Model comparison
Mistral Small vs o1
o1 is the stronger model overall, scoring 40.9 to 33.4 on the Noometry Index. Mistral Small costs 100× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.
Last verified . 30 shared benchmarks.
Summary
- They share 30 benchmarks with published results for both. Mistral Small scores higher in 1 category and o1 in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where o1 leads 36.1 to 16.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 5.8% for Mistral Small and 73.3% for o1.
- Mistral Small is cheaper at $0.15 / $0.60 per million input/output tokens, against $15 / $60 for o1.
- Mistral Small accepts more context: 262K tokens versus 200K.
- Mistral Small has downloadable open weights; the other is API-only.
Side by side
| Mistral Small | o1 | |
|---|---|---|
| Provider | Mistral AI | OpenAI |
| Noometry Index | 33.4 | 40.9 |
| Released | 2024-02-26 | 2024-09-12 |
| Weights | Open | Proprietary |
| Context window | 262K | 200K |
| Max output | 256K | 100K |
| Input $ / M tokens | $0.15 | $15 |
| Output $ / M tokens | $0.60 | $60 |
| Results tracked | 39 | 52 |
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Category by category
Coding o1 leads
Mistral Small: 34.0 (#247), o1: 46.1 (#70)
| Benchmark | Mistral Small | o1 |
|---|---|---|
| LiveBench Coding | 36.2% | 69.7% |
| LMArena Coding | 1362 | 1367 |
| Aider Polyglot | — | 61.7% |
| SciCode | 26.5% | — |
| WeirdML | — | 47.6% |
| BigCodeBench Instruct | 36.1% | — |
| BigCodeBench Complete | 46.6% | — |
| CadEval | — | 56% |
| ALE-Bench | 497.62 | — |
| HumanEval+ | — | 89% |
| MBPP+ | — | 80.2% |
Agentic & Tool Use Mistral Small leads
Mistral Small: 28.1 (#93), o1: 24.6 (#117)
| Benchmark | Mistral Small | o1 |
|---|---|---|
| Berkeley Function Calling Leaderboard | 37.1% | — |
| Cybench | — | 10% |
| METR Time Horizons | — | 51.1% |
Reasoning o1 leads
Mistral Small: 19.8 (#250), o1: 27.9 (#111)
| Benchmark | Mistral Small | o1 |
|---|---|---|
| LiveBench Reasoning | 44.8% | 91.6% |
| LMArena Hard Prompts | 1335 | 1371 |
| DTBench | 70.9% | 74.7% |
| LiveBench Data Analysis | 53.7% | 65.5% |
| LMCA | 20.6% | 22.3% |
| LiveBench | 44% | 75.7% |
| SimpleBench | — | 41.7% |
| Kagi LLM Benchmark | 37.8% | — |
| ARC-AGI-1 | — | 30.7% |
| CritPt | 0% | — |
| Chess Puzzles | — | 15% |
| EnigmaEval | — | 5.7% |
| Epoch Capabilities Index | — | 141.91 |
Math o1 leads
Mistral Small: 16.4 (#293), o1: 36.1 (#175)
| Benchmark | Mistral Small | o1 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 5.8% | 73.3% |
| LiveBench Math | 39.9% | 80.3% |
| LMArena Math | 1341 | 1388 |
| MATH Level 5 | 46.8% | 94.7% |
| FrontierMath (Tiers 1-3) | — | 14.7% |
| FrontierMath (Feb 2025 set) | — | 9.3% |
Knowledge o1 leads
Mistral Small: 31.0 (#222), o1: 41.5 (#110)
| Benchmark | Mistral Small | o1 |
|---|---|---|
| GPQA Diamond | 47.5% | 76.8% |
| LMArena Expert | 1291 | 1361 |
| Humanity's Last Exam | — | 8% |
| SimpleQA Verified | — | 41.1% |
| Confabulations | — | 11.7% |
| Vectara Hallucination Rate | 5.1% | — |
| MMLU | 68.7% | — |
Multimodal Too close to call
Mistral Small: 33.5 (#96), o1: 34.2 (#93)
| Benchmark | Mistral Small | o1 |
|---|---|---|
| LMArena Vision | 1142 | 1168 |
| GeoBench | — | 80% |
| VPCT | — | 37% |
| SpatialViz-Bench | — | 41.4% |
Multilingual o1 leads
Mistral Small: 45.5 (#169), o1: 48.6 (#142)
| Benchmark | Mistral Small | o1 |
|---|---|---|
| LMArena Non-English | 1315 | 1358 |
| LMArena Chinese | 1340 | 1394 |
| LMArena French | 1337 | 1344 |
| LMArena German | 1340 | 1337 |
| LMArena Japanese | 1275 | 1346 |
| LMArena Korean | 1259 | 1396 |
| LMArena Russian | 1324 | 1356 |
| LMArena Spanish | 1346 | 1345 |
Instruction Following o1 leads
Mistral Small: 66.4 (#209), o1: 74.8 (#86)
| Benchmark | Mistral Small | o1 |
|---|---|---|
| LiveBench Instruction Following | 63.7% | 81.5% |
| LMArena Instruction Following | 1310 | 1367 |
Long Context o1 leads
Mistral Small: 40.4 (#156), o1: 50.3 (#9)
| Benchmark | Mistral Small | o1 |
|---|---|---|
| LMArena Longer Query | 1327 | 1378 |
| Fiction.LiveBench | — | 83.3% |
Writing & Preference o1 leads
Mistral Small: 52.5 (#171), o1: 55.6 (#144)
| Benchmark | Mistral Small | o1 |
|---|---|---|
| LMArena Text | 1338 | 1366 |
| LMArena Creative Writing | 1305 | 1348 |
| LMArena Multi-Turn | 1344 | 1369 |
| LiveBench Language | 30.5% | 65.4% |
| Short-Story Creative Writing | — | 70.2% |
Frequently asked questions
Is Mistral Small better than o1?
o1 is the stronger model overall, scoring 40.9 to 33.4 on the Noometry Index. Mistral Small costs 100× less per token, which makes it the better buy when o1's lead doesn't matter for your workload.
Which is cheaper, Mistral Small or o1?
Mistral Small is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; o1 lists at $15 and $60.
Is Mistral Small or o1 better for coding?
o1 scores higher on coding benchmarks: 46.1 versus 34.0 in the Noometry coding category.
Which has the bigger context window?
Mistral Small does, with 262K tokens against 200K.
How many benchmarks do Mistral Small and o1 share?
30 benchmarks have published results for both models. Mistral Small has 39 scored results on Noometry and o1 has 52.