":"6deb10f0a8f2313f43793d20f2d689dfcef200d777cfc4d2b679ecfe4cda8f8a","repetition":3,"request_hash":"9811f24aa011209125ab1c3ece7e9d564847cc4f8acc3e87dbd887547038dc9f","request_id":"req_a1af76bf069d43058a8a8bfd30cd8297","sent_at":"2026-10-04T17:01:02.043886+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-redact","trial_id":"4b02e9ff9a80459f5f02b0461c0e6d84795274fe3d5b75ce673a9b1f9a4e36f4","usage":{"input_tokens":120,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":220,"output_tokens_details":{"reasoning_tokens":146},"total_tokens":340}},{"answer":"import bisect is not allowed, so I'll implement the binary search by hand.\n\nThe approach is to aggregate counts by day, sort the distinct days, build prefix sums, and answer each query as the prefix sum over days up to q minus the prefix sum over days up to q-7.\n\n```python\ndef solve(data):\n    events = data.get(\"events\", []) or []\n    queries = data.get(\"query_days\", []) or []\n\n    agg = {}\n    for e in events:\n        d = e[0]\n        c = e[1]\n        agg[d] = agg.get(d, 0) + c\n\n    days = sorted(agg.keys())\n    prefix = [0]\n    for d in days:\n        prefix.append(prefix[-1] + agg[d])\n\n    def upto(x):\n        # number of days <= x\n        lo, hi = 0, len(days)\n        while lo < hi:\n            mid = (lo + hi) // 2\n            if days[mid] <= x:\n                lo = mid + 1\n            else:\n                hi = mid\n        return prefix[lo]\n\n    res = []\n    for q in queries:\n        res.append(upto(q) - upto(q - 7))\n    return res\n```","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.004104,"finished_at":"2026-10-04T16:45:10.519861+00:00","grade":{"checks":[{"case":1,"passed":false},{"case":2,"passed":false},{"case":3,"passed":false}],"passed":false,"reason":"execution_rejected"},"input_hash":"c6f7f8c8f0956173720c269f46431a265622f2c8fb396b1fb5c95f70105acdde","latency_seconds":2.659321,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {events:[[day,count],...], query_days:[integer,...]}. Return totals for each query day covering inclusive days q-6 through q. Events can be unsorted, repeated or negative-day; count is nonnegative. Query order must be preserved.","raw_response_hash":"4315d3216c294e4299fa09c817bb48b77411eded2b7059187c58a60ca1775cc6","repetition":1,"request_hash":"c016611f66c2925f34eadde75ba975eb9028544e9adffd7315a5933e06e567ff","request_id":"req_011CfhXxS6R4mfdFeSmCeDhD","sent_at":"2026-10-04T16:45:07.860540+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-rolling","trial_id":"c543142e50aa44368b474ac0c2790f7de86290b3c8f14e5316329629b0e854e8","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":207,"output_tokens":369,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"def solve(data):\n    import bisect\n    events = data.get(\"events\", []) or []\n    queries = data.get(\"query_days\", []) or []\n    agg = {}\n    for e in events:\n        d, c = e[0], e[1]\n        agg[d] = agg.get(d, 0) + c\n    days = sorted(agg)\n    prefix = [0]\n    for d in days:\n        prefix.append(prefix[-1] + agg[d])\n    res = []\n    for q in queries:\n        lo = bisect.bisect_left(days, q - 6)\n        hi = bisect.bisect_right(days, q)\n        res.append(prefix[hi] - prefix[lo])\n    return res","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.002614,"finished_at":"2026-10-04T16:46:15.280522+00:00","grade":{"checks":[{"case":1,"passed":false},{"case":2,"passed":false},{"case":3,"passed":false}],"passed":false,"reason":"execution_rejected"},"input_hash":"c6f7f8c8f0956173720c269f46431a265622f2c8fb396b1fb5c95f70105acdde","latency_seconds":1.813556,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {events:[[day,count],...], query_days:[integer,...]