Model comparison
GPT-5.6 Sol vs Mistral Small 3.1
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 31.4 on the Noometry Index. Mistral Small 3.1 costs 20× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 23 shared benchmarks.
Mistral AISummary
- They share 23 benchmarks with published results for both. GPT-5.6 Sol scores higher in 9 categories and Mistral Small 3.1 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Sol leads 85.5 to 13.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-5.6 Sol and 3.9% for Mistral Small 3.1.
- Mistral Small 3.1 is cheaper at $0.35 / $0.56 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 128K.
- Mistral Small 3.1 has downloadable open weights; the other is API-only.
Side by side
| GPT-5.6 Sol | Mistral Small 3.1 | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 64.4 | 31.4 |
| Released | 2026-07-09 | 2025-03-17 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 128K |
| Max output | 128K | 102K |
| Input $ / M tokens | $4 | $0.35 |
| Output $ / M tokens | $20 | $0.56 |
| Results tracked | 64 | 28 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-5.6 Sol leads
GPT-5.6 Sol: 61.1 (#7), Mistral Small 3.1: 37.7 (#175)
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 |
|---|---|---|
| LMArena Coding | 1493 | 1309 |
| DeepSWE | 72.7% | — |
| FrontierCode | 47.5% | — |
| CursorBench | 41.7% | — |
| LMArena WebDev | 1619 | — |
| FrontierSWE | 32.2% | — |
| SciCode | 57.1% | — |
| WeirdML | 89.4% | — |
| MirrorCode | 20% | — |
| ALE-Bench | 2,177 | — |
Agentic & Tool Use Not comparable
GPT-5.6 Sol: 50.2 (#7), Mistral Small 3.1: —
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 |
|---|---|---|
| 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 Small 3.1: 18.9 (#249)
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 |
|---|---|---|
| Chess Puzzles | 64% | 1% |
| LMArena Hard Prompts | 1481 | 1278 |
| Epoch Capabilities Index | 161.99 | 127.48 |
| 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% | — |
| DTBench | 96% | — |
| LMCA | 59.2% | — |
| Surface Evolver Bench | 93.1% | — |
| Bench to the Future 3 | 0.14 | — |
Math GPT-5.6 Sol leads
GPT-5.6 Sol: 85.5 (#7), Mistral Small 3.1: 13.7 (#295)
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 3.9% |
| LMArena Math | 1472 | 1261 |
| FrontierMath (Tiers 1-3) | 89.1% | — |
| FrontierMath Tier 4 | 82.9% | — |
| ProofBench | 83% | — |
| Omni-MATH | — | 24.8% |
| FrontierMath Erdős | 0% | — |
Knowledge GPT-5.6 Sol leads
GPT-5.6 Sol: 64.3 (#16), Mistral Small 3.1: 22.6 (#266)
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 |
|---|---|---|
| GPQA Diamond | 93.5% | 41.9% |
| LMArena Expert | 1517 | 1258 |
| SimpleQA Verified | 69.7% | — |
| MMLU-Pro | — | 61% |
| Vectara Hallucination Rate | 12.4% | — |
| GPQA (HELM) | — | 39.2% |
Multimodal GPT-5.6 Sol leads
GPT-5.6 Sol: 48.9 (#8), Mistral Small 3.1: 32.6 (#93)
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 |
|---|---|---|
| LMArena Vision | 1281 | 1136 |
| 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 Small 3.1: 41.4 (#204)
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 |
|---|---|---|
| LMArena Non-English | 1450 | 1255 |
| LMArena Chinese | 1523 | 1254 |
| LMArena French | 1475 | 1270 |
| LMArena German | 1476 | 1266 |
| LMArena Japanese | 1475 | 1210 |
| LMArena Korean | 1434 | 1206 |
| LMArena Russian | 1466 | 1263 |
| LMArena Spanish | 1449 | 1283 |
Instruction Following GPT-5.6 Sol leads
GPT-5.6 Sol: 77.8 (#16), Mistral Small 3.1: 63.8 (#225)
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 |
|---|---|---|
| LMArena Instruction Following | 1480 | 1264 |
| IFEval | — | 75% |
Long Context GPT-5.6 Sol leads
GPT-5.6 Sol: 45.3 (#42), Mistral Small 3.1: 39.5 (#172)
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 |
|---|---|---|
| LMArena Longer Query | 1479 | 1300 |
Writing & Preference GPT-5.6 Sol leads
GPT-5.6 Sol: 73.5 (#12), Mistral Small 3.1: 37.2 (#254)
| Benchmark | GPT-5.6 Sol | Mistral Small 3.1 |
|---|---|---|
| LMArena Text | 1456 | 1278 |
| LMArena Creative Writing | 1450 | 1253 |
| EQ-Bench Creative Writing | 1972 | 761 |
| LMArena Multi-Turn | 1456 | 1270 |
| WildBench | — | 78.8% |
| EQ-Bench 4 | 1250 | — |
Frequently asked questions
Is GPT-5.6 Sol better than Mistral Small 3.1?
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 31.4 on the Noometry Index. Mistral Small 3.1 costs 20× 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 Small 3.1?
Mistral Small 3.1 is cheaper. It lists at $0.35 per million input tokens and $0.56 per million output tokens; GPT-5.6 Sol lists at $4 and $20.
Is GPT-5.6 Sol or Mistral Small 3.1 better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 61.1 versus 37.7 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Sol does, with 1.05M tokens against 128K.
How many benchmarks do GPT-5.6 Sol and Mistral Small 3.1 share?
23 benchmarks have published results for both models. GPT-5.6 Sol has 64 scored results on Noometry and Mistral Small 3.1 has 28.