GPT-6.1 Sol vs Muse Spark Open 816B A42B
Full profiles: GPT-6.1 Sol (OpenAI) and Muse Spark Open 816B A42B (Meta): every benchmark, price and rank.
Updated · Data from 9 sources
As of 29 Oct 2026, GPT-6.1 Sol is ahead of Muse Spark Open 816B A42B on quality (67.2 vs 58.5 on the BenchLeader Index), but Muse Spark Open 816B A42B is 2.0× cheaper at $2.00 vs $4.00 per 1M tokens blended. GPT-6.1 Sol leads in every category we measure both in.
Key facts (as of 29 Oct 2026)
- GPT-6.1 Sol scores 67.2 on the BenchLeader Index and Muse Spark Open 816B A42B scores 58.5 (BenchLeader, 29 Oct 2026).
- GPT-6.1 Sol costs $4.00 per 1M tokens blended (list price, 29 Oct 2026).
- Muse Spark Open 816B A42B costs $2.00 per 1M tokens blended (list price, 29 Oct 2026).
- Of the 7 benchmarks both models have, GPT-6.1 Sol leads 6 (BenchLeader, 29 Oct 2026).
GPT-6.1 Sol benchmarks max OpenAI
Muse Spark Open 816B A42B benchmarks xhigh MetaOpen in the comparison toolWhich 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 | GPT-6.1 Sol | 72 vs 66 category score; SciCode 54.2% vs 58.5% | Medium |
| Maths | GPT-6.1 Sol | 73 vs 59 category score; FrontierMath Tiers 1–3 93.7% vs 69.8% | High |
| Reasoning | GPT-6.1 Sol | 73 vs 64 category score; GPQA Diamond 95.4% vs 88.6% | High |
| Knowledge | GPT-6.1 Sol | 73 vs 62 category score; SimpleQA Verified 73.9% vs 56.6% | High |
| Chat quality (human preference) | – | Only GPT-6.1 Sol has an Arena rating (67 category score) | Not comparable |
| Lowest cost | Muse Spark Open 816B A42B | $2.00 vs $4.00 per 1M blended; agentic coding session $0.092 vs $0.160 | – |
| 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 Sol (max) leads on quality: 67.2 vs 58.5.
- GPT-6.1 Sol (max) is stronger in agents & tools, coding, human preference, knowledge, long context, maths, multimodal, reasoning.
- Muse Spark Open 816B A42B (xhigh) is 2.0× cheaper ($2.00 vs $4.00 per 1M blended).
At a glance
| Spec | GPT-6.1 Sol | Muse Spark Open 816B A42B |
|---|---|---|
| Developer | OpenAI | Meta |
| Released | 29 Sept 2026 | 15 Oct 2026 |
| Context window (tokens) | 1,050,000 | 1,048,576 |
| Compared at | GPT-6.1 Sol (max) | Muse Spark Open 816B A42B (xhigh) |
| Open weights | No | Yes |
| Price in / out / cached input per 1M | $2.00 / $10.00 / $0.100 | $1.25 / $4.25 / $0.150 |
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 Spark Open 816B A42B (xhigh) | Leader |
|---|---|---|---|
| Reasoning | 73.2 | 63.6 | GPT-6.1 Sol |
| Coding | 71.6 | 65.5 | GPT-6.1 Sol |
| Agents & tools | 64.4 | – | not comparable |
| Maths | 72.8 | 59.4 | GPT-6.1 Sol |
| Knowledge | 73.0 | 62.2 | GPT-6.1 Sol |
| Human preference | 67.1 | – | not comparable |
| Multimodal | 65.9 | – | not comparable |
| Long context | 66.4 | – | not comparable |
Where sources disagree
Across the 7 benchmarks both have, GPT-6.1 Sol leads 6, Muse Spark Open 816B A42B leads 1. Every overall index that rates both puts GPT-6.1 Sol ahead.