}. Return totals for each query day covering inclusive days q-6 through q. Events can be unsorted, repeated or negative-day; count is nonnegative. Query order must be preserved.","raw_response_hash":"d1240017075322d0a99f4097881c1ae933d828d7200a5409d73e34797b7207b0","repetition":2,"request_hash":"c016611f66c2925f34eadde75ba975eb9028544e9adffd7315a5933e06e567ff","request_id":"req_011CfhY3Ge2phGBR641v3rCq","sent_at":"2026-10-04T16:46:13.466966+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-rolling","trial_id":"54c46c721a5e6227f8e89b97774499a3f944ef0a2cc707d6e364469bdb9d1390","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":207,"output_tokens":220,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"def solve(data):\n    import bisect\n    events = data.get(\"events\", []) or []\n    queries = data.get(\"query_days\", []) or []\n    agg = {}\n    for e in events:\n        d, c = e[0], e[1]\n        agg[d] = agg.get(d, 0) + c\n    days = sorted(agg)\n    prefix = [0]\n    for d in days:\n        prefix.append(prefix[-1] + agg[d])\n    res = []\n    for q in queries:\n        lo = bisect.bisect_left(days, q - 6)\n        hi = bisect.bisect_right(days, q)\n        res.append(prefix[hi] - prefix[lo])\n    return res","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.002614,"finished_at":"2026-10-04T16:57:18.252234+00:00","grade":{"checks":[{"case":1,"passed":false},{"case":2,"passed":false},{"case":3,"passed":false}],"passed":false,"reason":"execution_rejected"},"input_hash":"c6f7f8c8f0956173720c269f46431a265622f2c8fb396b1fb5c95f70105acdde","latency_seconds":5.848562,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {events:[[day,count],...], query_days:[integer,...]}. Return totals for each query day covering inclusive days q-6 through q. Events can be unsorted, repeated or negative-day; count is nonnegative. Query order must be preserved.","raw_response_hash":"cb7fdb3436d3855358db3e18b2f5abca3bf598a1487c5edef63f1e2b1eb66ad8","repetition":3,"request_hash":"c016611f66c2925f34eadde75ba975eb9028544e9adffd7315a5933e06e567ff","request_id":"req_011CfhYsqoR6MapRGQFee4Sv","sent_at":"2026-10-04T16:57:12.403672+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-rolling","trial_id":"f94cc5a3586ab6d0eb64c7b1120946f6b7a7c664eeaf125b2f043460edf29c8f","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":207,"output_tokens":220,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"import collections\n\ndef solve(data):\n    events = data.get(\"events\", [])\n    query_days = data.get(\"query_days\", [])\n    \n    day_counts = collections.defaultdict(int)\n    for day, count in events:\n        day_counts[day] += count\n        \n    unique_days = sorted(day_counts.keys())\n    n = len(unique_days)\n    \n    pref = [0] * (n + 1)\n    for i in range(n):\n        pref[i + 1] = pref[i] + day_counts[unique_days[i]]\n        \n    def bisect_left(target):\n        lo, hi = 0, n\n        while lo < hi:\n            mid = (lo + hi) // 2\n            if unique_days[mid] < target:\n                lo = mid + 1\n            else:\n                hi = mid\n        return lo\n\n    def bisect_right(target):\n        lo, hi = 0, n\n        while lo < hi:\n            mid = (lo + hi) // 2\n            if target < unique_days[mid]:\n                hi = mid\n            else:\n                lo = mid + 1\n        return lo\n\n    results = []\n    for q in query_days:\n        l = bisect_left(q - 6)\n        r = bisect_right(q)\n        results.append(pref[r] - pref[l])\n        \n    return results","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.00587,"finished_at":"2026-10-04T16:45:17.422740+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"c6f7f8c8f0956173720c269f46431a265622f2c8fb396b1fb5c95f70105acdde","latency_seconds":6.836572,"model_id":"gemini-3.8-flash","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {events:[[day,count],...], query_days:[integer,...]