Model comparison
GPT-6.1 Sol vs Mistral Small
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 33.4 on the Noometry Index. Mistral Small costs 15× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Last verified . 17 shared benchmarks.
Mistral AISummary
- They share 17 benchmarks with published results for both. GPT-6.1 Sol scores higher in 10 categories and Mistral Small in 0 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6.1 Sol leads 93.7 to 16.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 100% for GPT-6.1 Sol and 5.8% for Mistral Small.
- Mistral Small is cheaper at $0.15 / $0.60 per million input/output tokens, against $2 / $10 for GPT-6.1 Sol.
- GPT-6.1 Sol accepts more context: 1.05M tokens versus 262K.
- Mistral Small has downloadable open weights; the other is API-only.
Side by side
| GPT-6.1 Sol | Mistral Small | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 65.6 | 33.4 |
| Released | 2026-09-29 | 2024-02-26 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 262K |
| Max output | 128K | 256K |
| Input $ / M tokens | $2 | $0.15 |
| Output $ / M tokens | $10 | $0.60 |
| Results tracked | 34 | 39 |
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Category by category
Coding GPT-6.1 Sol leads
GPT-6.1 Sol: 63.2 (#8), Mistral Small: 34.0 (#247)
| Benchmark | GPT-6.1 Sol | Mistral Small |
|---|---|---|
| SciCode | 55.8% | 26.5% |
| LMArena Coding | 1487 | 1362 |
| DeepSWE | 75.2% | — |
| FrontierCode | 50.2% | — |
| LMArena WebDev | 1755 | — |
| BigCodeBench Instruct | — | 36.1% |
| LiveBench Coding | — | 36.2% |
| BigCodeBench Complete | — | 46.6% |
| ALE-Bench | — | 497.62 |
Agentic & Tool Use GPT-6.1 Sol leads
GPT-6.1 Sol: 39.6 (#26), Mistral Small: 28.1 (#93)
| Benchmark | GPT-6.1 Sol | Mistral Small |
|---|---|---|
| APEX-Agents | 60% | — |
| Berkeley Function Calling Leaderboard | — | 37.1% |
| GDP.pdf | 32% | — |
Reasoning GPT-6.1 Sol leads
GPT-6.1 Sol: 81.9 (#2), Mistral Small: 19.8 (#250)
| Benchmark | GPT-6.1 Sol | Mistral Small |
|---|---|---|
| CritPt | 31.7% | 0% |
| LMArena Hard Prompts | 1466 | 1335 |
| ARC-AGI-2 | 94.2% | — |
| Kagi LLM Benchmark | — | 37.8% |
| NYT Connections (extended) | 95.5% | — |
| ARC-AGI-1 | 98.5% | — |
| Chess Puzzles | 61% | — |
| EBR-Bench | 54.3% | — |
| LiveBench Reasoning | — | 44.8% |
| Mystery Game Puzzles | 80% | — |
| DTBench | — | 70.9% |
| LiveBench Data Analysis | — | 53.7% |
| LMCA | — | 20.6% |
| Epoch Capabilities Index | 166.09 | — |
| LiveBench | — | 44% |
Math GPT-6.1 Sol leads
GPT-6.1 Sol: 93.7 (#1), Mistral Small: 16.4 (#293)
| Benchmark | GPT-6.1 Sol | Mistral Small |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 100% | 5.8% |
| LMArena Math | 1464 | 1341 |
| FrontierMath (Tiers 1-3) | 93.7% | — |
| FrontierMath Tier 4 | 100% | — |
| ProofBench | 99% | — |
| LiveBench Math | — | 39.9% |
| MATH Level 5 | — | 46.8% |
Knowledge GPT-6.1 Sol leads
GPT-6.1 Sol: 71.8 (#4), Mistral Small: 31.0 (#222)
| Benchmark | GPT-6.1 Sol | Mistral Small |
|---|---|---|
| GPQA Diamond | 95.4% | 47.5% |
| LMArena Expert | 1502 | 1291 |
| SimpleQA Verified | 73.9% | — |
| Vectara Hallucination Rate | — | 5.1% |
| MMLU | — | 68.7% |
Multimodal GPT-6.1 Sol leads
GPT-6.1 Sol: 52.7 (#5), Mistral Small: 33.5 (#96)
| Benchmark | GPT-6.1 Sol | Mistral Small |
|---|---|---|
| LMArena Vision | 1288 | 1142 |
| Furniture Assembly | 80% | — |
Multilingual GPT-6.1 Sol leads
GPT-6.1 Sol: 54.3 (#46), Mistral Small: 45.5 (#169)
| Benchmark | GPT-6.1 Sol | Mistral Small |
|---|---|---|
| LMArena Non-English | 1438 | 1315 |
| LMArena Chinese | 1477 | 1340 |
| LMArena Russian | 1455 | 1324 |
| LMArena French | — | 1337 |
| LMArena German | — | 1340 |
| LMArena Japanese | — | 1275 |
| LMArena Korean | — | 1259 |
| LMArena Spanish | — | 1346 |
Instruction Following GPT-6.1 Sol leads
GPT-6.1 Sol: 77.0 (#29), Mistral Small: 66.4 (#209)
| Benchmark | GPT-6.1 Sol | Mistral Small |
|---|---|---|
| LMArena Instruction Following | 1468 | 1310 |
| LiveBench Instruction Following | — | 63.7% |
Long Context GPT-6.1 Sol leads
GPT-6.1 Sol: 44.9 (#54), Mistral Small: 40.4 (#156)
| Benchmark | GPT-6.1 Sol | Mistral Small |
|---|---|---|
| LMArena Longer Query | 1465 | 1327 |
Writing & Preference GPT-6.1 Sol leads
GPT-6.1 Sol: 63.6 (#63), Mistral Small: 52.5 (#171)
| Benchmark | GPT-6.1 Sol | Mistral Small |
|---|---|---|
| LMArena Text | 1447 | 1338 |
| LMArena Creative Writing | 1432 | 1305 |
| LMArena Multi-Turn | 1449 | 1344 |
| LiveBench Language | — | 30.5% |
Frequently asked questions
Is GPT-6.1 Sol better than Mistral Small?
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 33.4 on the Noometry Index. Mistral Small costs 15× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Which is cheaper, GPT-6.1 Sol or Mistral Small?
Mistral Small is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GPT-6.1 Sol lists at $2 and $10.
Is GPT-6.1 Sol or Mistral Small better for coding?
GPT-6.1 Sol scores higher on coding benchmarks: 63.2 versus 34.0 in the Noometry coding category.
Which has the bigger context window?
GPT-6.1 Sol does, with 1.05M tokens against 262K.
How many benchmarks do GPT-6.1 Sol and Mistral Small share?
17 benchmarks have published results for both models. GPT-6.1 Sol has 34 scored results on Noometry and Mistral Small has 39.