Model comparison
GPT-5.6 Sol vs Mistral 7B
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 22.9 on the Noometry Index. Mistral 7B costs 32× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
Mistral AISummary
- They share 21 benchmarks with published results for both. GPT-5.6 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-5.6 Sol leads 85.5 to 7.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-5.6 Sol and 0.3% for Mistral 7B.
- Mistral 7B is cheaper at $0.25 / $0.25 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 8K.
- Mistral 7B has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Sol | Mistral 7B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 64.4 | 22.9 |
| Released | 2026-07-09 | 2023-09-27 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 8K |
| Max output | 128K | 8K |
| Input $ / M tokens | $4 | $0.25 |
| Output $ / M tokens | $20 | $0.25 |
| Results tracked | 64 | 37 |
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Category by category
Coding GPT-5.6 Sol leads
GPT-5.6 Sol: 61.1 (#7), Mistral 7B: 25.9 (#321)
| Benchmark | GPT-5.6 Sol | Mistral 7B |
|---|---|---|
| LMArena Coding | 1493 | 1082 |
| DeepSWE | 72.7% | — |
| FrontierCode | 47.5% | — |
| CursorBench | 41.7% | — |
| LMArena WebDev | 1619 | — |
| FrontierSWE | 32.2% | — |
| SciCode | 57.1% | — |
| WeirdML | 89.4% | — |
| BigCodeBench Instruct | — | 19.5% |
| MirrorCode | 20% | — |
| BigCodeBench Complete | — | 27.3% |
| ALE-Bench | 2,177 | — |
| HumanEval+ | — | 36% |
| MBPP+ | — | 42.1% |
Agentic & Tool Use Not comparable
GPT-5.6 Sol: 50.2 (#7), Mistral 7B: —
| Benchmark | GPT-5.6 Sol | Mistral 7B |
|---|---|---|
| APEX-Agents | 51.4% | — |
| OSWorld 2.0 | 27.3% | — |
| τ²-bench Banking | 46.9% | — |
| PostTrainBench | 36.2% | — |
| BALROG | 60% | — |
| GBAEval | 52.6% | — |
| GDP.pdf | 30.7% | — |
| LMArena Search | 1257 | — |
| Vending-Bench 2 | 9,619 | — |
Reasoning GPT-5.6 Sol leads
GPT-5.6 Sol: 74.7 (#6), Mistral 7B: 12.6 (#331)
| Benchmark | GPT-5.6 Sol | Mistral 7B |
|---|---|---|
| Chess Puzzles | 64% | 0% |
| LMArena Hard Prompts | 1481 | 1067 |
| DTBench | 96% | 42.5% |
| Epoch Capabilities Index | 161.99 | 111.97 |
| ARC-AGI-2 | 92.5% | — |
| SimpleBench | 71.7% | — |
| Kagi LLM Benchmark | 67% | — |
| NYT Connections (extended) | 93.8% | — |
| ARC-AGI-1 | 97.5% | — |
| CritPt | 32.3% | — |
| EnigmaEval | 37.1% | — |
| EBR-Bench | 44.8% | — |
| Mystery Game Puzzles | 58% | — |
| LMCA | 59.2% | — |
| Surface Evolver Bench | 93.1% | — |
| Adversarial NLI | — | 47.1% |
| Bench to the Future 3 | 0.14 | — |
| BIG-Bench Hard | — | 56.1% |
| HellaSwag | — | 81% |
| PIQA | — | 83% |
| WinoGrande | — | 75.3% |
Math GPT-5.6 Sol leads
GPT-5.6 Sol: 85.5 (#7), Mistral 7B: 7.6 (#319)
| Benchmark | GPT-5.6 Sol | Mistral 7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 0.3% |
| LMArena Math | 1472 | 1085 |
| FrontierMath (Tiers 1-3) | 89.1% | — |
| FrontierMath Tier 4 | 82.9% | — |
| ProofBench | 83% | — |
| MATH Level 5 | — | 3.7% |
| FrontierMath Erdős | 0% | — |
| GSM8K | — | 54.4% |
Knowledge GPT-5.6 Sol leads
GPT-5.6 Sol: 64.3 (#16), Mistral 7B: 7.3 (#306)
| Benchmark | GPT-5.6 Sol | Mistral 7B |
|---|---|---|
| GPQA Diamond | 93.5% | 15.2% |
| LMArena Expert | 1517 | 1036 |
| SimpleQA Verified | 69.7% | — |
| Vectara Hallucination Rate | 12.4% | — |
| ARC (AI2) Challenge | — | 78.6% |
| BoolQ | — | 87.4% |
| MMLU | — | 62.5% |
| OpenBookQA | — | 79.8% |
| TriviaQA | — | 75.2% |
Multimodal Not comparable
GPT-5.6 Sol: 48.9 (#8), Mistral 7B: —
| Benchmark | GPT-5.6 Sol | Mistral 7B |
|---|---|---|
| LMArena Vision | 1281 | — |
| Blueprint-Bench 2 | 33.6% | — |
| Furniture Assembly | 56.7% | — |
| LMArena Document | 1483 | — |
Multilingual GPT-5.6 Sol leads
GPT-5.6 Sol: 55.4 (#32), Mistral 7B: 26.0 (#278)
| Benchmark | GPT-5.6 Sol | Mistral 7B |
|---|---|---|
| LMArena Non-English | 1450 | 1012 |
| LMArena Chinese | 1523 | 1009 |
| LMArena French | 1475 | 1036 |
| LMArena German | 1476 | 987 |
| LMArena Japanese | 1475 | 878 |
| LMArena Russian | 1466 | 1018 |
| LMArena Spanish | 1449 | 1026 |
| LMArena Korean | 1434 | — |
Instruction Following GPT-5.6 Sol leads
GPT-5.6 Sol: 77.8 (#16), Mistral 7B: 54.5 (#275)
| Benchmark | GPT-5.6 Sol | Mistral 7B |
|---|---|---|
| LMArena Instruction Following | 1480 | 1060 |
Long Context GPT-5.6 Sol leads
GPT-5.6 Sol: 45.3 (#42), Mistral 7B: 32.2 (#266)
| Benchmark | GPT-5.6 Sol | Mistral 7B |
|---|---|---|
| LMArena Longer Query | 1479 | 1060 |
Writing & Preference GPT-5.6 Sol leads
GPT-5.6 Sol: 73.5 (#12), Mistral 7B: 30.9 (#281)
| Benchmark | GPT-5.6 Sol | Mistral 7B |
|---|---|---|
| LMArena Text | 1456 | 1090 |
| LMArena Creative Writing | 1450 | 1068 |
| LMArena Multi-Turn | 1456 | 1062 |
| EQ-Bench Creative Writing | 1972 | — |
| EQ-Bench 4 | 1250 | — |
Frequently asked questions
Is GPT-5.6 Sol better than Mistral 7B?
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 22.9 on the Noometry Index. Mistral 7B costs 32× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-5.6 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-5.6 Sol lists at $4 and $20.
Is GPT-5.6 Sol or Mistral 7B better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 61.1 versus 25.9 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Sol does, with 1.05M tokens against 8K.
How many benchmarks do GPT-5.6 Sol and Mistral 7B share?
21 benchmarks have published results for both models. GPT-5.6 Sol has 64 scored results on Noometry and Mistral 7B has 37.