}. Return totals for each query day covering inclusive days q-6 through q. Events can be unsorted, repeated or negative-day; count is nonnegative. Query order must be preserved.","raw_response_hash":"a40f4b2777749517b8515bc0b79313380cb09f40047f4a4c63d7f4cbbb2ee18e","repetition":1,"request_hash":"ee71b7647ac2a670a524fadba41121334bd9521abe5d24719b6f1711dea8e1f5","request_id":null,"sent_at":"2026-10-04T16:45:10.586168+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-rolling","trial_id":"6fc9028f977ab4b6b9e63aa00db938ee9458ecedeab3451af112fb5a795f8c24","usage":{"candidatesTokenCount":349,"promptTokenCount":141,"promptTokensDetails":[{"modality":"TEXT","tokenCount":141}],"serviceTier":"standard","thoughtsTokenCount":1188,"totalTokenCount":1678}},{"answer":"import collections\n\ndef solve(data):\n    events = data.get('events', [])\n    query_days = data.get('query_days', [])\n\n    counts = collections.defaultdict(int)\n    for day, count in events:\n        counts[day] += count\n\n    results = []\n    for q in query_days:\n        total = sum(counts.get(q - i, 0) for i in range(7))\n        results.append(total)\n\n    return results","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.004861,"finished_at":"2026-10-04T16:46:21.585636+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"c6f7f8c8f0956173720c269f46431a265622f2c8fb396b1fb5c95f70105acdde","latency_seconds":6.252511,"model_id":"gemini-3.8-flash","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {events:[[day,count],...], query_days:[integer,...]}. Return totals for each query day covering inclusive days q-6 through q. Events can be unsorted, repeated or negative-day; count is nonnegative. Query order must be preserved.","raw_response_hash":"628b6b5351d200a44b43463e80452993898776988fea047d0161044e777e1f29","repetition":2,"request_hash":"ee71b7647ac2a670a524fadba41121334bd9521abe5d24719b6f1711dea8e1f5","request_id":null,"sent_at":"2026-10-04T16:46:15.333125+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-rolling","trial_id":"34b27812de55025be9ae621bfd779fb86de4b6629a903543b5f4b0e82c204db9","usage":{"candidatesTokenCount":111,"promptTokenCount":141,"promptTokensDetails":[{"modality":"TEXT","tokenCount":141}],"serviceTier":"standard","thoughtsTokenCount":1157,"totalTokenCount":1409}},{"answer":"def solve(data):\n    events = data.get(\"events\", [])\n    query_days = data.get(\"query_days\", [])\n    \n    day_counts = collections.defaultdict(int)\n    for day, count in events:\n        day_counts[day] += count\n        \n    unique_days = sorted(day_counts.keys())\n    prefix = [0] * (len(unique_days) + 1)\n    for idx, d in enumerate(unique_days):\n        prefix[idx + 1] = prefix[idx] + day_counts[d]\n        \n    def bisect_left(arr, x):\n        lo, hi = 0, len(arr)\n        while lo < hi:\n            mid = (lo + hi) // 2\n            if arr[mid] < x:\n                lo = mid + 1\n            else:\n                hi = mid\n        return lo\n\n    def bisect_right(arr, x):\n        lo, hi = 0, len(arr)\n        while lo < hi:\n            mid = (lo + hi) // 2\n            if arr[mid] <= x:\n                lo = mid + 1\n            else:\n                hi = mid\n        return lo\n\n    results = []\n    for q in query_days:\n        left = bisect_left(unique_days, q - 6)\n        right = bisect_right(unique_days, q)\n        results.append(prefix[right] - prefix[left])\n        \n    return results","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.005686,"finished_at":"2026-10-04T16:57:05.795573+00:00","grade":{"checks":[{"case":1,"passed":false},{"case":2,"passed":false},{"case":3,"passed":false}],"passed":false,"reason":"execution_rejected"},"input_hash":"c6f7f8c8f0956173720c269f46431a265622f2c8fb396b1fb5c95f70105acdde","latency_seconds":6.41646,"model_id":"gemini-3.8-flash","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {events:[[day,count],...], query_days:[integer,...]