GPT-6.1 Terra vs Muse Wonder Full
Full profiles: GPT-6.1 Terra (OpenAI) and Muse Wonder Full (Meta): every benchmark, price and rank.
Updated · Data from 5 sources
As of 27 Oct 2026, GPT-6.1 Terra (max) and Muse Wonder Full (max) are neck and neck on quality (65.9 vs 70.3, inside the ±11.1 margin of error) and close on price, so pick on the category that matters to you. Pick GPT-6.1 Terra for knowledge (75 vs 71); pick Muse Wonder Full for reasoning (94 vs 86).
GPT-6.1 Terra benchmarks 
Muse Wonder Full 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 |
|---|---|---|---|
| Reasoning | Muse Wonder Full | 94 vs 86 category score; CritPt 32.9% vs 28.0% | High |
| Knowledge | GPT-6.1 Terra | 75 vs 71 category score; AA-Omniscience 29.8 vs 30.1 | Medium |
| Long documents | Either | 66 vs 67 category score; AA-LCR 82.7% vs 83.7% | Either |
| High volume on a budget | Neither flagship | A smaller model is usually the better fit: Cheapest good LLMs, Fastest LLMs | – |
Key differences
- GPT-6.1 Terra (max) and Muse Wonder Full (max) are neck and neck on quality (65.9 vs 70.3, within the ±11.1 margin).
- GPT-6.1 Terra (max) is stronger in knowledge, multimodal.
- Muse Wonder Full (max) is stronger in composite, long context, reasoning, coding.
At a glance
| Spec | GPT-6.1 Terra | Muse Wonder Full |
|---|---|---|
| Developer | OpenAI | Meta |
| Released | 26 Oct 2026 | 12 Oct 2026 |
| Context window (tokens) | 1,050,000 | 1,000,000 |
| Compared at | GPT-6.1 Terra (max) | Muse Wonder Full (max) |
| Open weights | No | No |
| Price in / out / cached input per 1M | $1.00 / $5.00 / $0.050 | – |
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 Terra (max) | Muse Wonder Full (max) | Leader |
|---|---|---|---|
| Reasoning | 86.2 | 94.1 | Muse Wonder Full |
| Coding | – | 72.0 | not comparable |
| Knowledge | 74.5 | 71.1 | GPT-6.1 Terra |
| Multimodal | 67.5 | – | not comparable |
| Long context | 66.3 | 66.8 | level |
| Composite | 82.8 | 91.3 | Muse Wonder Full |
Where sources disagree
Across the 13 benchmarks both have, GPT-6.1 Terra leads 2, Muse Wonder Full leads 11. Every overall index that rates both puts Muse Wonder Full ahead.
| Overall index | GPT-6.1 Terra (max) | Muse Wonder Full (max) | Leader |
|---|---|---|---|
| BenchLeader Index | 65.9 | 70.3 | neck and neck |
| AA Intelligence Index v4.3.2 | 48.1 | 55.8 | 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 | GPT-6.1 Terra (max) | Muse Wonder Full (max) |
|---|---|---|
| Blended price per 1M tokens | $2.00 | – |
| Input / output per 1M | $1.00 / $5.00 | – / – |
| Cached input per 1M | $0.050 | – |
| Price per task: chat reply | $0.0019 | – |
| Price per task: summarize a 30-page report | $0.015 | – |
| Price per task: code edit | $0.013 | – |
| Price per task: agentic coding session | $0.080 | – |
| Price per task: structured extraction | $0.0030 | – |
| Cost to run a full benchmark suite | $0.403 | – |
| Output speed | 91 tok/s | – |
| Time to first answer | 131.8 s | – |
| Time to a complete answer | 137.3 s | – |
Each model is compared at its best-ranked reasoning-effort setting. GPT-6.1 Terra (max) scores 65.9 and takes 132 s to its first answer token; GPT-6.1 Terra (xhigh) scores 65.0 in 18 s, GPT-6.1 Terra (high) scores 64.7 in 7 s. Muse Wonder Full (max) scores 70.3. 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
58 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 | GPT-6.1 Terra (max) | Muse Wonder Full (max) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 65.9 | 70.3 |
| Composite score | BenchLeader | 82.8 | 91.3 |
| Knowledge score | BenchLeader | 74.5 | 71.1 |
| Long context score | BenchLeader | 66.3 | 66.8 |
| Multimodal score | BenchLeader | 67.5 | – |
| Reasoning score | BenchLeader | 86.2 | 94.1 |
| Coding score | BenchLeader | – | 72.0 |
| Blended price $/M | BenchLeader | $2.00 | – |
| Output speed | BenchLeader | 91 tok/s | – |
| Time to first answer | BenchLeader | 131.8 s | – |
| Context window | BenchLeader | 1,050,000 tokens | 1,000,000 tokens |
| Reasoning | |||
| LiveBench 2.0 Reasoning | LiveBench | 88.7%† | – |
| Humanity's Last Exam (AA) | Artificial Analysis | 46.3% | 53.6% |
| CritPt | Artificial Analysis | 28.0% | 32.9% |
| MysteryMechanism | Vals AI | – | 35.1% |
| Coding | |||
| Arena WebDev | Arena (LMArena) | – | 1726 |
| LiveBench 2.0 Coding | LiveBench | 90.8%† | – |
| SciCode (AA) | Artificial Analysis | 55.1% | 62.6% |
