Verdict: Claude Opus 5 has the strongest current public case for the best overall agentic and professional-work model. GPT-5.6 Sol is the more compelling all-round choice when OpenAI’s broad tool stack, long context and coding workflow matter as much as the last few benchmark points. Grok 4.6 may be the value leader, but this is a launch-day first look, not a settled recommendation.
Editor’s note: Evidence was checked through August 12, 2026, the day Grok 4.6 launched. This comparison separates vendor-published claims from independent results. Controlled hands-on testing is still required before a hands-on verdict.
A model can lead an aggregate intelligence index, lose a repository-level coding test, excel at browser research and still be the wrong choice once price, latency, tool support and reliability enter the decision.
The public evidence for GPT-5.6 Sol, Claude Opus 5 and Grok 4.6 makes that trade-off plain. On Artificial Analysis’s current Intelligence Index, Opus 5 leads this group at 63, followed by Claude Fable 5 at 62, with Sol and Grok 4.6 tied at 61. Grok reaches that tier at a much lower list price. Sol offers the broadest documented first-party tool menu of the three. Opus has the best public showing on several work-oriented agent benchmarks.
The winner depends on the work.
Quick verdict
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Best overall on current public evidence: Claude Opus 5. It leads the latest independent aggregate snapshot and has particularly strong results on professional tasks, computer use and long-running agents.
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Best OpenAI generalist: GPT-5.6 Sol. It combines a 1.05-million-token context window, 128,000-token maximum output and a deep tool stack. Its weak point is economics: output is $30 per million tokens, and very long prompts trigger a higher rate.
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Best value, provisionally: Grok 4.6. Its base API price is $2 per million input tokens and $6 per million output tokens, while its launch-day independent aggregate score matches Sol. That is unusually strong price-performance, but one day of evidence is not enough to establish production reliability.
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Best capability-at-any-cost secondary option: Claude Fable 5. Fable remains Anthropic’s top capability tier and sits just behind Opus in the current independent aggregate, but its $10/$50 input/output pricing limits its appeal.
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Best secondary comparator for native multimodal input: Gemini 3.1 Pro. It accepts text, images, audio, video and PDFs within a one-million-token context window. It is still a preview model, and the current public head-to-head evidence is less complete.
This verdict rests on audited public sources. The hands-on winner remains open.
GPT-5.6 Sol vs Opus 5 vs Grok 4.6: Specifications and API pricing
| Model | Status and model ID | Context / maximum output | Documented input modalities | API price per 1M tokens: input / cached input / output | Important pricing condition |
|---|---|---|---|---|---|
| GPT-5.6 Sol | Available; gpt-5.6-sol (gpt-5.6 alias) |
1.05M / 128K | Text, images | $5 / $0.50 / $30 | Above 272K input tokens, the full request is billed at 2× input and 1.5× output rates |
| Claude Opus 5 | Generally available; claude-opus-5 |
1M / 128K | Text, images | $5 / $0.50 cache hit / $25 | Standard pricing applies across the full 1M context; cache writes cost extra |
| Grok 4.6 | Released August 12; grok-4.6 |
500K / no text output limit | Text, images | $2 / $0.50 / $6 | At 200K input tokens or more, the full request is $4 / $1 / $12 |
| Claude Fable 5 | Generally available; claude-fable-5 |
1M / 128K | Text, images | $10 / $1 cache hit / $50 | Standard pricing applies across the full 1M context; cache writes cost extra |
| Gemini 3.1 Pro | Preview; gemini-3.1-pro-preview |
1M / 64K | Text, images, audio, video, PDFs | $2 / $0.20 / $12 | Above 200K input tokens, rates rise to $4 / $0.40 / $18 |
Sources: official OpenAI model specification, Anthropic model overview and pricing page, xAI Grok 4.6 developer documentation and pricing, and Google’s Gemini 3.1 Pro page and API pricing. Prices exclude tools, storage, regional premiums and batch discounts.
The list prices need context. Claude’s current tokenizer can produce roughly 30% more tokens for the same text than earlier Claude models, according to Anthropic, so equal documents do not necessarily create equal bills across vendors. Tool calls, retries, caching and agent loops can matter more than the headline rate.