}. Return totals for each query day covering inclusive days q-6 through q. Events can be unsorted, repeated or negative-day; count is nonnegative. Query order must be preserved.","raw_response_hash":"9cec04374faf4a1512b1459e6fd22d7531c238f08cc608ab663133e8470cd830","repetition":3,"request_hash":"ee71b7647ac2a670a524fadba41121334bd9521abe5d24719b6f1711dea8e1f5","request_id":null,"sent_at":"2026-10-04T16:56:59.379113+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-rolling","trial_id":"4bc4d144ff4c552bcbae1412759951f35526b2e9fcb37a86d84ccf3f5b142c9c","usage":{"candidatesTokenCount":354,"promptTokenCount":141,"promptTokensDetails":[{"modality":"TEXT","tokenCount":141}],"serviceTier":"standard","thoughtsTokenCount":1134,"totalTokenCount":1629}},{"answer":"def solve(data):\n    events = sorted(data[\"events\"], key=lambda event: event[0])\n    queries = sorted(enumerate(data[\"query_days\"]), key=lambda item: item[1])\n    results = [0] * len(queries)\n\n    left = 0\n    right = 0\n    total = 0\n\n    for index, day in queries:\n        while right < len(events) and events[right][0] <= day:\n            total += events[right][1]\n            right += 1\n\n        while left < right and events[left][0] < day - 6:\n            total -= events[left][1]\n            left += 1\n\n        results[index] = total\n\n    return results","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.003494,"finished_at":"2026-10-04T16:45:07.807163+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"c6f7f8c8f0956173720c269f46431a265622f2c8fb396b1fb5c95f70105acdde","latency_seconds":7.172942,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {events:[[day,count],...], query_days:[integer,...]}. Return totals for each query day covering inclusive days q-6 through q. Events can be unsorted, repeated or negative-day; count is nonnegative. Query order must be preserved.","raw_response_hash":"7abf8e20ba52bc710eafc66e9b203d96ed416953f75e2a9493dc1cd3aeefa583","repetition":1,"request_hash":"392bb90e816230ae4597061d8164d095b1fb0fda14126b90d37270afdbb9d302","request_id":"req_c2ccfe5405ec466baf2204de40794814","sent_at":"2026-10-04T16:45:00.634221+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-rolling","trial_id":"6bd9851d8fa37fe745137a7860951bb44b77b2afcd7f8563e38af1e5a91e4e91","usage":{"input_tokens":142,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":321,"output_tokens_details":{"reasoning_tokens":167},"total_tokens":463}},{"answer":"def solve(data):\n    events = sorted(data[\"events\"], key=lambda event: event[0])\n    queries = sorted(enumerate(data[\"query_days\"]), key=lambda item: item[1])\n    totals = [0] * len(queries)\n\n    left = 0\n    right = 0\n    total = 0\n    n = len(events)\n\n    for index, day in queries:\n        while right < n and events[right][0] <= day:\n            total += events[right][1]\n            right += 1\n\n        start = day - 6\n        while left < right and events[left][0] < start:\n            total -= events[left][1]\n            left += 1\n\n        totals[index] = total\n\n    return totals","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.003474,"finished_at":"2026-10-04T16:46:29.269583+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"c6f7f8c8f0956173720c269f46431a265622f2c8fb396b1fb5c95f70105acdde","latency_seconds":7.633057,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {events:[[day,count],...], query_days:[integer,...]}. Return totals for each query day covering inclusive days q-6 through q. Events can be unsorted, repeated or negative-day; count is nonnegative. Query order must be preserved.","raw_response_hash":"99ebaba816b882d12ec263b179d1e92982a2b0930dce2acfd401f94ba46db87f","repetition":2,"request_hash":"392bb90e816230ae4597061d8164d095b1fb0fda14126b90d37270afdbb9d302","request_id":"req_4ac75992a87548289b0fe29170055b4e","sent_at":"2026-10-04T16:46:21.636526+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-rolling","trial_id":"c33017201da62c9305566db091ab7d9e27763506cda9e776ac4c1238e327698b","usage":{"input_tokens":142,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":319,"output_tokens_details":{"reasoning_tokens":156},"total_tokens":461}},{"answer":"def