Muse Wonder Full vs o3
Full profiles: Muse Wonder Full (Meta) and o3 (OpenAI): every benchmark, price and rank.
Updated · Data from 14 sources
As of 21 Oct 2026, Muse Wonder Full (max) is the better pick for most workloads: it leads on quality (70.7 vs 59.5 on the BenchLeader Index). Muse Wonder Full leads in every category we measure both in.
Muse Wonder Full benchmarks 
o3 benchmarks Which to pick for…
The pick's numbers come first. Category scores on the BenchLeader scale (50 is the average model, 15 points one standard deviation). High confidence means a lead of half a standard deviation or more; under one point is called either.
| Use case | Pick | Why | Confidence |
|---|---|---|---|
| Coding | Muse Wonder Full | 72 vs 62 category score | High |
| Reasoning | Muse Wonder Full | 95 vs 52 category score; CritPt 32.9% vs 1.1% | High |
| Knowledge | Muse Wonder Full | 71 vs 55 category score; AA-Omniscience 30.1 vs -15.6 | High |
| Long documents | Muse Wonder Full | 67 vs 62 category score; AA-LCR 83.7% vs 74.7% | Medium |
| Chat quality (human preference) | – | Only o3 has an Arena rating (61 category score) | Not comparable |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- Muse Wonder Full (max) leads on quality: 70.7 vs 59.5.
- Muse Wonder Full (max) is stronger in coding, composite, knowledge, long context, reasoning.
- o3 is stronger in agents & tools, human preference, instruction following, maths, multimodal.
At a glance
| Spec | Muse Wonder Full | o3 |
|---|---|---|
| Developer | Meta | OpenAI |
| Released | 12 Oct 2026 | 16 Apr 2025 |
| Context window (tokens) | 1,000,000 | 200,000 |
| Compared at | Muse Wonder Full (max) | o3 |
| Open weights | No | No |
| Price in / out / cached input per 1M | – | $2.00 / $8.00 / $0.500 |
Category by category
Category scores on the BenchLeader scale: 50 is the average evaluated model, 15 points is one standard deviation. A gap under one point is called level.
| Category | Muse Wonder Full (max) | o3 | Leader |
|---|---|---|---|
| Reasoning | 95.0 | 51.9 | Muse Wonder Full |
| Coding | 72.1 | 61.8 | Muse Wonder Full |
| Agents & tools | – | 70.4 | not comparable |
| Maths | – | 70.6 | not comparable |
| Knowledge | 71.3 | 55.3 | Muse Wonder Full |
| Instruction following | – | 65.3 | not comparable |
| Human preference | – | 61.3 | not comparable |
| Multimodal | – | 56.9 | not comparable |
| Long context | 67.0 | 62.3 | Muse Wonder Full |
| Composite | 92.1 | 52.2 | Muse Wonder Full |
Where sources disagree
Across the 6 benchmarks both have, Muse Wonder Full leads 6, o3 leads 0. Every overall index that rates both puts Muse Wonder Full ahead.
| Overall index | Muse Wonder Full (max) | o3 | Leader |
|---|---|---|---|
| BenchLeader Index | 70.7 | 59.5 | Muse Wonder Full |
| AA Intelligence Index v4.3.2 | 55.8 | 20.2 | Muse Wonder Full |
Each publisher runs its own tasks, harnesses and effort settings, so their overall verdicts can differ; BenchLeader reconciles them on one scale. † measured at another effort setting of the same model (the publisher did not test the one compared here).
Price, speed and latency
List prices blended at three input tokens per output token. Per-task prices use the token counts on the methodology page; suite cost is what Artificial Analysis paid to run its whole Intelligence Index.
| Measure | Muse Wonder Full (max) | o3 |
|---|---|---|
| Blended price per 1M tokens | – | $3.50 |
| Input / output per 1M | – / – | $2.00 / $8.00 |
| Cached input per 1M | – | $0.500 |
| Price per task: chat reply | – | $0.0032 |
| Price per task: summarize a 30-page report | – | $0.029 |
| Price per task: code edit | – | $0.024 |
| Price per task: agentic coding session | – | $0.152 |
| Price per task: structured extraction | – | $0.0056 |
| Output speed | – | 130 tok/s |
| Time to first answer | – | 7.1 s |
| Time to a complete answer | – | 11.0 s |
Each model is compared at its best-ranked reasoning-effort setting. Muse Wonder Full (max) scores 70.7. o3 scores 59.5 and takes 7 s to its first answer token; o3 (high) scores 54.1 in 7 s, o3 (medium) scores 53.4 in 7 s. Higher effort means more thinking before the answer: better scores, longer waits and more tokens billed. How reasoning effort changes scores and cost.