For illustration, a single 150K-input, 10K-output request with no cache or tool charges would cost approximately $1.05 on Sol, $1 on Opus, $0.36 on Grok, $2 on Fable and $0.42 on Gemini. At 350K input and 20K output, the corresponding figures are about $4.40, $2.25, $1.64, $4.50 and $1.76 because Sol, Grok and Gemini cross long-prompt pricing thresholds. These are arithmetic examples, not measured workload costs.
How the five models differ
GPT-5.6 Sol: the tool-rich generalist
OpenAI positions Sol as its frontier model for coding, research, computer use and professional work. It supports reasoning levels from none through max and documents a large set of hosted tools: web and file search, image generation, code interpreter, hosted shell, patch application, skills, computer use, MCP and tool search.
That breadth matters in real deployments. A benchmark measures a model inside a particular evaluation setup, while production work also depends on whether the model can reach the browser, terminal, files, APIs and applications that contain the work. Sol’s strongest case combines high-end reasoning with an unusually complete tool environment.
The trade-off is cost. Its $30 output rate is higher than Opus 5 and five times Grok 4.6’s base output price. Sol’s long-context surcharge also means a request just above 272K input tokens changes the economics of the entire call.
Claude Opus 5: the work-oriented leader
Anthropic launched Opus 5 on July 24, 2026, describing it as the best Claude choice for complex agentic coding and enterprise workflows. It has a one-million-token context window, 128K maximum output and adaptive reasoning.
The public evidence supports a narrow but meaningful lead. Opus ranks first among these models in Artificial Analysis’s current aggregate snapshot, and Anthropic’s system card shows particularly strong results in computer use, professional deliverables and automation. Unlike earlier long-context Claude pricing, Anthropic says the full one-million-token window now uses the standard per-token rate.
Anthropic’s own system card says Opus 5 is not more capable overall than Fable 5; the models trade wins. Opus offers near-top capability at half Fable’s standard token price.
Grok 4.6: a compelling first look, not a mature verdict
xAI released Grok 4.6 on August 12, 2026. Its 500K context window is smaller than Sol, Opus, Fable and Gemini, but still large enough for substantial repositories and document collections. It supports text and image input, four reasoning levels, structured output and function calling; xAI also documents web search, X search and code execution within its platform.
The launch-day numbers are stronger than the price suggests. Independent evaluator Artificial Analysis places Grok level with Sol on its aggregate index and reports a cost of $0.84 per Intelligence Index task. Grok also performs well on agentic banking, terminal work and professional deliverables.
This is still a first look. There is no meaningful post-launch record yet for uptime, regressions, refusal behaviour, long-horizon reliability or performance under diverse production scaffolds. xAI also reports slightly different cutoff dates across its materials: January 2026 in the model card and February 1, 2026 in its developer documentation. The discrepancy does not invalidate the model, but it is another reason to avoid premature certainty.
xAI says Grok 4.6 received supplemental training on anonymized Cursor workflow data. That may improve alignment with real coding-agent behaviour. Cursor-shaped evaluations therefore need an explicit ecosystem-alignment caveat, without turning that caveat into an unsupported accusation of contamination.
Independent evidence: the cleanest common test setup
The most useful common-run snapshot available on launch day comes from Artificial Analysis. Its Intelligence Index v4.1.1 combines nine evaluations and weights agents at 34%, coding at 24%, scientific reasoning at 24% and general reasoning at 18%. The index is English and text-only, so it does not measure native audio, video, image generation, every tool integration or enterprise governance.
| Independent evidence | Claude Opus 5 | Claude Fable 5 | GPT-5.6 Sol | Grok 4.6 | Readout |
|---|---|---|---|---|---|
| Artificial Analysis Intelligence Index v4.1.1, August 12 snapshot | 63 | 62 | 61 | 61 | Opus leads narrowly; Grok enters level with Sol |
| GDPval-AA v2 Elo | Higher than 1,753 | Confidence intervals overlap Grok | Below Grok in the cited snapshot | 1,753 | Grok trails only Opus; uncertainty prevents a clean Fable separation |
| AA Briefcase Elo | Higher than 1,577 | Approximately Grok’s tier | Below Grok in the cited snapshot | 1,577 | Grok is competitive on multi-step analytical and document work but does not lead |
| Terminal-Bench 2.1 | Not reported | Not reported | Not reported | 88.4% | Strong Grok result; missing same-report rows prevent a fresh four-way ranking |
Sources: Artificial Analysis’s Grok 4.6 launch-day analysis and Intelligence Index methodology. “Not reported” means the cited launch-day report did not provide a directly comparable number; it does not mean the model was tested and scored zero.