| Overall index | GPT-6.1 Sol (max) | Muse Spark Open 816B A42B (xhigh) | Leader |
|---|---|---|---|
| BenchLeader Index | 67.2 | 58.5 | GPT-6.1 Sol |
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 Sol (max) | Muse Spark Open 816B A42B (xhigh) |
|---|---|---|
| Blended price per 1M tokens | $4.00 | $2.00 |
| Input / output per 1M | $2.00 / $10.00 | $1.25 / $4.25 |
| Cached input per 1M | $0.100 | $0.150 |
| Price per task: chat reply | $0.0038 | $0.0018 |
| Price per task: summarize a 30-page report | $0.030 | $0.018 |
| Price per task: code edit | $0.027 | $0.014 |
| Price per task: agentic coding session | $0.160 | $0.092 |
| Price per task: structured extraction | $0.0060 | $0.0034 |
| Cost to run a full benchmark suite | $1.27 | – |
| Output speed | 79 tok/s | – |
| Time to first answer | 249.5 s | – |
| Time to a complete answer | 255.8 s | – |
Each model is compared at its best-ranked reasoning-effort setting. GPT-6.1 Sol (max) scores 67.2 and takes 249 s to its first answer token; GPT-6.1 Sol (xhigh) scores 64.3 in 97 s, GPT-6.1 Sol (high) scores 64.0 in 28 s. Muse Spark Open 816B A42B (xhigh) scores 58.5. 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
115 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 Sol (max) | Muse Spark Open 816B A42B (xhigh) |
|---|---|---|---|
| BenchLeader Index | BenchLeader | 67.2 | 58.5 |
| Agents & tools score | BenchLeader | 64.4 | – |
| Coding score | BenchLeader | 71.6 | 65.5 |
| Human preference score | BenchLeader | 67.1 | – |
| Knowledge score | BenchLeader | 73.0 | 62.2 |
| Long context score | BenchLeader | 66.4 | – |
| Maths score | BenchLeader | 72.8 | 59.4 |
| Multimodal score | BenchLeader | 65.9 | – |
| Reasoning score | BenchLeader | 73.2 | 63.6 |
| Blended price $/M | BenchLeader | $4.00 | $2.00 |
| Output speed | BenchLeader | 79 tok/s | – |
| Time to first answer | BenchLeader | 249.5 s | – |
| Context window | BenchLeader | 1,050,000 tokens | 1,048,576 tokens |
| Reasoning | |||
| GPQA Diamond | Epoch AI Benchmarking Hub | 95.4% | 88.6% |
| Arena Hard Prompts | Arena (LMArena) | 1504 | – |
| LiveBench 2.0 Reasoning | LiveBench | 92.6%† | – |
| LiveBench 1.0 Reasoning | LiveBench | 92.6% | – |
| Humanity's Last Exam (AA) | Artificial Analysis | 52.9% | – |
| HLE-Diamond (AA) | Artificial Analysis | 58.8% | – |
| ARC-AGI-1 | ARC Prize | 96.5% | – |
| ARC-AGI-2 | ARC Prize | 94.2% | – |
| ARC-AGI-3 | ARC Prize | 96.2% | – |
| CritPt | Artificial Analysis | 31.7% | – |
| CritPt v2 hard subset (AA) | Artificial Analysis | 68.5% | – |
| MysteryMechanism | Vals AI | 46.4% | – |
| Chess Puzzles | Epoch AI Benchmarking Hub | 68.0% | – |
| Mystery Game Puzzles | Epoch AI Benchmarking Hub | 80.0% | – |
| LMCA | Epoch AI Benchmarking Hub | 62.8% | – |
| DTBench | Epoch AI Benchmarking Hub | 96.5% | – |
| Coding | |||
| SciCode | SciCode | 54.2% | 58.5% |
| FrontierCode | Cognition | 50.2%† | – |
| Arena Coding | Arena (LMArena) | 1537 | – |
| Arena WebDev | Arena (LMArena) | 1752 | – |
| LiveBench 2.0 Coding | LiveBench | 96.0%† | – |
| LiveBench 1.0 Coding | LiveBench | 80.4% | – |
| SciCode (AA) | Artificial Analysis | 54.2% | – |
| IOI | Vals AI | 96.9% | – |
| Code Migration | Vals AI | 65.1% | – |
Show all 115 benchmarks
| Metric | Publisher | GPT-6.1 Sol (max) | Muse Spark Open 816B A42B (xhigh) |
|---|---|---|---|
| Coding | |||
| Code Migration v1.1 | Vals AI | 77.3% | – |
| ProgramBench | Vals AI | 3.0% | – |
| SWE-bench Multimodal | Vals AI | 43.5% | – |
| Vibe Code Bench v1.1 | Vals AI | 88.9% | – |
