GPT-5.5 vs Muse Wonder Full
As of 18 Oct 2026, Muse Wonder Full (max) is the better pick for most workloads: it leads on quality (68.6 vs 65.7 on the BenchLeader Index).
GPT-5.5 
Muse Wonder Full Key differences
- Muse Wonder Full (max) leads on quality: 68.6 vs 65.7.
- GPT-5.5 (xhigh) is stronger in agents & tools, instruction following, long context, maths, multimodal.
- Muse Wonder Full (max) is stronger in coding, composite, knowledge, reasoning.
Choose GPT-5.5 (xhigh) if
It does not lead on anything measured here; the other is the safer default.
Choose Muse Wonder Full (max) if
- reasoning is the priority (95 vs 75 on the category score)
- knowledge is the priority (71 vs 65 on the category score)
- coding is the priority (64 vs 61 on the category score)
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-5.5 (xhigh) | Muse Wonder Full (max) | Leader |
|---|---|---|---|
| Reasoning | 75.4 | 95.0 | Muse Wonder Full |
| Coding | 61.2 | 64.0 | Muse Wonder Full |
| Agents & tools | 61.1 | – | not comparable |
| Maths | 69.7 | – | not comparable |
| Knowledge | 65.3 | 71.3 | Muse Wonder Full |
| Instruction following | 73.8 | – | not comparable |
| Multimodal | 63.0 | – | not comparable |
| Long context | 67.3 | 67.0 | level |
| Composite | 68.8 | 92.2 | Muse Wonder Full |
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-5.5 (xhigh) | Muse Wonder Full (max) |
|---|---|---|
| Blended price per 1M tokens | $11.25 | – |
| Input / output per 1M | $5.00 / $30.00 | – / – |
| Price per task: chat reply | $0.011 | – |
| Price per task: summarise a 30-page report | $0.078 | – |
| Price per task: code edit | $0.075 | – |
| Price per task: agentic coding session | $0.420 | – |
| Price per task: structured extraction | $0.016 | – |
| Cost to run a full benchmark suite | $2.63 | – |
| Output speed | 101 tok/s | – |
| Time to first answer | 53.2 s | – |
| Time to a complete answer | 58.1 s | – |
Every benchmark
| Metric | GPT-5.5 (xhigh) | Muse Wonder Full (max) |
|---|---|---|
| BenchLeader Index | 65.7 | 68.6 |
| Agents & tools score | 61.1 | – |
| Coding score | 61.2 | 64.0 |
| Composite score | 68.8 | 92.2 |
| Instruction following score | 73.8 | – |
| Knowledge score | 65.3 | 71.3 |
| Long context score | 67.3 | 67.0 |
| Maths score | 69.7 | – |
| Multimodal score | 63.0 | – |
| Reasoning score | 75.4 | 95.0 |
| Blended price $/M | $11.25 | – |
| Output speed | 101 tok/s | – |
| Time to first answer | 53.2 s | – |
| Context window | 1.1M | 1M |
| FrontierMath Tiers 1–3 | 85.3% | – |
| FrontierMath Tier 4 | 72.5% | – |
| SimpleQA Verified | 63.0% | – |
| OSWorld-Verified 2.0 | 13.0% | – |
| SciCode | 55.8% | – |
| WeirdML | 84.9% | – |
| ProofBench | 50.0% | – |
| GSO-Bench | 40.2% | – |
| Arena WebDev | 1513 | – |
| Arena Agent | 3.1 | – |
| LiveBench | 80.2% | – |
| LiveBench Reasoning | 89.7% | – |
| LiveBench Coding | 82.2% | – |
| LiveBench Agentic Coding | 54.0% | – |
| LiveBench Mathematics | 95.9% | – |
| LiveBench Data Analysis | 81.6% | – |
| LiveBench Language | 87.4% | – |
| LiveBench Instruction Following | 70.7% | – |
| AA Intelligence Index v4.3.2 | 38.4 | 55.8 |
| IFBench | 75.8% | – |
| AA-LCR | 84.3% | 83.7% |
| MMMU-Pro | 79.9% | – |