Artificial Analysis also reports Grok at 50.7% on τ³-Banking, placing it in the top two models on that agentic task. Its $0.84 average cost per Intelligence Index task is notably below the $1.04 Artificial Analysis reported for Sol in its July launch analysis. That comparison is directionally useful, not a guaranteed API bill: different runs, reasoning settings and agent trajectories can change both token use and cost.
The independent picture is clearer than the launch marketing, though still incomplete. Opus has the best aggregate position. Grok has the most striking value result. Sol remains in the same capability band, and Fable stays near the top at a premium price.
Independent coding evidence is the largest gap. Scale’s SWE-Bench Pro is designed around contamination resistance, diverse repositories and reproducible Docker environments, but its public leaderboard does not yet provide a clean, current row for all three headline models under one configuration. Repository-level coding claims should remain provisional until it does or until controlled testing is complete.
Vendor-reported benchmarks: useful, but not a common scoreboard
The following results come from model makers’ launch material or system cards. They remain vendor-reported even when an outside organization created the benchmark. The vendors use different reasoning settings, tools, scaffolds, trial counts and benchmark versions, so their tables do not form one common scoreboard.
OpenAI’s reported results for GPT-5.6 Sol
OpenAI reports 80.0 on the Artificial Analysis Coding Agent Index, 64.6% on SWE-Bench Pro, 72.7% on DeepSWE v1.1, 88.8% on Terminal-Bench 2.1 and 90.4% on BrowseComp. It also reports 62.6% on OSWorld2 and 18.1% on AutomationBench. These are strong breadth signals, particularly for coding and browsing.
They are not proof that Sol wins each category. OpenAI’s launch tables combine its own runs with externally sourced competitor figures, and some charts use simulated or estimated cost and latency. The newer independent aggregate snapshot also supersedes the older launch-day index values shown on OpenAI’s page.
Source: OpenAI’s GPT-5.6 launch report.
Anthropic’s reported results for Claude Opus 5
Anthropic’s system card reports 79.2% on SWE-Bench Pro, 68.8% on DeepSWE v1.1, 90.8% on BrowseComp, 70.6% on OSWorld2 and 26.0% on AutomationBench. The card says its default table uses adaptive reasoning at maximum effort and generally averages five trials unless a row says otherwise.
Those figures make Opus look exceptionally strong at repository work, browsing, computer use and multi-step automation. Anthropic also publishes Opus at 1,861 Elo on GDPval-AA v2 and 1,720 on AA Briefcase, but it explicitly identifies those two evaluations as independently run by Artificial Analysis. They are independent evaluator results reproduced in a vendor publication, not Anthropic’s own test runs.
Source: Anthropic’s Claude Opus 5 System Card and launch announcement.
xAI’s reported results for Grok 4.6
xAI’s model card reports 61.3% on FrontierCode v1.1 Extended, 65.9% on DeepSWE v1.1, 26.0% on Terminal-Bench 3.0, 56.4% on APEX-SWE, 31.9% on SWE Marathon v1.1 and 57.5% on APEX Agents.
The coding picture is mixed. In xAI’s own table, Grok trails Opus, Fable and Sol on DeepSWE and Terminal-Bench 3.0, and trails Opus and Fable on APEX-SWE. Its FrontierCode result is closer to the leaders. Grok’s first-look case rests on reaching the frontier band at an aggressive price, not on winning every benchmark.
Source: xAI’s Grok 4.6 model card and launch announcement.
Google’s reported results for Gemini 3.1 Pro
Google reports 44.4% on Humanity’s Last Exam without tools, 51.4% with search and code, 77.1% on ARC-AGI-2, 94.3% on GPQA Diamond and 68.5% on Terminal-Bench 2.0. Gemini’s multimodal inputs and one-million-token context make it an important secondary comparator, but it is not a clean participant in every current common-run table. Its preview status also adds deployment risk.
Source: Google DeepMind’s Gemini 3.1 Pro model page.
Coding: no uncontested winner
The public coding results split by benchmark.