Every benchmark
117 benchmarks, grouped by category; each name links to the board and its leaders. † measured at another effort setting of the same model, shown for reference and not counted as a win.
| Metric | Publisher | Muse Wonder Full (max) | o3 |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 70.7 | 59.5 |
| Coding score | BenchLeader | 72.1 | 61.8 |
| Composite score | BenchLeader | 92.1 | 52.2 |
| Knowledge score | BenchLeader | 71.3 | 55.3 |
| Long context score | BenchLeader | 67.0 | 62.3 |
| Reasoning score | BenchLeader | 95.0 | 51.9 |
| Agents & tools score | BenchLeader | – | 70.4 |
| Human preference score | BenchLeader | – | 61.3 |
| Instruction following score | BenchLeader | – | 65.3 |
| Maths score | BenchLeader | – | 70.6 |
| Multimodal score | BenchLeader | – | 56.9 |
| Blended price $/M | BenchLeader | – | $3.50 |
| Output speed | BenchLeader | – | 130 tok/s |
| Time to first answer | BenchLeader | – | 7.1 s |
| Context window | BenchLeader | 1,000,000 tokens | 200,000 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | – | 81.8%† |
| Humanity's Last Exam | Scale AI / CAIS | – | 20.3%† |
| SimpleBench | SimpleBench | – | 53.1%† |
| Arena Hard Prompts | Arena (LMArena) | – | 1442 |
| GPQA Diamond (AA) | Artificial Analysis | – | 82.7% |
| Humanity's Last Exam (AA) | Artificial Analysis | 53.6% | 20.1% |
| GPQA Diamond (Vals) | Vals AI | – | 84.1%† |
| Kagi LLM Benchmark | Kagi LLM Benchmark | – | 67.6% |
| GPQA Diamond (HELM) | HELM Capabilities | – | 75.3% |
| ARC-AGI-1 | ARC Prize | – | 60.8%† |
| ARC-AGI-2 | ARC Prize | – | 6.5%† |
| CritPt | Artificial Analysis | 32.9% | 1.1% |
| MysteryMechanism | Vals AI | 35.1% | – |
| SciPredict | Scale AI SEAL | – | 17.9% |
| Chess Puzzles | Epoch AI Benchmarking Hub | – | 36.0%† |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | – | 29.0%† |
| CL-bench | Epoch AI Benchmarking Hub | – | 17.8%† |
| LMCA | Epoch AI Benchmarking Hub | – | 39.7%† |
| DTBench | Epoch AI Benchmarking Hub | – | 84.8%† |
| ForecastBench | ForecastBench | – | 64.6% |
| Coding | |||
| SWE-bench Verified (Epoch) | Epoch AI Benchmarking Hub | – | 62.3%† |
| WeirdML | WeirdML | – | 52.4%† |
| GSO-Bench | GSO-Bench | – | 8.8%† |
| Arena Coding | Arena (LMArena) | – | 1460 |
| Arena WebDev | Arena (LMArena) | 1726 | – |
Show all 117 benchmarks
| Metric | Publisher | Muse Wonder Full (max) | o3 |
|---|---|---|---|
| Coding | |||
| SciCode (AA) | Artificial Analysis | 62.6% | – |
| LiveCodeBench | Vals AI | – | 83.9%† |
| IOI | Vals AI | 79.8% | – |
| Aider Polyglot | Aider polyglot leaderboard | – | 81.3% |
| SWE-bench Verified (bash only) | SWE-bench | – | 58.4% |
| SWE-bench Verified (any scaffold) | SWE-bench | – | 58.4% |
| Code Migration | Vals AI | 61.0% | – |
| ProgramBench | Vals AI | 0.5% | – |
| Vibe Code Bench 1-100 | Vals AI | 34.0% | – |
| Vibe Code Bench v1.1 | Vals AI | 86.0% | – |
| CadEval | Epoch AI Benchmarking Hub | – | 74.0%† |
