Model comparison
GPT-6.1 Sol vs Mistral Large
Mistral Large has enough public results to be ranked (#258); GPT-6.1 Sol does not yet, so treat this comparison as directional.
Last verified . 0 shared benchmarks.
Mistral AISummary
- The widest gap is in coding, where GPT-6.1 Sol leads 51.4 to 33.8.
- 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 | 42.1 | 31.6 |
| 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 | 2 | 51 |
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Category by category
Coding GPT-6.1 Sol leads
GPT-6.1 Sol: 51.4, Mistral Large: 33.8 (#240)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| DeepSWE | 75.2% | — |
| SciCode | — | 36.2% |
| BigCodeBench Instruct | — | 30% |
| LiveBench Coding | — | 47.1% |
| LMArena Coding | — | 1277 |
| 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: 34.3, Mistral Large: 28.4 (#83)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 38.4% |
| GDP.pdf | 32% | — |
Reasoning Not comparable
GPT-6.1 Sol: —, Mistral Large: 15.4 (#305)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| SimpleBench | — | 22.5% |
| CritPt | — | 0% |
| LiveBench Reasoning | — | 43.5% |
| LMArena Hard Prompts | — | 1257 |
| DTBench | — | 65.1% |
| LiveBench Data Analysis | — | 50.1% |
| LMCA | — | 16.7% |
| Epoch Capabilities Index | — | 128.52 |
| ForecastBench | — | 57.1 |
| LiveBench | — | 48.4% |
Math Not comparable
GPT-6.1 Sol: —, Mistral Large: 17.1 (#285)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 8.5% |
| Omni-MATH | — | 28.1% |
| LiveBench Math | — | 42.5% |
| LMArena Math | — | 1262 |
| MATH Level 5 | — | 50.3% |
| FrontierMath (Feb 2025 set) | — | 0.3% |
Knowledge Not comparable
GPT-6.1 Sol: —, Mistral Large: 30.0 (#224)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| GPQA Diamond | — | 51.3% |
| MMLU-Pro | — | 59.9% |
| Confabulations | — | 21.4% |
| Vectara Hallucination Rate | — | 4.5% |
| GPQA (HELM) | — | 43.5% |
| LMArena Expert | — | 1232 |
| MMLU | — | 80% |
Multilingual Not comparable
GPT-6.1 Sol: —, Mistral Large: 40.1 (#214)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| LMArena Non-English | — | 1237 |
| LMArena Chinese | — | 1240 |
| LMArena French | — | 1324 |
| LMArena German | — | 1254 |
| LMArena Japanese | — | 1187 |
| LMArena Korean | — | 1202 |
| LMArena Russian | — | 1257 |
| LMArena Spanish | — | 1268 |
Instruction Following Not comparable
GPT-6.1 Sol: —, Mistral Large: 68.1 (#186)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| LiveBench Instruction Following | — | 67.9% |
| IFEval | — | 87.7% |
| LMArena Instruction Following | — | 1249 |
Long Context Not comparable
GPT-6.1 Sol: —, Mistral Large: 38.3 (#194)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| LMArena Longer Query | — | 1262 |
Writing & Preference Not comparable
GPT-6.1 Sol: —, Mistral Large: 40.9 (#237)
| Benchmark | GPT-6.1 Sol | Mistral Large |
|---|---|---|
| LMArena Text | — | 1266 |
| LMArena Creative Writing | — | 1243 |
| Short-Story Creative Writing | — | 69% |
| EQ-Bench Creative Writing | — | 985 |
| WildBench | — | 80.1% |
| LMArena Multi-Turn | — | 1260 |
| LiveBench Language | — | 39.4% |
Frequently asked questions
Is GPT-6.1 Sol better than Mistral Large?
Mistral Large has enough public results to be ranked (#258); GPT-6.1 Sol does not yet, so treat this comparison as directional.
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: 51.4 versus 33.8 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?
0 benchmarks have published results for both models. GPT-6.1 Sol has 2 scored results on Noometry and Mistral Large has 51.