Opus leads Sol and Grok on Anthropic’s SWE-Bench Pro table and leads Grok on several xAI-published agentic coding tests. Sol leads Opus and Grok on DeepSWE v1.1 in the competitor results reproduced across launch materials. Grok is closer on FrontierCode but has weaker vendor-reported results on longer and more operational coding tasks.
Those results can differ without contradicting one another. SWE-Bench Pro measures issue resolution in repositories. DeepSWE tests coding-agent performance in a particular scaffold. Terminal-Bench measures command-line execution, and version 2.0, 2.1 and 3.0 are not interchangeable. Reasoning effort, retry policy and agent design can also shift a model’s score.
The provisional coding recommendation is:
- Choose Opus 5 when autonomous repository work and long-running agents are the priority.
- Choose Sol when coding is embedded in a broader OpenAI tool workflow, especially one involving shell, patching, files and browser work.
- Pilot Grok 4.6 when cost matters enough to justify launch-week uncertainty.
That recommendation must be revised after controlled repository tests.
Research, analysis and professional deliverables
Opus currently has the best public claim in this category. It leads the current Artificial Analysis aggregate, leads GDPval-AA v2 among the models discussed in the launch-day report, and posts the strongest published results on AutomationBench and OSWorld2 in Anthropic’s system card.
Grok’s first results are impressive rather than dominant. A 1,753 GDPval-AA v2 Elo and 1,577 AA Briefcase Elo place it near the top group at a much lower list price. Artificial Analysis reports that Grok typically used fewer agent turns and fewer input tokens than the highest-effort Opus configuration on Briefcase. That supports Grok’s efficiency case, although Opus still produced the stronger result.
Sol remains credible here. OpenAI reports strong browsing and professional-work results, and Artificial Analysis’s July evaluation found Sol particularly strong on presentation quality even when Fable led some analytical dimensions. Controlled tests now need to measure how consistently Sol produces that quality relative to Opus under identical instructions and tools.
Long context and multimodality
Context-window numbers are capacity limits, not evidence of reliable recall across the entire window.
Sol has the largest advertised context here at 1.05 million tokens, followed closely by the one-million-token windows of Opus, Fable and Gemini. Grok’s 500K window is smaller but still substantial. Output limits also matter: Sol, Opus and Fable document 128K maximum output, Gemini documents 64K, and xAI documents no text output limit for Grok.
Gemini is the most broadly multimodal input model in this comparison because it natively accepts audio and video in addition to text, images and PDFs. Sol, Claude and Grok accept images but produce text in the documented API configurations. That makes Gemini a sensible secondary option for media-heavy analysis, even though its preview label and incomplete common-run evidence prevent an overall recommendation.
No context-window winner should be declared until the models are tested for retrieval accuracy, citation fidelity, instruction retention and cost near the advertised limit.
Tools and deployment fit
Sol has the clearest documented breadth of hosted tools. That makes it attractive for teams already building with OpenAI’s Responses platform or Codex-style workflows.
Opus’s strongest deployment fit is complex agentic work where model quality and long-horizon execution justify a premium over cheaper models. Its list price is competitive with Sol, particularly on output and on requests beyond Sol’s 272K surcharge threshold.
Grok’s immediate appeal is economical tool-using inference, including access to web and X search in xAI’s ecosystem. Production buyers should still test service reliability, tool-call correctness, access controls and observability rather than infer them from benchmark scores.
Gemini is the natural secondary candidate for Google-oriented and media-heavy stacks. Fable is the escalation model when a hard task justifies spending roughly twice Opus’s standard token rate.
Hands-on tests required before the verdict is final
Hands-on testing has not yet been performed, so this article does not claim a hands-on winner. Planned follow-up work includes five-trial repository repair and feature tasks; blinded research-memo and spreadsheet/deck evaluations; long-context retrieval, instruction-retention and multimodal extraction tests; and repeated browser or computer-use agent tasks with fixed success criteria.
Future reporting should include pass and completion rates, factual and citation errors, regressions, human interventions, tool errors, latency, turns and measured cost. Grok 4.6 remains a launch-day first look until comparable runs are complete.
Which model should you choose?
Choose Claude Opus 5 if quality on complex work is the priority
Opus is the most defensible default for autonomous professional workflows today. It leads the latest independent aggregate snapshot and has strong evidence on computer use, automation and professional deliverables. It is also cheaper than Sol on output tokens and does not add a long-context multiplier within its one-million-token window.