| AA-Omniscience | 20.5 | 30.1 |
| Terminal-Bench Hard | 60.6% | – |
| GPQA Diamond (AA) | 93.5% | – |
| Humanity's Last Exam (AA) | 45.8% | 53.6% |
| SciCode (AA) | 55.8% | 62.6% |
| τ²-Bench Telecom (AA) | 93.9% | – |
| LiveCodeBench | 85.3% | – |
| MMLU-Pro | 88.1% | – |
| IOI | – | 79.8% |
| LegalBench | 86.5% | 87.4% |
| CorpFin | 68.4% | – |
| TaxEval | 75.0% | – |
| SWE-bench (Vals) | 82.6% | – |
| GPQA Diamond (Vals) | 93.2% | – |
| Vals Index | – | 64.6 |
| AIME 2026 | 100.0% | – |
| HMMT February 2026 | 98.5% | – |
| USAMO 2026 | 98.2% | – |
| MathArena Apex | 80.2% | – |
| MCP Atlas | 75.3% | – |
| ARC-AGI-1 | 95.0% | – |
| ARC-AGI-2 | 85.0% | – |
| τ³-bench Banking | 44.6% | – |
| CritPt | 27.1% | 32.9% |
| GDPval-AA v2.1 | 42.5% | 67.8% |
| τ³-Banking (AA) | 39.0% | – |
| ITBench SRE (AA) | 45.8% | – |
| Analyst Agent (AA) | 50.0% | – |
| APEX-Agents (AA) | 37.7% | – |
| BioMysteryBench | – | 73.3% |
| CaseLaw v2 | 66.2% | – |
| Code Migration | 45.2% | 61.0% |
| CyberBench | – | 73.6% |
| Excel Modeling Benchmark | 64.5% | 71.9% |
| Finance Agent v2 | 51.8% | 59.1% |
| Harvey's Legal Agent Benchmark | 3.8% | 21.7% |
| Legal Research Bench | 40.4% | 53.4% |
| MedCode | 49.1% | 49.9% |
| MedScribe | 86.9% | 89.3% |
| MMMU-Pro (Vals) | 88.3% | – |
| MortgageTax | 68.8% | – |
| MysteryMechanism | – | 35.1% |
| ProgramBench | 0.5% | 0.5% |
| Public Benefits Bench | 60.9% | 73.8% |
| SAGE | 51.5% | – |
| SkillsBench | 62.2% | – |
| SREBench | 3.8% | 11.8% |
| Tax Agent Bench | 21.8% | 43.0% |
| Terminal-Bench 4.0 (Vals) | – | 58.1% |
| Terminal-Bench Science | – | 37.1% |
| Time Horizon Index: KSP | 8.3% | – |
| Vals Multimodal Index | 68.1% | – |
| Vibe Code Bench 1-100 | – | 34.0% |
| Vibe Code Bench v1.1 | 69.8% | 86.0% |
| DrugDiscoveryBench | 51.6% | – |
| FORTRESS | 16.3% | – |
| SWE Atlas: Codebase QnA | 45.4% | – |
| SWE Atlas: Refactoring | 44.8% | – |
| SWE Atlas: Test Writing | 42.6% | – |
| Chess Puzzles | 63.0% | – |
| EBR-bench | 34.3% | – |
| Mystery Game Puzzles | 56.0% | – |
| PostTrainBench | 27.2% | – |
| DeepSWE v1.1 | 67.0% | – |
| LMCA | 54.3% | – |
| DTBench | 96.0% | – |
| ALE-Bench | 1943.0 | – |
| GDP.pdf | 26.0% | – |
| Terminal-Bench 4.0 (AA) | 14.7% | 57.1% |
| Terminal-Bench 2.1 (AA) | 84.3% | – |
| AutomationBench | 47.3% | 71.3% |
| GDP.pdf | 21.2% | 32.6% |
| MLCR | 17.8% | – |
| Harvey LAB-AA v1.1 | – | 13.1% |
| EnterpriseOps-Gym | 46.6% | – |
| AA-Omniscience: accuracy | 58.0% | 43.2% |
| AA-Omniscience: non-hallucination | 11.0% | 77.0% |
| AA-Briefcase v1.1 | – | 1729 |
Data as of 2026-10-18. Best configuration of each model; every score links to its source on the model pages.
GPT-5.5 vs Muse Wonder Full: questions
- Is GPT-5.5 better than Muse Wonder Full?
- Muse Wonder Full (max) leads on quality: 68.6 vs 65.7. The BenchLeader Index combines every independent quality benchmark; Muse Wonder Full (max) is ahead overall as of 2026-10-18, but check the category scores for your use.
- Is GPT-5.5 better than Muse Wonder Full for coding?
- Muse Wonder Full scores higher in coding (64 vs 61 on the category index, where 50 is average).
- Which has the larger context window?
- GPT-5.5 accepts more context: 1.1M against 1M tokens.