Model comparison
gpt-oss-120b vs Mistral Small
gpt-oss-120b is the stronger model overall, scoring 36.0 to 33.1 on the Noometry Index.
Last verified . 26 shared benchmarks.
Mistral AISummary
- They share 26 benchmarks with published results for both. gpt-oss-120b scores higher in 5 categories and Mistral Small in 4 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 50.6 to 15.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 88.9% for gpt-oss-120b and 5.8% for Mistral Small.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.15 / $0.60 for Mistral Small.
- Mistral Small accepts more context: 256K tokens versus 131K.
Side by side
| gpt-oss-120b | Mistral Small | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 36.0 | 33.1 |
| Released | 2025-08-05 | 2024-02-26 |
| Weights | Open | Open |
| Context window | 131K | 256K |
| Max output | 41K | 256K |
| Input $ / M tokens | $0.037 | $0.15 |
| Output $ / M tokens | $0.17 | $0.60 |
| Results tracked | 48 | 39 |
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Category by category
Coding Too close to call
gpt-oss-120b: 33.2 (#251), Mistral Small: 33.5 (#242)
| Benchmark | gpt-oss-120b | Mistral Small |
|---|---|---|
| SciCode | 36% | 26.5% |
| LMArena Coding | 1380 | 1363 |
| ALE-Bench | 575.62 | 497.62 |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| WeirdML | 48.2% | — |
| BigCodeBench Instruct | — | 36.1% |
| LiveBench Coding | — | 36.2% |
| BigCodeBench Complete | — | 46.6% |
| AlgoTune | 1.41 | — |
Agentic & Tool Use Mistral Small leads
gpt-oss-120b: 12.1 (#145), Mistral Small: 27.9 (#87)
| Benchmark | gpt-oss-120b | Mistral Small |
|---|---|---|
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| Berkeley Function Calling Leaderboard | — | 37.1% |
| METR Time Horizons | 56.6% | — |
| Vending-Bench 2 | -21.53 | — |
Reasoning Too close to call
gpt-oss-120b: 19.5 (#236), Mistral Small: 19.3 (#238)
| Benchmark | gpt-oss-120b | Mistral Small |
|---|---|---|
| Kagi LLM Benchmark | 58.6% | 37.8% |
| CritPt | 1.1% | 0% |
| LMArena Hard Prompts | 1364 | 1336 |
| DTBench | 76.3% | 70.9% |
| LMCA | 22.1% | 20.6% |
| SimpleBench | 22.1% | — |
| Chess Puzzles | 20% | — |
| LiveBench Reasoning | — | 44.8% |
| Mystery Game Puzzles | 2% | — |
| LiveBench Data Analysis | — | 53.7% |
| Surface Evolver Bench | 25% | — |
| Epoch Capabilities Index | 139.94 | — |
| LiveBench | — | 44% |
Math gpt-oss-120b leads
gpt-oss-120b: 50.6 (#43), Mistral Small: 15.4 (#287)
| Benchmark | gpt-oss-120b | Mistral Small |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.9% | 5.8% |
| LMArena Math | 1388 | 1341 |
| Omni-MATH | 68.8% | — |
| LiveBench Math | — | 39.9% |
| MATH Level 5 | — | 46.8% |
Knowledge gpt-oss-120b leads
gpt-oss-120b: 42.3 (#89), Mistral Small: 30.9 (#214)
| Benchmark | gpt-oss-120b | Mistral Small |
|---|---|---|
| GPQA Diamond | 75.8% | 47.5% |
| Vectara Hallucination Rate | 14.2% | 5.1% |
| LMArena Expert | 1356 | 1293 |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| GPQA (HELM) | 68.4% | — |
| MMLU | — | 68.7% |
Multimodal Not comparable
gpt-oss-120b: —, Mistral Small: 32.9 (#91)
| Benchmark | gpt-oss-120b | Mistral Small |
|---|---|---|
| LMArena Vision | — | 1142 |
Multilingual gpt-oss-120b leads
gpt-oss-120b: 48.2 (#142), Mistral Small: 45.7 (#164)
| Benchmark | gpt-oss-120b | Mistral Small |
|---|---|---|
| LMArena Non-English | 1350 | 1315 |
| LMArena Chinese | 1384 | 1342 |
| LMArena French | 1370 | 1337 |
| LMArena German | 1355 | 1341 |
| LMArena Japanese | 1332 | 1275 |
| LMArena Korean | 1283 | 1260 |
| LMArena Russian | 1343 | 1324 |
| LMArena Spanish | 1388 | 1346 |
Instruction Following gpt-oss-120b leads
gpt-oss-120b: 69.5 (#168), Mistral Small: 66.6 (#204)
| Benchmark | gpt-oss-120b | Mistral Small |
|---|---|---|
| LMArena Instruction Following | 1318 | 1310 |
| LiveBench Instruction Following | — | 63.7% |
| IFEval | 83.6% | — |
Long Context Mistral Small leads
gpt-oss-120b: 31.4 (#273), Mistral Small: 40.4 (#151)
| Benchmark | gpt-oss-120b | Mistral Small |
|---|---|---|
| LMArena Longer Query | 1319 | 1327 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference Mistral Small leads
gpt-oss-120b: 46.7 (#213), Mistral Small: 52.6 (#165)
| Benchmark | gpt-oss-120b | Mistral Small |
|---|---|---|
| LMArena Text | 1366 | 1338 |
| LMArena Creative Writing | 1274 | 1304 |
| LMArena Multi-Turn | 1340 | 1344 |
| Short-Story Creative Writing | 77.1% | — |
| EQ-Bench Creative Writing | 961 | — |
| WildBench | 84.5% | — |
| LiveBench Language | — | 30.5% |
Frequently asked questions
Is gpt-oss-120b better than Mistral Small?
gpt-oss-120b is the stronger model overall, scoring 36.0 to 33.1 on the Noometry Index.
Which is cheaper, gpt-oss-120b or Mistral Small?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Mistral Small lists at $0.15 and $0.60.
Is gpt-oss-120b or Mistral Small better for coding?
They score almost the same on coding (33.2 vs 33.5); test both on your own repository before choosing.
Which has the bigger context window?
Mistral Small does, with 256K tokens against 131K.
How many benchmarks do gpt-oss-120b and Mistral Small share?
26 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and Mistral Small has 39.