Model comparison
GPT-6 Sol vs Mistral 7B
GPT-6 Sol is the stronger model overall, scoring 61.8 to 23.0 on the Noometry Index. Mistral 7B costs 16× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 20 shared benchmarks.
Mistral AISummary
- They share 20 benchmarks with published results for both. GPT-6 Sol scores higher in 8 categories and Mistral 7B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Sol leads 87.2 to 8.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-6 Sol and 0.3% for Mistral 7B.
- Mistral 7B is cheaper at $0.25 / $0.25 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 8K.
- Mistral 7B has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Sol | Mistral 7B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 61.8 | 23.0 |
| Released | 2026-09-22 | 2023-09-27 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 8K |
| Max output | 128K | 8K |
| Input $ / M tokens | $2 | $0.25 |
| Output $ / M tokens | $10 | $0.25 |
| Results tracked | 45 | 37 |
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Category by category
Coding GPT-6 Sol leads
GPT-6 Sol: 60.1 (#11), Mistral 7B: 26.4 (#326)
| Benchmark | GPT-6 Sol | Mistral 7B |
|---|---|---|
| LMArena Coding | 1447 | 1082 |
| DeepSWE | 68.8% | — |
| FrontierCode | 49.3% | — |
| LMArena WebDev | 1688 | — |
| SciCode | 57.6% | — |
| BigCodeBench Instruct | — | 19.5% |
| BigCodeBench Complete | — | 27.3% |
| ALE-Bench | 2,462 | — |
| HumanEval+ | — | 36% |
| MBPP+ | — | 42.1% |
Agentic & Tool Use Not comparable
GPT-6 Sol: 37.2 (#36), Mistral 7B: —
| Benchmark | GPT-6 Sol | Mistral 7B |
|---|---|---|
| APEX-Agents | 54.3% | — |
| GDP.pdf | 26.4% | — |
| Vending-Bench 2 | 14,428 | — |
Reasoning GPT-6 Sol leads
GPT-6 Sol: 74.0 (#9), Mistral 7B: 13.1 (#336)
| Benchmark | GPT-6 Sol | Mistral 7B |
|---|---|---|
| LMArena Hard Prompts | 1418 | 1067 |
| DTBench | 97.3% | 42.5% |
| Epoch Capabilities Index | 162.72 | 112.21 |
| ARC-AGI-2 | 89.6% | — |
| NYT Connections (extended) | 90.1% | — |
| ARC-AGI-1 | 95.5% | — |
| CritPt | 30.9% | — |
| Chess Puzzles | — | 0% |
| EBR-Bench | 53.3% | — |
| Mystery Game Puzzles | 56% | — |
| LMCA | 59.1% | — |
| Adversarial NLI | — | 47.1% |
| BIG-Bench Hard | — | 56.1% |
| HellaSwag | — | 81% |
| PIQA | — | 83% |
| WinoGrande | — | 75.3% |
Math GPT-6 Sol leads
GPT-6 Sol: 87.2 (#7), Mistral 7B: 8.1 (#325)
| Benchmark | GPT-6 Sol | Mistral 7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 0.3% |
| LMArena Math | 1402 | 1085 |
| FrontierMath (Tiers 1-3) | 89.8% | — |
| FrontierMath Tier 4 | 90% | — |
| ProofBench | 83% | — |
| MATH Level 5 | — | 3.7% |
| GSM8K | — | 54.4% |
Knowledge GPT-6 Sol leads
GPT-6 Sol: 64.8 (#15), Mistral 7B: 7.4 (#311)
| Benchmark | GPT-6 Sol | Mistral 7B |
|---|---|---|
| GPQA Diamond | 94.3% | 15.2% |
| LMArena Expert | 1439 | 1036 |
| SimpleQA Verified | 60.7% | — |
| Vectara Hallucination Rate | 6.5% | — |
| ARC (AI2) Challenge | — | 78.6% |
| BoolQ | — | 87.4% |
| MMLU | — | 62.5% |
| OpenBookQA | — | 79.8% |
| TriviaQA | — | 75.2% |
Multimodal Not comparable
GPT-6 Sol: 47.6 (#10), Mistral 7B: —
| Benchmark | GPT-6 Sol | Mistral 7B |
|---|---|---|
| LMArena Vision | 1245 | — |
| Blueprint-Bench 2 | 36.9% | — |
| Furniture Assembly | 58.3% | — |
Multilingual GPT-6 Sol leads
GPT-6 Sol: 50.5 (#118), Mistral 7B: 25.8 (#283)
| Benchmark | GPT-6 Sol | Mistral 7B |
|---|---|---|
| LMArena Non-English | 1385 | 1012 |
| LMArena Chinese | 1405 | 1009 |
| LMArena French | 1410 | 1037 |
| LMArena German | 1390 | 987 |
| LMArena Japanese | 1385 | 878 |
| LMArena Russian | 1401 | 1018 |
| LMArena Spanish | 1384 | 1026 |
| LMArena Korean | 1341 | — |
Instruction Following GPT-6 Sol leads
GPT-6 Sol: 74.5 (#94), Mistral 7B: 54.2 (#280)
| Benchmark | GPT-6 Sol | Mistral 7B |
|---|---|---|
| LMArena Instruction Following | 1412 | 1060 |
Long Context GPT-6 Sol leads
GPT-6 Sol: 43.1 (#108), Mistral 7B: 32.2 (#271)
| Benchmark | GPT-6 Sol | Mistral 7B |
|---|---|---|
| LMArena Longer Query | 1411 | 1060 |
Writing & Preference GPT-6 Sol leads
GPT-6 Sol: 71.9 (#18), Mistral 7B: 30.7 (#286)
| Benchmark | GPT-6 Sol | Mistral 7B |
|---|---|---|
| LMArena Text | 1395 | 1090 |
| LMArena Creative Writing | 1378 | 1068 |
| LMArena Multi-Turn | 1412 | 1062 |
| EQ-Bench Creative Writing | 2125 | — |
Frequently asked questions
Is GPT-6 Sol better than Mistral 7B?
GPT-6 Sol is the stronger model overall, scoring 61.8 to 23.0 on the Noometry Index. Mistral 7B costs 16× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-6 Sol or Mistral 7B?
Mistral 7B is cheaper. It lists at $0.25 per million input tokens and $0.25 per million output tokens; GPT-6 Sol lists at $2 and $10.
Is GPT-6 Sol or Mistral 7B better for coding?
GPT-6 Sol scores higher on coding benchmarks: 60.1 versus 26.4 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Sol does, with 1.05M tokens against 8K.
How many benchmarks do GPT-6 Sol and Mistral 7B share?
20 benchmarks have published results for both models. GPT-6 Sol has 45 scored results on Noometry and Mistral 7B has 37.