GPT-6.1 Sol vs Muse Wonder
As of 13 Oct 2026, GPT-6.1 Sol (max) is the better pick for most workloads: it leads on quality (69.2 vs 66.3 on the BenchLeader Index) and costs 2.0× less.
GPT-6.1 Sol 
Muse Wonder Key differences
- GPT-6.1 Sol (max) leads on quality: 69.2 vs 66.3.
- GPT-6.1 Sol (max) is stronger in agents & tools, coding, composite, human preference, knowledge, long context, maths, multimodal.
- Muse Wonder (max) is stronger in reasoning.
- GPT-6.1 Sol (max) is 2.0× cheaper ($4.00 vs $8.00 per 1M blended).
- Muse Wonder (max) streams 1.3× faster (85 vs 65 tokens per second).
Choose GPT-6.1 Sol (max) if
- knowledge is the priority (79 vs 73 on the category score)
- long context is the priority (67 vs 66 on the category score)
- cost matters: $4.00 against $8.00 per million tokens blended
Choose Muse Wonder (max) if
- reasoning is the priority (88 vs 76 on the category score)
- you need fast output: 85 against 65 tokens per second
- the wait for the first word matters: 60.8 s against 339.4 s to first answer
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 | GPT-6.1 Sol (max) | Muse Wonder (max) | Leader |
|---|---|---|---|
| Reasoning | 76.3 | 87.8 | Muse Wonder |
| Coding | 72.4 | – | not comparable |
| Agents & tools | 63.9 | – | not comparable |
| Maths | 72.5 | – | not comparable |
| Knowledge | 78.7 | 73.0 | GPT-6.1 Sol |
| Human preference | 68.1 | – | not comparable |
| Multimodal | 66.2 | – | not comparable |
| Long context | 66.7 | 65.5 | GPT-6.1 Sol |
| Composite | 78.6 | 78.2 | level |
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 | GPT-6.1 Sol (max) | Muse Wonder (max) |
|---|---|---|
| Blended price per 1M tokens | $4.00 | $8.00 |
| Input / output per 1M | $2.00 / $10.00 | $4.00 / $20.00 |
| Price per task: chat reply | $0.0038 | $0.0076 |
| Price per task: summarise a 30-page report | $0.030 | $0.060 |
| Price per task: code edit | $0.027 | $0.054 |
| Price per task: agentic coding session | $0.160 | $0.320 |
| Price per task: structured extraction | $0.0060 | $0.012 |
| Cost to run a full benchmark suite | $0.724 | $4.52 |
| Output speed | 65 tok/s | 85 tok/s |
| Time to first answer | 339.4 s | 60.8 s |
| Time to a complete answer | 347.1 s | 66.7 s |
Every benchmark
| Metric | GPT-6.1 Sol (max) | Muse Wonder (max) |
|---|---|---|
| BenchLeader Index | 69.2 | 66.3 |
| Agents & tools score | 63.9 | – |
| Coding score | 72.4 | – |
| Composite score | 78.6 | 78.2 |
| Human preference score | 68.1 | – |
| Knowledge score | 78.7 | 73.0 |
| Long context score | 66.7 | 65.5 |
| Maths score | 72.5 | – |
| Multimodal score | 66.2 | – |
| Reasoning score | 76.3 | 87.8 |
| Blended price $/M | $4.00 | $8.00 |
| Output speed | 65 tok/s | 85 tok/s |
| Time to first answer | 339.4 s | 60.8 s |
| Context window | 1.1M | 1.0M |
| GPQA Diamond | 95.4% | – |
| FrontierMath Tiers 1–3 | 93.7% | – |
| FrontierMath Tier 4 | 100.0% | – |
| OTIS Mock AIME | 100.0% | – |
| SimpleQA Verified | 73.9% | – |
| Terminal-Bench | 58.2% | – |
