Muse Wonder vs Claude Opus 4.7
- Muse Wonder rates higher on LMArena’s text leaderboard: 1501 against 1490.
- Muse Wonder is cheaper: $4 / $20 per million input / output tokens, against $5 / $25 for Claude Opus 4.7.
- On the 3 Vals AI benchmarks both have taken, Muse Wonder wins 3 and Claude Opus 4.7 wins 0.
LatestClaude Opus 4.7 1490
Side by side
Models (2/6)
- Muse Wonder
- Claude Opus 4.7
| Benchmark | Muse Wonder | Claude Opus 4.7 |
|---|---|---|
| The model | ||
| Lab | Meta | Anthropic |
| Released | 2026-10-12 | 2026-04-16 |
| Weights | Proprietary | Proprietary |
| Context window | 1M | 1M |
| Input, per 1M tokens | $4 | $5 |
| Output, per 1M tokens | $20 | $25 |
| LMArena, people’s votes | ||
| LMArena TextMillions of people compare two anonymous models side by side and vote for the better answer | 1501#7 of 351 | 1490#13 of 351 |
| LMArena WebDevPeople ask two anonymous models to build the same web app, try both and vote for the better one | 1651#18 of 124 | 1558#41 of 124 |
| LMArena VisionSide-by-side votes on prompts that include an image | — | 1317#5 of 146 |
| LMArena CodingLMArena’s text votes on prompts that ask for code | 1518#11 of 346 | 1518#9 of 346 |
| LMArena MathLMArena’s text votes on math prompts | 1484#30 of 339 | 1498#17 of 339 |
| LMArena Creative writingLMArena’s text votes on stories, poems and other creative writing | 1493#7 of 349 | 1486#11 of 349 |
| LMArena Hard promptsLMArena’s text votes on the hardest prompts | 1505#12 of 351 | 1506#11 of 351 |
| LMArena Instruction followingLMArena’s text votes on prompts with specific instructions to follow | 1493#10 of 351 | 1498#9 of 351 |
| Vals AI, overall | ||
| Vals IndexVals AI’s headline score | 64.88%#6 of 27 | — |
| Vals AI, coding | ||
| Terminal-Bench 4.0Frontier-difficulty tasks an agent completes in a real terminal | 49.49%#10 of 55 | — |
| SWE-benchReal GitHub issues from open-source projects | — | 82.00%#21 of 85 |
| Vibe Code BenchBuilding complete web apps from a plain-language brief, graded on whether the app works | 83.09%#20 of 115 | 71.00%#41 of 115 |
| Code MigrationRewriting working programs in a new language, graded on whether the new program still passes its tests | 72.36%#5 of 28 | — |
| LiveCodeBenchFresh competitive programming problems, collected after models’ training cut-offs to avoid contamination | — | 85.07%#36 of 143 |
| Vals AI, finance | ||
| Finance AgentThe core tasks of a financial analyst, done as an agent with research tools | 65.55%#6 of 31 | — |
| Finance IndexVals AI’s composite of finance tasks | 61.23%#6 of 31 | — |
| Tax Agent BenchAgentic research into US corporate tax questions | 36.27%#12 of 76 | 23.74%#43 of 76 |
| Vals AI, law | ||
| Legal IndexVals AI’s composite of legal tasks | 75.26%#3 of 28 | — |
| Legal Research BenchAgentic US legal research | 50.48%#6 of 85 | 38.46%#35 of 85 |
| Vals AI, medicine | ||
| MedCodeAssigning the right medical billing codes from clinical notes | — | 54.86%#7 of 107 |
| Vals AI, reasoning | ||
| GPQA DiamondGraduate-level questions in biology, physics and chemistry that experts answer well and search does not | — | 90.15%#26 of 138 |
| MMLU-ProHarder, reasoning-focused multiple choice across 14 subjects, from law to engineering | — | 89.87%#8 of 138 |
| AIMEProblems from the American Invitational Mathematics Examination, a qualifier for the US Math Olympiad | — | 96.25%#7 of 96 |
| MMMUCollege-level questions that need both an image and text | — | 85.55%#24 of 93 |
| Terminal-Bench ScienceExpert-written scientific research workflows, run by an agent in a terminal | 22.86%#10 of 42 | — |
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Questions
Which is better, Muse Wonder or Claude Opus 4.7?
Muse Wonder rates higher on LMArena’s text leaderboard: 1501 against 1490. Muse Wonder is cheaper: $4 / $20 per million input / output tokens, against $5 / $25 for Claude Opus 4.7. On the 3 Vals AI benchmarks both have taken, Muse Wonder wins 3 and Claude Opus 4.7 wins 0.
Which is cheaper, Muse Wonder or Claude Opus 4.7?
Muse Wonder is cheaper: $4 / $20 per million input / output tokens, against $5 / $25 for Claude Opus 4.7.
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