| ALE-Bench | Epoch AI Benchmarking Hub | – | 933.5† |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 57.1% | – |
| FrontierCode 1.1 (AA) | Artificial Analysis | 44.9% | – |
| Agents & tools | |||
| GDPval | OpenAI | – | 30.8%† |
| Terminal-Bench Hard | Artificial Analysis | – | 37.1% |
| τ²-Bench Telecom (AA) | Artificial Analysis | – | 80.7% |
| BFCL Overall | Berkeley Function Calling Leaderboard | – | 63.0% |
| GDPval-AA v2.1 | Artificial Analysis | 67.8% | – |
| CyberBench | Vals AI | 73.6% | – |
| Finance Agent v2 | Vals AI | 59.1% | – |
| Harvey's Legal Agent Benchmark | Vals AI | 23.3% | – |
| Legal Research Bench | Vals AI | 53.4% | – |
| SREBench | Vals AI | 11.8% | – |
| Tax Agent Bench | Vals AI | 43.0% | – |
| Terminal-Bench 4.0 (Vals) | Vals AI | 58.1% | – |
| Terminal-Bench Science | Vals AI | 37.1% | – |
| DeepResearch Bench | DeepResearch Bench | – | 45.2%† |
| METR Time Horizons | METR | – | 63.6% |
| AutomationBench | Artificial Analysis | 71.3% | – |
| GDP.pdf (AA) | Artificial Analysis | 32.6% | – |
| Harvey LAB-AA v1.1 | Harvey | 13.1% | – |
| AA-Briefcase v1.1 | Artificial Analysis | 1729 | – |
| BrowseComp-Plus | BrowseComp-Plus | – | 50.5% |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | – | 33.3%† |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | – | 83.9%† |
| MATH Level 5 | Epoch AI Benchmarking Hub | – | 97.8%† |
| AIME (Vals) | Vals AI | – | 85.3%† |
| MGSM | Vals AI | – | 91.8%† |
| IMO 2025 | MathArena | – | 16.7%† |
| Omni-MATH (HELM) | HELM Capabilities | – | 71.4% |
| MATH 500 | Vals AI | – | 94.6%† |
| Arena Maths | Arena (LMArena) | – | 1448 |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | – | 49.4%† |
| AA-Omniscience | Artificial Analysis | 30.1 | -15.6 |
| MMLU-Pro | Vals AI | – | 85.6%† |
| LegalBench | Vals AI | 87.4% | 83.8%† |
| CorpFin | Vals AI | – | 59.7%† |
| TaxEval | Vals AI | – | 74.6%† |
| MedQA | Vals AI | – | 96.1%† |
| PRBench Finance | Scale AI SEAL | – | 47.7% |
| PRBench Legal | Scale AI SEAL | – | 48.6% |
| MultiNRC | Scale AI SEAL | – | 45.5%† |
| MMLU-Pro (HELM) | HELM Capabilities | – | 85.9% |
| BioMysteryBench | Vals AI | 73.3% | – |
| Excel Modeling Benchmark | Vals AI | 71.9% | – |
| MedCode | Vals AI | 49.9% | 47.3%† |
| MedScribe | Vals AI | 89.3% | 76.7%† |
| Public Benefits Bench | Vals AI | 73.8% | – |
| AA-Omniscience: accuracy | Artificial Analysis | 43.2% | 38.6% |
| AA-Omniscience: non-hallucination | Artificial Analysis | 77.0% | 11.9% |
| Instruction following | |||
| IFBench | Artificial Analysis | – | 71.4% |
| MultiChallenge | Scale AI SEAL | – | 56.6%† |
| TutorBench | Scale AI SEAL | – | 52.1%† |
| IFEval (HELM) | HELM Capabilities | – | 86.9% |
| WildBench (HELM) | HELM Capabilities | – | 86.1% |
| Arena Instruction Following | Arena (LMArena) | – | 1403 |
| Human preference | |||
| Arena Text | Arena (LMArena) | – | 1432 |
| Arena Creative Writing | Arena (LMArena) | – | 1383 |
| Arena Multi-turn | Arena (LMArena) | – | 1420 |