| SciCode | 54.2% | – |
| APEX-Agents | 60.0% | – |
| LMArena Text | 1484 | – |
| LMArena Hard Prompts | 1507 | – |
| LMArena Coding | 1545 | – |
| LMArena WebDev | 1755 | – |
| LMArena Vision | 1288 | – |
| LMArena Agent | 11.7 | – |
| LiveBench | 81.6% | 81.2% |
| LiveBench Reasoning | 92.6% | 90.6% |
| LiveBench Coding | 80.4% | 80.7% |
| LiveBench Agentic Coding | 54.5% | 62.7% |
| LiveBench Mathematics | 96.8% | 96.5% |
| LiveBench Data Analysis | 82.7% | 81.6% |
| LiveBench Language | 90.1% | 82.9% |
| LiveBench Instruction Following | 74.2% | 73.0% |
| AA Intelligence Index v4.3.2 | 51.8 | 52.4 |
| AA-LCR | 83.0% | 80.7% |
| MMMU-Pro | 86.0% | – |
| AA-Omniscience | 41.5 | 25.5 |
| Humanity's Last Exam (AA) | 52.9% | 50.1% |
| SciCode (AA) | 54.2% | 60.3% |
| IOI | 96.9% | – |
| Vals Index | 61.1 | – |
| ARC-AGI-1 | 96.5% | – |
| ARC-AGI-2 | 94.2% | – |
| ARC-AGI-3 | 96.2% | – |
| CritPt | 31.7% | 28.3% |
| GDPval-AA v2.1 | 54.6% | 63.5% |
| Analyst Agent (AA) | 50.0% | – |
| BioMysteryBench | 79.6% | – |
| Code Migration | 65.1% | – |
| CyberBench | 39.3% | – |
| Excel Modeling Benchmark | 70.8% | – |
| Finance Agent v2 | 52.0% | – |
| Harvey's Legal Agent Benchmark | 5.4% | – |
| Legal Research Bench | 38.5% | – |
| MedCode | 48.8% | – |
| MedScribe | 86.5% | – |
| MysteryMechanism | 46.4% | – |
| Public Benefits Bench | 59.3% | – |
| SAGE | 46.5% | – |
| SREBench | 50.8% | – |
| Tax Agent Bench | 62.3% | – |
| Terminal-Bench 4.0 (Vals) | 55.0% | – |
| Vibe Code Bench v1.1 | 88.9% | – |
| LMArena Maths | 1488 | – |
| LMArena Creative Writing | 1462 | – |
| LMArena Instruction Following | 1488 | – |
| LMArena Multi-turn | 1487 | – |
| LMArena Longer Queries | 1496 | – |
| Chess Puzzles | 61.0% | – |
| EBR-bench | 54.3% | – |
| Mystery Game Puzzles | 80.0% | – |
| Terminal-Bench 4.0 (AA) | 56.1% | 52.0% |
| AutomationBench | 64.9% | 68.1% |
| GDP.pdf | 31.0% | 30.6% |
| MLCR | 33.9% | – |
| Harvey LAB-AA v1.1 | 6.9% | – |
| AA-Omniscience: accuracy | 62.1% | 41.1% |
| AA-Omniscience: non-hallucination | 45.7% | 73.5% |
| AA-Briefcase v1.1 | 1557 | 1659 |
| FrontierCode 1.1 (AA) | 49.1% | – |
| Terminal-Bench-Science 0.1 (AA) | 58.1% | – |
| AA-3D | 1114 | – |
Data as of 2026-10-13. Best configuration of each model; every score links to its source on the model pages.
GPT-6.1 Sol vs Muse Wonder: questions
- Is GPT-6.1 Sol better than Muse Wonder?
- GPT-6.1 Sol (max) leads on quality: 69.2 vs 66.3. The BenchLeader Index combines every independent quality benchmark; GPT-6.1 Sol (max) is ahead overall as of 2026-10-13, but check the category scores for your use.
- Which is cheaper, GPT-6.1 Sol or Muse Wonder?
- GPT-6.1 Sol is cheaper: $4.00 against $8.00 per million tokens, blended at three input tokens per output token.
- Which is faster, GPT-6.1 Sol or Muse Wonder?
- Muse Wonder streams faster: 85 against 65 output tokens per second.
- Which has the larger context window?
- GPT-6.1 Sol accepts more context: 1.1M against 1.0M tokens.