Choose GPT-5.6 Sol if the whole tool ecosystem matters
Sol is the stronger choice when model capability must be combined with hosted shell work, patching, file search, web research, image generation, MCP and computer use. It remains a top-tier model in independent testing. Watch output-heavy costs and prompts beyond 272K tokens.
Pilot Grok 4.6 if price-performance matters most
Grok’s launch-day numbers are good enough to demand attention. Matching Sol’s independent aggregate score at $2/$6 base token pricing is a serious value proposition. But it is not yet evidence of stable production behaviour. Use a bounded pilot, preserve a fallback model and measure retries, tool failures and total task cost.
Use Claude Fable 5 as an escalation tier
Fable sits near the top of the independent rankings and trades benchmark wins with Opus. Its $10 input and $50 output rates make more sense for the hardest cases than as a universal default.
Use Gemini 3.1 Pro carefully for native media-heavy input
Gemini has the broadest documented input modalities in this comparison and competitive pricing below 200K tokens. Its preview status and weaker current head-to-head coverage make it a secondary comparator, not the winner of this article.
Final verdict
If choosing today from public evidence alone, Claude Opus 5 is the best overall model, GPT-5.6 Sol is the best tool-rich generalist, and Grok 4.6 is the most promising value play.
Grok carries the largest asterisk. This first look rests on launch-day documentation and one early independent evaluation. Grok has earned a place in the top tier, but not yet a production-reliability verdict.
Fable 5 remains the premium escalation option, while Gemini 3.1 Pro is the most relevant secondary comparator for multimodal input. Neither changes the headline result: Opus has the strongest evidence today, Sol may be the more useful system for some teams, and Grok has made the price-performance contest much harder to ignore.
The hands-on tests should decide the final recommendation.
How this comparison was evaluated
This article uses three evidence classes:
- Official specifications and pricing for model IDs, availability, context limits, modalities and list rates.
- Independent results only when an outside evaluator ran the model in its own disclosed evaluation setup.
- Vendor-reported results for scores published by a model maker, including competitor numbers reproduced from other sources.
Scores are compared only when the benchmark version, metric and evaluation setup are sufficiently aligned. A result on Terminal-Bench 2.0 is not treated as a result on 2.1 or 3.0. Best-of-N, multiple attempts, tool access and reasoning effort are part of the result, not footnotes to ignore.
Open-weight models are intentionally outside this head-to-head. They should be evaluated in a separate lane that accounts for self-hosting cost, quantization, hardware, licensing and operational control; folding them into an API-model ranking would create false precision.
Frequently asked questions
Is Grok 4.6 better than GPT-5.6 Sol?
Not conclusively. They tie at 61 on the current Artificial Analysis Intelligence Index, while Grok is much cheaper at base API rates. Sol has a larger context window and a broader documented hosted-tool environment. Grok needs more post-launch independent and hands-on evidence.
Is Claude Opus 5 better than Claude Fable 5?
It depends on the task. Opus leads the current independent aggregate and costs half as much per standard input and output token. Anthropic says Opus is not more capable overall than Fable, and the two models trade benchmark wins. Fable is best treated as a premium escalation option.
Which model is best for coding?
There is no clean public winner. Opus has the strongest showing on several repository and agent benchmarks; Sol is compelling inside its tool-rich coding environment; and Grok offers aggressive pricing but weaker results on several long-horizon coding tests in xAI’s own card. Controlled same-repository testing is required.
Which model is cheapest?
At standard list rates below the long-context thresholds, Grok 4.6 is cheapest among the three headline models at $2 per million input tokens and $6 per million output tokens. Total task cost can reverse a rate-card advantage if a model uses more turns, retries or tool calls.
Why isn’t Gemini 3.1 Pro a headline model?
Gemini remains a preview model and does not yet appear in the cleanest current independent head-to-head for all of the work categories assessed here. It is included because its one-million-token context, native audio/video inputs and pricing make it a serious alternative for multimodal work.
When will this verdict be updated?
The first substantive revision should occur after the controlled hands-on suite is complete or when a reputable independent evaluator publishes a common-run comparison that includes all three headline models. Grok’s first-look label should remain until at least one of those conditions is met.