| ALE-Bench | Epoch AI Benchmarking Hub | 2743.8 | 1590.1 |
| Terminal-Bench 4.0 (AA) | Artificial Analysis | 56.1% | – |
| Startrise Frontend | Startrise | 78.6%† | – |
| FrontierCode 1.1 (AA) | Artificial Analysis | 49.1% | – |
| FrontierCode 2.0 (AA) | Artificial Analysis | 40.6% | – |
| Agents & tools | |||
| Terminal-Bench | Terminal-Bench | 58.2% | – |
| APEX-Agents | Mercor | 60.0% | 33.4%† |
| Arena Agent | Arena (LMArena) | 13.6 | – |
| LiveBench Long Agentic | LiveBench | 68.7%† | – |
| LiveBench Browser | LiveBench | 93.3%† | – |
| LiveBench Agentic Coding | LiveBench | 54.5% | – |
| HiL-Bench | Scale AI SEAL | 62.3%† | – |
| GDPval-AA v2.1, normalised score | Artificial Analysis | 54.8% | – |
| GDPval-AA v2.1 | Artificial Analysis | 1608 | – |
| Analyst Agent (AA) | Artificial Analysis | 50.0% | – |
| CyberBench | Vals AI | 39.3% | – |
| Finance Agent v2 | Vals AI | 52.0% | – |
| Finance Agent v2.1 | Vals AI | 61.8% | – |
| Harvey's Legal Agent Benchmark v1.1 | Vals AI | 6.3% | – |
| Harvey's Legal Agent Benchmark v1 | Vals AI | 5.4% | – |
| Legal Research Bench | Vals AI | 38.5% | – |
| Robo-bench | Vals AI | 28.3% | – |
| SREBench | Vals AI | 50.8% | – |
| Tax Agent Bench | Vals AI | 21.8% | – |
| Terminal-Bench 4.0 (Vals) | Vals AI | 55.0% | – |
| Terminal-Bench Science | Vals AI | 52.9% | – |
| Arena Search | Arena (LMArena) | 1250 | – |
| AutomationBench | Artificial Analysis | 64.9% | – |
| GDP.pdf (AA) | Artificial Analysis | 31.0% | – |
| Harvey LAB-AA v1.1 | Harvey | 6.9% | – |
| AA-Briefcase v1.1 | Artificial Analysis | 1555 | – |
| Terminal-Bench-Science 0.1 (AA) | Artificial Analysis | 58.1% | – |
| FACTS Search | 88.2%† | – | |
| Maths | |||
| FrontierMath Tiers 1–3 | Epoch AI Benchmarking Hub | 93.7% | 69.8% |
| FrontierMath Tier 4 | Epoch AI Benchmarking Hub | 100.0% | 31.7% |
| OTIS Mock AIME | Epoch AI Benchmarking Hub | 100.0% | 98.1% |
| ProofBench | Vals AI | 99.0%† | – |
| LiveBench Mathematics | LiveBench | 96.8% | – |
| Arena Maths | Arena (LMArena) | 1513 | – |
| Knowledge | |||
| SimpleQA Verified | Epoch AI Benchmarking Hub | 73.9% | 56.6% |
| LiveBench 2.0 Data Analysis | LiveBench | 82.7%† | – |
| LiveBench Research | LiveBench | 83.5%† | – |
| LiveBench 1.0 Data Analysis | LiveBench | 82.7% | – |
| AA-Omniscience v1.1 | Artificial Analysis | 42.0 | – |
| PRBench Finance | Scale AI SEAL | 46.2%† | – |
| PRBench Legal | Scale AI SEAL | 47.9%† | – |
| BioMysteryBench | Vals AI | 79.6% | – |
| ClaimBench | Vals AI | 100.0% | – |
| Excel Modeling Benchmark | Vals AI | 70.8% | – |
| Excel Modeling Benchmark v1.1 | Vals AI | 71.1% | – |
| MedCode | Vals AI | 48.8% | – |
| MedScribe | Vals AI | 86.5% | – |
| Public Benefits Bench v1.1 | Vals AI | 59.3% | – |
| EBR-bench | Epoch AI Benchmarking Hub | 54.3% | – |
| GDP.pdf | Epoch AI Benchmarking Hub | 35.0% | – |
| AA-Omniscience v1.1: accuracy | Artificial Analysis | 61.8% | – |
| AA-Omniscience v1.1: non-hallucination | Artificial Analysis | 48.1% | – |
| HealthBench Professional (AA) | OpenAI | 65.1% | – |
| Instruction following | |||
| LiveBench Language | LiveBench | 90.1% | – |
| LiveBench Instruction Following | LiveBench | 74.2% | – |
| MultiChallenge | Scale AI SEAL | 82.0%† | – |
| Arena Instruction Following | Arena (LMArena) | 1485 | – |
| Human preference | |||
| Arena Text | Arena (LMArena) | 1480 | – |
| EQ-Bench 4 | EQ-Bench | 1272† | – |
| Arena Creative Writing | Arena (LMArena) | 1453 | – |
| Arena Multi-turn | Arena (LMArena) | 1483 | – |
| Arena Longer Queries | Arena (LMArena) | 1489 | – |