solve(data):\n    events = sorted(data[\"events\"])\n    queries = sorted(enumerate(data[\"query_days\"]), key=lambda item: item[1])\n    totals = [0] * len(queries)\n    left = right = 0\n    running_total = 0\n\n    for index, day in queries:\n        while right < len(events) and events[right][0] <= day:\n            running_total += events[right][1]\n            right += 1\n\n        start = day - 6\n        while left < right and events[left][0] < start:\n            running_total -= events[left][1]\n            left += 1\n\n        totals[index] = running_total\n\n    return totals","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.003194,"finished_at":"2026-10-04T16:57:12.350487+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"c6f7f8c8f0956173720c269f46431a265622f2c8fb396b1fb5c95f70105acdde","latency_seconds":6.505306,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is {events:[[day,count],...], query_days:[integer,...]}. Return totals for each query day covering inclusive days q-6 through q. Events can be unsorted, repeated or negative-day; count is nonnegative. Query order must be preserved.","raw_response_hash":"567a5d6a4cf3827872f181161c7a3ee9ed96b67d742283d10f60e8bc75a65420","repetition":3,"request_hash":"392bb90e816230ae4597061d8164d095b1fb0fda14126b90d37270afdbb9d302","request_id":"req_e27901ffd6a04f34acd7b0da62095961","sent_at":"2026-10-04T16:57:05.845181+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-rolling","trial_id":"8daa33a3ff73e355bd3e5f2c5b51ba296efe601eec4afc8995d607045f174bb3","usage":{"input_tokens":142,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":291,"output_tokens_details":{"reasoning_tokens":140},"total_tokens":433}},{"answer":"import unicodedata\n\ndef solve(data):\n    s = unicodedata.normalize(\"NFKD\", data)\n    s = \"\".join(ch for ch in s if not unicodedata.combining(ch))\n    s = s.lower()\n    out = []\n    pending = False\n    for ch in s:\n        if (\"a\" <= ch <= \"z\") or (\"0\" <= ch <= \"9\"):\n            if pending and out:\n                out.append(\"-\")\n            pending = False\n            out.append(ch)\n        else:\n            pending = True\n    return \"\".join(out)","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.002142,"finished_at":"2026-10-04T16:44:46.332600+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"7681c98661d6a0b41dbf19b2eb1f1b831e89f35588ad60b5ba101a1ceb8bae96","latency_seconds":2.20217,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a string. Apply Unicode NFKD normalization, drop combining marks, lowercase, retain only ASCII a-z and digits, replace each run of other characters with one hyphen, strip edge hyphens. Return the slug.","raw_response_hash":"fe96a3b60df163df6ca4bfbb250223a2ca0dbde1cefa674d42e836c7303c4b7b","repetition":1,"request_hash":"8438f71809ed46497eda047d206ade2605203db309c933f63c73d8a557fe7e65","request_id":"req_011CfhXvgoQ52tyjXTHC8E2J","sent_at":"2026-10-04T16:44:44.130430+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-slug","trial_id":"92998081cc71ff0b76d01c46c86731defea17d12a01a65d7513e1ba778802a55","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":201,"output_tokens":174,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"import unicodedata\nimport re\n\ndef solve(data):\n    s = unicodedata.normalize(\"NFKD\", data)\n    s = \"\".join(c for c in s if not unicodedata.combining(c))\n    s = s.lower()\n    s = re.sub(r\"[^a-z0-9]+\", \"-\", s)\n    return s.strip(\"-\")","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.001512,"finished_at":"2026-10-04T16:47:04.312013+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"7681c98661d6a0b41dbf19b2eb1f1b831e89f35588ad60b5ba101a1ceb8bae96","latency_seconds":1.771817,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a string. Apply Unicode NFKD normalization, drop combining marks, lowercase, retain only ASCII a-z and digits, replace each run of other