Kingy measured this pilot on October 12, 2026. Muse Wonder 1.0, GPT-6.1 Sol and Claude Sonnet 5.5 each completed two fresh conversations on the same Python project. All six runs passed all 33 final checks on their first attempt. Every applicable checkpoint case passed, and we observed no previously passing behavior regress and no repairs. This small task separated cost more than coding quality.
The project was a fictional campaign-budget planner. We added first-occurrence deduplication, archive and channel filters, stable priority ordering, decimal-dollar migration and an audit list of unaffordable proposals. Priority explicitly replaced the original input-order rule. Two unrelated writing tasks inserted competing preferences such as alphabetical order and newest information first, while saying that those preferences belonged to another project.
| Configuration and repetition | Final cases | First-attempt checkpoint cases | Regressed behavior checks | Repairs | Active time | Token estimate, US$ |
|---|---|---|---|---|---|---|
| Muse Wonder 1.0, run 1 | 33/33 | 124/124 | 0 | 0 | 81.8s | $0.1863 |
| Muse Wonder 1.0, run 2 | 33/33 | 124/124 | 0 | 0 | 95.4s | $0.2419 |
| GPT-6.1 Sol, run 1 | 33/33 | 124/124 | 0 | 0 | 101.8s | $0.0540 |
| GPT-6.1 Sol, run 2 | 33/33 | 124/124 | 0 | 0 | 103.3s | $0.0523 |
| Claude Sonnet 5.5, run 1 | 33/33 | 124/124 | 0 | 0 | 102.7s | $0.1867 |
| Claude Sonnet 5.5, run 2 | 33/33 | 124/124 | 0 | 0 | 96.4s | $0.1857 |
Across all six runs and three tiny access canaries, the token-cost estimate was US$0.9081. The conservative allowance, including a 35% reserve for fees and foreign-exchange uncertainty and higher input pricing where metering might be incomplete, was US$1.2857. Both stayed below the approved combined US$5. These figures cover every generation request in this pilot; there were no generation retries or paid judges. They are not reconciled provider invoices.
A requirement counted as an observed regression only if an unchanged behavior check passed earlier and failed later. A rule that never worked would be an implementation miss, not evidence of forgetting. We also retired the original input-order check when the prompt replaced that rule with priority ordering. Adding the new audit field could fail the new output contract without proving that earlier budgeting behavior was forgotten.
After locking the final code, we asked each model to recall the requirements and explain three edge cases. In all six answers, the model correctly said that an archived first duplicate blocks later copies, a proposal exactly equal to the remaining budget is selected, and price overrides cost_cents. All identified the priority rule as replacing input-order traversal. These were manually reviewed diagnostic answers, not an exhaustive recall score or a reason to change the code results.
We used the native model IDs muse-wonder-1.0, gpt-6.1-sol and claude-sonnet-5-5, each with medium reasoning effort, a 4,096-output-token request ceiling and sampling settings left unset. Tools were disabled. There was one standard repair available per failing checkpoint, but none was used. Serial order rotated: Wonder, Claude, GPT in repetition one; GPT, Wonder, Claude in repetition two.
The original 40,000 cumulative input-token allowance stopped Claude's first run before the unsent recall question, after all its coding checkpoints had passed. We raised that allowance equally to 60,000 for every model and resumed only that recall. The prompts, code checks, output limits and combined US$5 budget were unchanged; no generation was repeated. The original stopped record remains in the evidence. Active time excludes the operator pause for this amendment; the evidence separately reports full wall-clock time.
Every model received the complete visible conversation. Claude also replayed its native signed thinking content, while GPT and Wonder did not replay hidden reasoning state. Native tokenizers, reasoning budgets and caching behavior differ, so medium does not mean equal compute. Claude requests originated from a different host to keep its credential there. The time column therefore describes these runs; it is not a controlled provider-latency ranking.
Wonder retained the tested requirements in this short task. GPT did too, at a lower estimated token cost in this setup. Claude also passed throughout. We cannot name a coding-quality winner from six perfect runs on one small project. Repeated edge-case checks are useful, but they do not become independent projects just because there are many of them.
This test did not exercise cross-session Active Memory, context compaction, hours-long Active Focus, agent tool use or Muse Code. Wonder Full was not tested: this was regular Wonder through the Model API. Nothing here independently validates Meta's long-duration benchmark claims. A harder follow-up would need longer projects, controlled memory conditions and several independent tasks.
Rates were checked against Meta pricing, OpenAI pricing and Claude Sonnet 5.5 documentation. The evidence package includes prompts, candidate code, raw response bodies, token records, stage scores, the amended protocol and the pinned isolated-worker checks. API credentials are excluded.