Model comparison
gpt-oss-120b vs Mistral Small 3.1
gpt-oss-120b is the stronger model overall, scoring 36.3 to 31.7 on the Noometry Index.
Last verified . 27 shared benchmarks.
Mistral AISummary
- They share 27 benchmarks with published results for both. gpt-oss-120b scores higher in 6 categories and Mistral Small 3.1 in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 52.5 to 14.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 88.9% for gpt-oss-120b and 3.9% for Mistral Small 3.1.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.35 / $0.56 for Mistral Small 3.1.
- gpt-oss-120b accepts more context: 131K tokens versus 128K.
Side by side
| gpt-oss-120b | Mistral Small 3.1 | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 36.3 | 31.7 |
| Released | 2025-08-05 | 2025-03-17 |
| Weights | Open | Open |
| Context window | 131K | 128K |
| Max output | 41K | 102K |
| Input $ / M tokens | $0.037 | $0.35 |
| Output $ / M tokens | $0.17 | $0.56 |
| Results tracked | 48 | 28 |
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Category by category
Coding Mistral Small 3.1 leads
gpt-oss-120b: 33.5 (#256), Mistral Small 3.1: 38.3 (#179)
| Benchmark | gpt-oss-120b | Mistral Small 3.1 |
|---|---|---|
| LMArena Coding | 1380 | 1309 |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| SciCode | 36% | — |
| WeirdML | 48.2% | — |
| ALE-Bench | 575.62 | — |
| AlgoTune | 1.41 | — |
Agentic & Tool Use Not comparable
gpt-oss-120b: 12.2 (#153), Mistral Small 3.1: —
| Benchmark | gpt-oss-120b | Mistral Small 3.1 |
|---|---|---|
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| METR Time Horizons | 56.6% | — |
| Vending-Bench 2 | -21.53 | — |
Reasoning Too close to call
gpt-oss-120b: 20.0 (#245), Mistral Small 3.1: 19.7 (#254)
| Benchmark | gpt-oss-120b | Mistral Small 3.1 |
|---|---|---|
| Chess Puzzles | 20% | 1% |
| LMArena Hard Prompts | 1364 | 1278 |
| Epoch Capabilities Index | 139.93 | 127.48 |
| SimpleBench | 22.1% | — |
| Kagi LLM Benchmark | 58.6% | — |
| CritPt | 1.1% | — |
| Mystery Game Puzzles | 2% | — |
| DTBench | 76.3% | — |
| LMCA | 22.1% | — |
| Surface Evolver Bench | 25% | — |
Math gpt-oss-120b leads
gpt-oss-120b: 52.5 (#50), Mistral Small 3.1: 14.7 (#301)
| Benchmark | gpt-oss-120b | Mistral Small 3.1 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.9% | 3.9% |
| Omni-MATH | 68.8% | 24.8% |
| LMArena Math | 1389 | 1262 |
Knowledge gpt-oss-120b leads
gpt-oss-120b: 42.4 (#96), Mistral Small 3.1: 22.6 (#271)
| Benchmark | gpt-oss-120b | Mistral Small 3.1 |
|---|---|---|
| GPQA Diamond | 75.8% | 41.9% |
| MMLU-Pro | 79.5% | 61% |
| GPQA (HELM) | 68.4% | 39.2% |
| LMArena Expert | 1356 | 1257 |
| Confabulations | 15.7% | — |
| Vectara Hallucination Rate | 14.2% | — |
Multimodal Not comparable
gpt-oss-120b: —, Mistral Small 3.1: 33.2 (#99)
| Benchmark | gpt-oss-120b | Mistral Small 3.1 |
|---|---|---|
| LMArena Vision | — | 1136 |
Multilingual gpt-oss-120b leads
gpt-oss-120b: 48.0 (#147), Mistral Small 3.1: 41.2 (#209)
| Benchmark | gpt-oss-120b | Mistral Small 3.1 |
|---|---|---|
| LMArena Non-English | 1351 | 1255 |
| LMArena Chinese | 1385 | 1253 |
| LMArena French | 1369 | 1273 |
| LMArena German | 1353 | 1266 |
| LMArena Japanese | 1331 | 1208 |
| LMArena Korean | 1282 | 1206 |
| LMArena Russian | 1343 | 1263 |
| LMArena Spanish | 1389 | 1283 |
Instruction Following gpt-oss-120b leads
gpt-oss-120b: 69.3 (#173), Mistral Small 3.1: 63.6 (#230)
| Benchmark | gpt-oss-120b | Mistral Small 3.1 |
|---|---|---|
| IFEval | 83.6% | 75% |
| LMArena Instruction Following | 1318 | 1264 |
Long Context Mistral Small 3.1 leads
gpt-oss-120b: 31.4 (#278), Mistral Small 3.1: 39.5 (#178)
| Benchmark | gpt-oss-120b | Mistral Small 3.1 |
|---|---|---|
| LMArena Longer Query | 1319 | 1299 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference gpt-oss-120b leads
gpt-oss-120b: 46.5 (#217), Mistral Small 3.1: 37.0 (#259)
| Benchmark | gpt-oss-120b | Mistral Small 3.1 |
|---|---|---|
| LMArena Text | 1365 | 1277 |
| LMArena Creative Writing | 1275 | 1253 |
| EQ-Bench Creative Writing | 961 | 761 |
| WildBench | 84.5% | 78.8% |
| LMArena Multi-Turn | 1340 | 1270 |
| Short-Story Creative Writing | 77.1% | — |
Frequently asked questions
Is gpt-oss-120b better than Mistral Small 3.1?
gpt-oss-120b is the stronger model overall, scoring 36.3 to 31.7 on the Noometry Index.
Which is cheaper, gpt-oss-120b or Mistral Small 3.1?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Mistral Small 3.1 lists at $0.35 and $0.56.
Is gpt-oss-120b or Mistral Small 3.1 better for coding?
Mistral Small 3.1 scores higher on coding benchmarks: 38.3 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
gpt-oss-120b does, with 131K tokens against 128K.
How many benchmarks do gpt-oss-120b and Mistral Small 3.1 share?
27 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and Mistral Small 3.1 has 28.