characters with one hyphen, strip edge hyphens. Return the slug.","raw_response_hash":"8bbb6f7ed93edc1d01423576fc9831a68165bb3ed3a1259ad9d9f05020f4295c","repetition":2,"request_hash":"8438f71809ed46497eda047d206ade2605203db309c933f63c73d8a557fe7e65","request_id":"req_011CfhY6tLxfzjQfnSGfN1uf","sent_at":"2026-10-04T16:47:02.540196+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-slug","trial_id":"49147f47b00688a9978dc6ef23fb01d242f35250d8ca2ebd97b439d16a16da83","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":201,"output_tokens":111,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"import unicodedata\n\ndef solve(data):\n    s = unicodedata.normalize(\"NFKD\", data)\n    s = \"\".join(ch for ch in s if not unicodedata.combining(ch))\n    s = s.lower()\n    out = []\n    pending = False\n    for ch in s:\n        if (\"a\" <= ch <= \"z\") or (\"0\" <= ch <= \"9\"):\n            if pending and out:\n                out.append(\"-\")\n            pending = False\n            out.append(ch)\n        else:\n            pending = True\n    return \"\".join(out)","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.002142,"finished_at":"2026-10-04T17:00:11.721969+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"7681c98661d6a0b41dbf19b2eb1f1b831e89f35588ad60b5ba101a1ceb8bae96","latency_seconds":2.18822,"model_id":"claude-sonnet-5-5","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a string. Apply Unicode NFKD normalization, drop combining marks, lowercase, retain only ASCII a-z and digits, replace each run of other characters with one hyphen, strip edge hyphens. Return the slug.","raw_response_hash":"6f5dfe205d317fcc6fc5762a0188bb1a29b993b37a0ab8c19e1aac4357844c8a","repetition":3,"request_hash":"8438f71809ed46497eda047d206ade2605203db309c933f63c73d8a557fe7e65","request_id":"req_011CfhZ6uF9fsoCwgTFxBzEk","sent_at":"2026-10-04T17:00:09.533749+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Claude direct API, Standard global"},"state":"responded","task_id":"coding-slug","trial_id":"2d3c50fd2e771204e98cedf305a4e1c1b9c092fa87dde24ddb039f2292dbe3c5","usage":{"cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":0},"cache_creation_input_tokens":0,"cache_read_input_tokens":0,"inference_geo":"global","input_tokens":201,"output_tokens":174,"output_tokens_details":{"thinking_tokens":0},"service_tier":"standard"}},{"answer":"import re\nimport unicodedata\n\ndef solve(data):\n    normalized = unicodedata.normalize('NFKD', data)\n    without_marks = ''.join(c for c in normalized if not unicodedata.combining(c))\n    lowercased = without_marks.lower()\n    slug = re.sub(r'[^a-z0-9]+', '-', lowercased)\n    return slug.strip('-')","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.003042,"finished_at":"2026-10-04T16:44:51.395361+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"7681c98661d6a0b41dbf19b2eb1f1b831e89f35588ad60b5ba101a1ceb8bae96","latency_seconds":5.007005,"model_id":"gemini-3.8-flash","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a string. Apply Unicode NFKD normalization, drop combining marks, lowercase, retain only ASCII a-z and digits, replace each run of other characters with one hyphen, strip edge hyphens. Return the slug.","raw_response_hash":"bfa5514b3884038b272585e3fe3b261f1399a5a81ae475cb9768459b9c3b8fb3","repetition":1,"request_hash":"5d5010f2b82f2bf3ab641ed3c26e1dbea4cae4ce56d577b6987ff67abe260757","request_id":null,"sent_at":"2026-10-04T16:44:46.388356+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-slug","trial_id":"dad3eda8a330802b5fcf3e480077373e9d8e90e5d036da3a7deef1bd95323b1d","usage":{"candidatesTokenCount":97,"promptTokenCount":130,"promptTokensDetails":[{"modality":"TEXT","tokenCount":130}],"serviceTier":"standard","thoughtsTokenCount":688,"totalTokenCount":915}},{"answer":"import re\nimport unicodedata\n\ndef solve(data):\n    normalized = unicodedata.normalize('NFKD', data)\n    without_marks = ''.join(\n        