Model comparison
GPT-6.1 Sol vs Mistral Large
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 31.9 on the Noometry Index.
Last verified . 17 shared benchmarks.
Mistral AISummary
- They share 17 benchmarks with published results for both. GPT-6.1 Sol scores higher in 9 categories and Mistral Large in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6.1 Sol leads 93.7 to 18.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-6.1 Sol and 8.5% for Mistral Large.
- Mistral Large is cheaper at $2 / $6 per million input/output tokens, against $2 / $10 for GPT-6.1 Sol.
- GPT-6.1 Sol accepts more context: 1.05M tokens versus 131K.
- Mistral Large has downloadable open weights; the other is API-only.
Side by side
| GPT-6.1 Sol | Mistral Large | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 65.6 | 31.9 |
| Released | 2026-09-29 | 2024-02-26 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 128K | 16K |
| Input $ / M tokens | $2 | $2 |
| Output $ / M tokens | $10 | $6 |
| Results tracked | 34 | 51 |
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Category by category
Coding GPT-6.1 Sol leads
GPT-6.1 Sol: 63.2 (#8), Mistral Large: 34.3 (#240)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| SciCode | 55.8% | 36.2% |
| LMArena Coding | 1487 | 1277 |
| DeepSWE | 75.2% | — |
| FrontierCode | 50.2% | — |
| LMArena WebDev | 1755 | — |
| BigCodeBench Instruct | — | 30% |
| LiveBench Coding | — | 47.1% |
| BigCodeBench Complete | — | 38.3% |
| ALE-Bench | — | 264.7 |
| HumanEval+ | — | 62.2% |
| MBPP+ | — | 59.5% |
Agentic & Tool Use GPT-6.1 Sol leads
GPT-6.1 Sol: 39.6 (#26), Mistral Large: 28.6 (#89)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| APEX-Agents | 60% | — |
| Berkeley Function Calling Leaderboard | — | 38.4% |
| GDP.pdf | 32% | — |
Reasoning GPT-6.1 Sol leads
GPT-6.1 Sol: 81.9 (#2), Mistral Large: 15.8 (#310)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| CritPt | 31.7% | 0% |
| LMArena Hard Prompts | 1466 | 1257 |
| Epoch Capabilities Index | 166.09 | 128.52 |
| ARC-AGI-2 | 94.2% | — |
| SimpleBench | — | 22.5% |
| NYT Connections (extended) | 95.5% | — |
| ARC-AGI-1 | 98.5% | — |
| Chess Puzzles | 61% | — |
| EBR-Bench | 54.3% | — |
| LiveBench Reasoning | — | 43.5% |
| Mystery Game Puzzles | 80% | — |
| DTBench | — | 65.1% |
| LiveBench Data Analysis | — | 50.1% |
| LMCA | — | 16.7% |
| ForecastBench | — | 57.1 |
| LiveBench | — | 48.4% |
Math GPT-6.1 Sol leads
GPT-6.1 Sol: 93.7 (#1), Mistral Large: 18.2 (#291)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 8.5% |
| LMArena Math | 1464 | 1262 |
| FrontierMath (Tiers 1-3) | 93.7% | — |
| FrontierMath Tier 4 | 100% | — |
| ProofBench | 99% | — |
| Omni-MATH | — | 28.1% |
| LiveBench Math | — | 42.5% |
| MATH Level 5 | — | 50.3% |
| FrontierMath (Feb 2025 set) | — | 0.3% |
Knowledge GPT-6.1 Sol leads
GPT-6.1 Sol: 71.8 (#4), Mistral Large: 30.1 (#230)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| GPQA Diamond | 95.4% | 51.3% |
| LMArena Expert | 1502 | 1232 |
| SimpleQA Verified | 73.9% | — |
| MMLU-Pro | — | 59.9% |
| Confabulations | — | 21.4% |
| Vectara Hallucination Rate | — | 4.5% |
| GPQA (HELM) | — | 43.5% |
| MMLU | — | 80% |
Multimodal Not comparable
GPT-6.1 Sol: 52.7 (#5), Mistral Large: —
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| LMArena Vision | 1288 | — |
| Furniture Assembly | 80% | — |
Multilingual GPT-6.1 Sol leads
GPT-6.1 Sol: 54.3 (#46), Mistral Large: 40.0 (#219)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| LMArena Non-English | 1438 | 1237 |
| LMArena Chinese | 1477 | 1240 |
| LMArena Russian | 1455 | 1257 |
| LMArena French | — | 1325 |
| LMArena German | — | 1254 |
| LMArena Japanese | — | 1188 |
| LMArena Korean | — | 1202 |
| LMArena Spanish | — | 1268 |
Instruction Following GPT-6.1 Sol leads
GPT-6.1 Sol: 77.0 (#29), Mistral Large: 67.9 (#191)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| LMArena Instruction Following | 1468 | 1249 |
| LiveBench Instruction Following | — | 67.9% |
| IFEval | — | 87.7% |
Long Context GPT-6.1 Sol leads
GPT-6.1 Sol: 44.9 (#54), Mistral Large: 38.3 (#199)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| LMArena Longer Query | 1465 | 1261 |
Writing & Preference GPT-6.1 Sol leads
GPT-6.1 Sol: 63.6 (#63), Mistral Large: 40.7 (#242)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| LMArena Text | 1447 | 1266 |
| LMArena Creative Writing | 1432 | 1243 |
| LMArena Multi-Turn | 1449 | 1260 |
| Short-Story Creative Writing | — | 69% |
| EQ-Bench Creative Writing | — | 985 |
| WildBench | — | 80.1% |
| LiveBench Language | — | 39.4% |
Frequently asked questions
Is GPT-6.1 Sol better than Mistral Large?
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 31.9 on the Noometry Index.
Which is cheaper, GPT-6.1 Sol or Mistral Large?
Mistral Large is cheaper. It lists at $2 per million input tokens and $6 per million output tokens; GPT-6.1 Sol lists at $2 and $10.
Is GPT-6.1 Sol or Mistral Large better for coding?
GPT-6.1 Sol scores higher on coding benchmarks: 63.2 versus 34.3 in the Noometry coding category.
Which has the bigger context window?
GPT-6.1 Sol does, with 1.05M tokens against 131K.
How many benchmarks do GPT-6.1 Sol and Mistral Large share?
17 benchmarks have published results for both models. GPT-6.1 Sol has 34 scored results on Noometry and Mistral Large has 51.