| IOI | Vals AI | – | 79.8% |
| Code Migration | Vals AI | – | 61.0% |
| Code Migration v1.1 | Vals AI | – | 62.8% |
| ProgramBench | Vals AI | – | 0.5% |
| SWE-bench Multimodal | Vals AI | – | 47.3% |
| Vibe Code Bench 1-100 | Vals AI | – | 34.0% |
| Vibe Code Bench v1.1 | Vals AI | – | 86.0% |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 55.6% | 57.1% |
| FrontierCode 1.1 (AA) | Artificial Analysis | 36.0% | 44.9% |
| Agents & tools | |||
| LiveBench Long Agentic | LiveBench | 48.6%† | – |
| LiveBench Browser | LiveBench | 93.1%† | – |
| GDPval-AA v2.1 | Artificial Analysis | – | 67.8% |
| CyberBench | Vals AI | – | 73.6% |
| Finance Agent v2 | Vals AI | – | 59.1% |
| Finance Agent v2.1 | Vals AI | – | 67.6% |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | – | 23.3% |
| Harvey's Legal Agent Benchmark v1 | Vals AI | – | 21.7% |
| Legal Research Bench | Vals AI | – | 53.4% |
Show all 58 benchmarks
| Metric | Publisher | GPT-6.1 Terra (max) | Muse Wonder Full (max) |
|---|---|---|---|
| Agents & tools | |||
| Robo-bench | Vals AI | – | 10.4% |
| 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% |
| AutomationBench | Artificial Analysis | 60.2% | 71.3% |
| GDP.pdf (AA) | Artificial Analysis | 28.6% | 32.6% |
| Harvey LAB-AA v1.1 | Harvey | – | 13.1% |
| AA-Briefcase v1.1 | Artificial Analysis | 1402 | 1729 |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 57.6% | 37.1% |
| Knowledge | |||
| LiveBench 2.0 Data Analysis | LiveBench | 82.3%† | – |
| LiveBench Research | LiveBench | 73.9%† | – |
| AA-Omniscience | Artificial Analysis | 29.8 | 30.1 |
| LegalBench | Vals AI | – | 87.4% |
| BioMysteryBench | Vals AI | – | 73.3% |
| Excel Modeling Benchmark | Vals AI | – | 71.9% |
| Excel Modeling Benchmark v1.1 | Vals AI | – | 75.0% |
| MedCode | Vals AI | – | 49.9% |
| MedScribe | Vals AI | – | 89.3% |
| Public Benefits Bench v1.1 | Vals AI | – | 73.8% |
| AA-Omniscience: accuracy | Artificial Analysis | 49.1% | 43.2% |
| AA-Omniscience: non-hallucination | Artificial Analysis | 62.0% | 77.0% |
| Multimodal | |||
| LiveBench Design | LiveBench | 82.3%† | – |
| MMMU-Pro | Artificial Analysis | 84.7% | – |
| Long context | |||
| AA-LCR | Artificial Analysis | 82.7% | 83.7% |
| Composite | |||
| LiveBench 2.0 | LiveBench | 77.2%† | – |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 48.1 | 55.8 |
| Vals Index | Vals AI | – | 66.8 |
| Vals Finance Index | Vals AI | – | 64.5% |
| Vals RSI Index | Vals AI | – | 24.4% |
| Vals Science Index | Vals AI | – | 48.5% |
| Vals Router Index | Vals AI | – | 66.8%† |
| Safety & honesty | |||
| FORTRESS | Scale AI SEAL | 18.2%† | – |
Data as of 2026-10-27. Best configuration of each model; every score links to its source on the model pages.
What changed
- Muse Wonder Full (max): 1 new result (Terminal-Bench-Science 0.1 (AA)).
- Muse Wonder Full (max): 9 new results (Code Migration v1.1, Excel Modeling Benchmark v1.1, Finance Agent v2.1…).
- 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.
- Muse Wonder Full (max): 21 new results (IOI, LegalBench, Vals Index…).
- Muse Wonder Full (max): 1 new result (Harvey LAB-AA v1.1).
- Muse Wonder Full (max): first results.
Related comparisons
- GPT-6.1 Terra vs Claude Opus 5.5
- GPT-6.1 Terra vs Claude Fable 5.1
- GPT-6.1 Terra vs GPT-6 Astra
- Muse Wonder Full vs Claude Opus 5.5
- Muse Wonder Full vs Claude Fable 5.1
- Muse Wonder Full vs GPT-6 Astra
- Muse Wonder Full vs GPT-6.1 Sol
- GPT-6.1 Terra vs Muse Spark 1.3
Rankings: Best LLM overall · Best LLM for coding · Best LLM for agents · Best cheap LLMs
GPT-6.1 Terra vs Muse Wonder Full: questions
- Is GPT-6.1 Terra better than Muse Wonder Full?
- GPT-6.1 Terra (max) and Muse Wonder Full (max) are neck and neck on quality (65.9 vs 70.3, within the ±11.1 margin). The BenchLeader Index combines every independent quality benchmark; the two are within the margin of error as of 2026-10-27, so check the category scores for your use.
- Which is better for long documents?
- They are level on long-context benchmarks (66 vs 67). Both take about 1.1M tokens: GPT-6.1 Terra 1,050,000, Muse Wonder Full 1,000,000.
- Do independent benchmarks agree on GPT-6.1 Terra vs Muse Wonder Full?
- Across the 13 benchmarks both have, GPT-6.1 Terra leads 2, Muse Wonder Full leads 11. Every overall index that rates both puts Muse Wonder Full ahead.
- When was GPT-6.1 Terra released?
- GPT-6.1 Terra was released on 26 Oct 2026.