c for c in normalized\n        if not unicodedata.combining(c) and not unicodedata.category(c).startswith('M')\n    )\n    lowered = without_marks.lower()\n    return re.sub(r'[^a-z0-9]+', '-', lowered).strip('-')","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.005858,"finished_at":"2026-10-04T16:47:10.873954+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"7681c98661d6a0b41dbf19b2eb1f1b831e89f35588ad60b5ba101a1ceb8bae96","latency_seconds":6.512597,"model_id":"gemini-3.8-flash","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a string. Apply Unicode NFKD normalization, drop combining marks, lowercase, retain only ASCII a-z and digits, replace each run of other characters with one hyphen, strip edge hyphens. Return the slug.","raw_response_hash":"e37f6232ddaffe87593156e8ebc75d6f36a611c5539fe39b49969569f82eaf6d","repetition":2,"request_hash":"5d5010f2b82f2bf3ab641ed3c26e1dbea4cae4ce56d577b6987ff67abe260757","request_id":null,"sent_at":"2026-10-04T16:47:04.361357+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-slug","trial_id":"8d260de45e4120d88d3d56fb1bc8828bfd9d35c24403804afff148275f350a54","usage":{"candidatesTokenCount":109,"promptTokenCount":130,"promptTokensDetails":[{"modality":"TEXT","tokenCount":130}],"serviceTier":"standard","thoughtsTokenCount":1427,"totalTokenCount":1666}},{"answer":"import re\nimport unicodedata\n\ndef solve(data: str) -> str:\n    normalized = unicodedata.normalize('NFKD', data)\n    without_marks = ''.join(\n        c for c in normalized\n        if not unicodedata.combining(c) and not unicodedata.category(c).startswith('M')\n    )\n    lowered = without_marks.lower()\n    slug = re.sub(r'[^a-z0-9]+', '-', lowered)\n    return slug.strip('-')","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.009375,"finished_at":"2026-10-04T17:00:06.200883+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"7681c98661d6a0b41dbf19b2eb1f1b831e89f35588ad60b5ba101a1ceb8bae96","latency_seconds":11.136128,"model_id":"gemini-3.8-flash","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a string. Apply Unicode NFKD normalization, drop combining marks, lowercase, retain only ASCII a-z and digits, replace each run of other characters with one hyphen, strip edge hyphens. Return the slug.","raw_response_hash":"2c219f7b413a8a290b698861a631d7a416ea72f1c5c8ddfa37879f5965a96293","repetition":3,"request_hash":"5d5010f2b82f2bf3ab641ed3c26e1dbea4cae4ce56d577b6987ff67abe260757","request_id":null,"sent_at":"2026-10-04T16:59:55.064755+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"Gemini Developer API, paid Standard"},"state":"responded","task_id":"coding-slug","trial_id":"2b45bf67fe3e1c16c8583a1cfa5fcfead81049c9ebdddebd51bbdf1607e3c74d","usage":{"candidatesTokenCount":120,"promptTokenCount":130,"promptTokensDetails":[{"modality":"TEXT","tokenCount":130}],"serviceTier":"standard","thoughtsTokenCount":2354,"totalTokenCount":2604}},{"answer":"import unicodedata\nimport re\n\ndef solve(data):\n    normalized = unicodedata.normalize(\"NFKD\", data)\n    text = \"\".join(char for char in normalized if not unicodedata.combining(char))\n    return re.sub(r\"[^a-z0-9]+\", \"-\", text.lower()).strip(\"-\")","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.00098,"finished_at":"2026-10-04T16:44:44.078219+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"7681c98661d6a0b41dbf19b2eb1f1b831e89f35588ad60b5ba101a1ceb8bae96","latency_seconds":2.088538,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a string. Apply Unicode NFKD normalization, drop combining marks, lowercase, retain only ASCII a-z and digits, replace each run of other characters with one hyphen, strip edge hyphens. Return the