| Multimodal | |||
| Arena Vision | Arena (LMArena) | 1288 | – |
| LiveBench Design | LiveBench | 82.1%† | – |
| MMMU-Pro | Artificial Analysis | 86.0% | – |
| SAGE | Vals AI | 90.5% | – |
| Code2Video-AA | Artificial Analysis | 1369 | – |
| AA-3D | Artificial Analysis | 1119 | – |
| Long context | |||
| AA-LCR v1.1 | Artificial Analysis | 83.0% | – |
| MLCR | Artificial Analysis | 38.3% | – |
| Composite | |||
| Epoch Capabilities Index | Epoch AI Benchmarking Hub | 168.4† | – |
| LiveBench 2.0 | LiveBench | 84.1%† | – |
| LiveBench 1.0 | LiveBench | 81.6% | – |
| AA Intelligence Index v5.0 | Artificial Analysis | 54.5 | – |
| AA Intelligence Index v4.3.2 | Artificial Analysis | 51.8 | – |
| AA Progress Index v1 | Artificial Analysis | 348.5 | – |
| Vals Index | Vals AI | 63.9 | – |
| Vals Finance Index | Vals AI | 55.8% | – |
| Vals RSI Index | Vals AI | 26.6% | – |
| Vals Science Index | Vals AI | 59.6% | – |
| Vals Router Index | Vals AI | 63.9%† | – |
Data as of 2026-10-29. Best configuration of each model; every score links to its source on the model pages.
What changed
- GPT-6.1 Sol (max): 5 new results (AA Intelligence Index v5.0, AA Progress Index v1, HLE-Diamond (AA)…); 3 score revisions; cost per task moved from $0.72/M to $1.27/M (76% up).
- GPT-6.1 Sol (max): 10 new results (ClaimBench, Code Migration v1.1, Excel Modeling Benchmark v1.1…).
- GPT-6.1 Sol (max): BenchLeader Index 67.9 → 67.4.
- GPT-6.1 Sol (max): 5 score revisions.
- GPT-6.1 Sol (max): 2 score revisions.
- Muse Spark Open 816B A42B (xhigh): BenchLeader Index 57.4 → 58.6.
- GPT-6.1 Sol (max): 2 new results (LMCA, DTBench).
- Muse Spark Open 816B A42B (xhigh): 2 new results (SciCode, ALE-Bench).
- GPT-6.1 Sol (max): 2 score revisions.
- Muse Spark Open 816B A42B (xhigh): first results.
Related comparisons
- GPT-6.1 Sol vs Claude Opus 5.5
- GPT-6.1 Sol vs GPT-6 Astra
- GPT-6.1 Sol vs Claude Fable 5.1
- Muse Spark Open 816B A42B vs Claude Opus 5.5
- Muse Spark Open 816B A42B vs GPT-6 Astra
- Muse Spark Open 816B A42B vs Claude Fable 5.1
- GPT-6.1 Sol vs Muse Wonder
- GPT-6.1 Sol vs Gemini 4 Argon
Rankings: Best LLM overall · Best LLM for coding · Best LLM for agents · Best cheap LLMs
GPT-6.1 Sol vs Muse Spark Open 816B A42B: questions
- Is GPT-6.1 Sol better than Muse Spark Open 816B A42B?
- GPT-6.1 Sol (max) leads on quality: 67.2 vs 58.5. The BenchLeader Index combines every independent quality benchmark; GPT-6.1 Sol (max) is ahead overall as of 29 Oct 2026, but check the category scores for your use.
- GPT-6.1 Sol vs Muse Spark Open 816B A42B: which is cheaper, and will that change?
- Muse Spark Open 816B A42B is cheaper today: $2.00 against $4.00 per million tokens, blended at three input tokens per output token. No price change has been announced for either.
- Is GPT-6.1 Sol or Muse Spark Open 816B A42B better for coding?
- GPT-6.1 Sol scores higher in coding (72 vs 66 on the category index, where 50 is average). On SciCode, the top shared coding benchmark, it is 54.2% for GPT-6.1 Sol against 58.5% for Muse Spark Open 816B A42B.
- Which is better for long documents?
- Both take about 1.1M tokens: GPT-6.1 Sol 1,050,000, Muse Spark Open 816B A42B 1,048,576.
- Do independent benchmarks agree on GPT-6.1 Sol vs Muse Spark Open 816B A42B?
- Across the 7 benchmarks both have, GPT-6.1 Sol leads 6, Muse Spark Open 816B A42B leads 1. Every overall index that rates both puts GPT-6.1 Sol ahead.
More questions (1)
- When was Muse Spark Open 816B A42B released?
- Muse Spark Open 816B A42B was released on 15 Oct 2026.