| Arena Longer Queries | Arena (LMArena) | – | 1411 |
| Lech Mazur Creative Writing | Lech Mazur | – | 83.9%† |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | – | 1214 |
| MMMU-Pro | Artificial Analysis | – | 70.1% |
| VISTA | Scale AI SEAL | – | 50.1%† |
| MMMU (validation) | MMMU | – | 82.9% |
| MMMU-Pro (official) | MMMU | – | 76.4% |
| MMMU-Pro (Vals) | Vals AI | – | 80.4%† |
| MortgageTax | Vals AI | – | 65.7%† |
| SAGE | Vals AI | – | 41.8%† |
| VTB | Scale AI SEAL | – | 13.7% |
| GeoBench | GeoBench | – | 60.0%† |
| VPCT | Epoch AI Benchmarking Hub | – | 52.0%† |
| Long context | |||
| Fiction.LiveBench 120k | Fiction.live | – | 100.0%† |
| AA-LCR | Artificial Analysis | 83.7% | 74.7% |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | – | 146.7 |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 55.8 | 20.2 |
| Vals Index | Vals AI | 66.8 | – |
| HELM Capabilities mean | HELM Capabilities | – | 81.1% |
| Safety & honesty | |||
| FORTRESS | Scale AI SEAL | – | 16.0% |
| MASK | Scale AI SEAL | – | 84.5%† |
| PropensityBench | Scale AI SEAL | – | 10.5% |
Data as of 2026-10-21. Best configuration of each model; every score links to its source on the model pages.
What changed
- Muse Wonder Full (max): 1 new result (FrontierCode 1.1 (AA)).
- Muse Wonder Full (max): BenchLeader Index 68.6 → 70.7.
- Muse Wonder Full (max): 1 new result (Arena WebDev); 2 score revisions.
- o3: 1 score revision.
- Muse Wonder Full (max): 21 new results (IOI, LegalBench, Vals Index…).
- Muse Wonder Full (max): 1 new result (Harvey LAB-AA v1.1).
- o3: 1 new result (PRBench Legal).
- Muse Wonder Full (max): first results.
- o3: 1 new result (VTB).
- o3: 1 new result (PRBench Legal).
Related comparisons
- Muse Wonder Full vs Claude Opus 5.5
- Muse Wonder Full vs Gemini 4 Argon
- Muse Wonder Full vs GPT-6 Astra
- o3 vs Claude Opus 5.5
- o3 vs Gemini 4 Argon
- o3 vs GPT-6 Astra
- Muse Wonder Full vs GPT-6.1 Sol
- Muse Wonder Full vs Claude Sonnet 5.5
Rankings: Best LLM overall · Best LLM for coding · Best LLM for agents · Best cheap LLMs
Muse Wonder Full vs o3: questions
- Is Muse Wonder Full better than o3?
- Muse Wonder Full (max) leads on quality: 70.7 vs 59.5. The BenchLeader Index combines every independent quality benchmark; Muse Wonder Full (max) is ahead overall as of 2026-10-21, but check the category scores for your use.
- Is Muse Wonder Full better than o3 for coding?
- Muse Wonder Full scores higher in coding (72 vs 62 on the category index, where 50 is average).
- Which is better for long documents?
- Muse Wonder Full scores higher on long-context benchmarks (67 vs 62). Muse Wonder Full accepts more context: 1,000,000 against 200,000 tokens for o3.
- Do independent benchmarks agree on Muse Wonder Full vs o3?
- Across the 6 benchmarks both have, Muse Wonder Full leads 6, o3 leads 0. Every overall index that rates both puts Muse Wonder Full ahead.
- When was Muse Wonder Full released?
- Muse Wonder Full was released on 12 Oct 2026.