slug.","raw_response_hash":"b9628af685e5fa5987e1cced64f06f3fa286433708797e16f22e4f2a1202e568","repetition":1,"request_hash":"403ead553e16c9ebd11e4bdcb928053cd819d3bf5bd9f81da056951ec9f7ea96","request_id":"req_fee4f4a33b3244ac94c2bc394a41bee7","sent_at":"2026-10-04T16:44:41.989681+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-slug","trial_id":"c240d930a83be0cc3f8138bd7e152eae2d2f6cf0b878e2191ad96991a405f414","usage":{"input_tokens":135,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":71,"output_tokens_details":{"reasoning_tokens":0},"total_tokens":206}},{"answer":"import re\nimport unicodedata\n\ndef solve(data):\n    normalized = unicodedata.normalize(\"NFKD\", data)\n    normalized = \"\".join(c for c in normalized if not unicodedata.combining(c))\n    return re.sub(r\"[^a-z0-9]+\", \"-\", normalized.lower()).strip(\"-\")","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.00098,"finished_at":"2026-10-04T16:47:12.892804+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"7681c98661d6a0b41dbf19b2eb1f1b831e89f35588ad60b5ba101a1ceb8bae96","latency_seconds":1.965378,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a string. Apply Unicode NFKD normalization, drop combining marks, lowercase, retain only ASCII a-z and digits, replace each run of other characters with one hyphen, strip edge hyphens. Return the slug.","raw_response_hash":"bf4c1d0afc7326a36cdecf73d51c3d25d4ff58f0a82548595c5dcd7877430bef","repetition":2,"request_hash":"403ead553e16c9ebd11e4bdcb928053cd819d3bf5bd9f81da056951ec9f7ea96","request_id":"req_8b32d9c3594b4d41a8e371986e3dad8a","sent_at":"2026-10-04T16:47:10.927426+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-slug","trial_id":"34c312ff3a4cc98f87b07359422592af0d5ac6036f21daee09ffbc4ad249521f","usage":{"input_tokens":135,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":71,"output_tokens_details":{"reasoning_tokens":0},"total_tokens":206}},{"answer":"import unicodedata\n\ndef solve(data):\n    normalized = unicodedata.normalize(\"NFKD\", data)\n    normalized = \"\".join(\n        char for char in normalized if not unicodedata.combining(char)\n    ).lower()\n\n    result = []\n    pending_hyphen = False\n    for char in normalized:\n        if \"a\" <= char <= \"z\" or \"0\" <= char <= \"9\":\n            if pending_hyphen and result:\n                result.append(\"-\")\n            result.append(char)\n            pending_hyphen = False\n        else:\n            pending_hyphen = True\n\n    return \"\".join(result)","billed_net_usd":null,"enabled_tools":[],"estimated_net_usd":0.00163,"finished_at":"2026-10-04T17:00:09.455152+00:00","grade":{"checks":[{"case":1,"passed":true},{"case":2,"passed":true},{"case":3,"passed":true},{"case":4,"passed":true},{"case":5,"passed":true}],"passed":true,"reason":"checked"},"input_hash":"7681c98661d6a0b41dbf19b2eb1f1b831e89f35588ad60b5ba101a1ceb8bae96","latency_seconds":3.167575,"model_id":"gpt-6.1-sol","output_cap_reached":false,"prompt":"Implement solve(data) in Python 3.9. Allowed imports: math, json, csv, io, re, collections, heapq, unicodedata. Use functions and built-in types; no custom classes, private/dunder attributes, globals/nonlocals, or async. Return only Python code, with no Markdown. Do not read files, use the network, run processes or access system state. Input is a string. Apply Unicode NFKD normalization, drop combining marks, lowercase, retain only ASCII a-z and digits, replace each run of other characters with one hyphen, strip edge hyphens. Return the slug.","raw_response_hash":"0aabeb4b65394906ecd0bb0d4a4d92ac6eda5a04a74a37538ac25a4851f06dfe","repetition":3,"request_hash":"403ead553e16c9ebd11e4bdcb928053cd819d3bf5bd9f81da056951ec9f7ea96","request_id":"req_9b3b31ad3aca4113a61871c7a2264040","sent_at":"2026-10-04T17:00:06.287577+00:00","settings":{"effort":"medium","max_output_tokens":8192,"surface":"OpenAI direct API, Standard global"},"state":"responded","task_id":"coding-slug","trial_id":"d086709919dc0e5147c77ac557a0c595c3265ccd66d1aa1cbb570ad43015192b","usage":{"input_tokens":135,"input_tokens_details":{"cache_write_tokens":0,"cached_tokens":0},"output_tokens":136,"output_tokens_details":{"reasoning_tokens":0},"total_tokens":271}}]}