Model comparison
gpt-oss-120b vs Mistral Small 3.2
gpt-oss-120b is the stronger model overall, scoring 36.3 to 31.2 on the Noometry Index.
Last verified . 6 shared benchmarks.
Mistral AISummary
- They share 6 benchmarks with published results for both. gpt-oss-120b scores higher in 4 categories and Mistral Small 3.2 in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 52.5 to 26.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 88.9% for gpt-oss-120b and 30.3% for Mistral Small 3.2.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.0938 / $0.25 for Mistral Small 3.2.
- Mistral Small 3.2 accepts more context: 256K tokens versus 131K.
Side by side
| gpt-oss-120b | Mistral Small 3.2 | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 36.3 | 31.2 |
| Released | 2025-08-05 | 2025-06-20 |
| Weights | Open | Open |
| Context window | 131K | 256K |
| Max output | 41K | 16K |
| Input $ / M tokens | $0.037 | $0.0938 |
| Output $ / M tokens | $0.17 | $0.25 |
| Results tracked | 48 | 6 |
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Category by category
Coding Not comparable
gpt-oss-120b: 33.5 (#256), Mistral Small 3.2: —
| Benchmark | gpt-oss-120b | Mistral Small 3.2 |
|---|---|---|
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| SciCode | 36% | — |
| WeirdML | 48.2% | — |
| LMArena Coding | 1380 | — |
| ALE-Bench | 575.62 | — |
| AlgoTune | 1.41 | — |
Agentic & Tool Use Not comparable
gpt-oss-120b: 12.2 (#153), Mistral Small 3.2: —
| Benchmark | gpt-oss-120b | Mistral Small 3.2 |
|---|---|---|
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| METR Time Horizons | 56.6% | — |
| Vending-Bench 2 | -21.53 | — |
Reasoning gpt-oss-120b leads
gpt-oss-120b: 20.0 (#245), Mistral Small 3.2: 18.1 (#287)
| Benchmark | gpt-oss-120b | Mistral Small 3.2 |
|---|---|---|
| Kagi LLM Benchmark | 58.6% | 40.4% |
| Chess Puzzles | 20% | 1% |
| Epoch Capabilities Index | 139.93 | 131.74 |
| SimpleBench | 22.1% | — |
| CritPt | 1.1% | — |
| LMArena Hard Prompts | 1364 | — |
| 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 3.2: 26.3 (#260)
| Benchmark | gpt-oss-120b | Mistral Small 3.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.9% | 30.3% |
| Omni-MATH | 68.8% | — |
| LMArena Math | 1389 | — |
Knowledge gpt-oss-120b leads
gpt-oss-120b: 42.4 (#96), Mistral Small 3.2: 26.7 (#256)
| Benchmark | gpt-oss-120b | Mistral Small 3.2 |
|---|---|---|
| GPQA Diamond | 75.8% | 49.1% |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| Vectara Hallucination Rate | 14.2% | — |
| GPQA (HELM) | 68.4% | — |
| LMArena Expert | 1356 | — |
Multilingual Not comparable
gpt-oss-120b: 48.0 (#147), Mistral Small 3.2: —
| Benchmark | gpt-oss-120b | Mistral Small 3.2 |
|---|---|---|
| LMArena Non-English | 1351 | — |
| LMArena Chinese | 1385 | — |
| LMArena French | 1369 | — |
| LMArena German | 1353 | — |
| LMArena Japanese | 1331 | — |
| LMArena Korean | 1282 | — |
| LMArena Russian | 1343 | — |
| LMArena Spanish | 1389 | — |
Instruction Following Not comparable
gpt-oss-120b: 69.3 (#173), Mistral Small 3.2: —
| Benchmark | gpt-oss-120b | Mistral Small 3.2 |
|---|---|---|
| IFEval | 83.6% | — |
| LMArena Instruction Following | 1318 | — |
Long Context Not comparable
gpt-oss-120b: 31.4 (#278), Mistral Small 3.2: —
| Benchmark | gpt-oss-120b | Mistral Small 3.2 |
|---|---|---|
| Fiction.LiveBench | 44.4% | — |
| LMArena Longer Query | 1319 | — |
Writing & Preference gpt-oss-120b leads
gpt-oss-120b: 46.5 (#217), Mistral Small 3.2: 45.0 (#224)
| Benchmark | gpt-oss-120b | Mistral Small 3.2 |
|---|---|---|
| EQ-Bench Creative Writing | 961 | 1255 |
| LMArena Text | 1365 | — |
| LMArena Creative Writing | 1275 | — |
| Short-Story Creative Writing | 77.1% | — |
| WildBench | 84.5% | — |
| LMArena Multi-Turn | 1340 | — |
Frequently asked questions
Is gpt-oss-120b better than Mistral Small 3.2?
gpt-oss-120b is the stronger model overall, scoring 36.3 to 31.2 on the Noometry Index.
Which is cheaper, gpt-oss-120b or Mistral Small 3.2?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Mistral Small 3.2 lists at $0.0938 and $0.25.
Which has the bigger context window?
Mistral Small 3.2 does, with 256K tokens against 131K.
How many benchmarks do gpt-oss-120b and Mistral Small 3.2 share?
6 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and Mistral Small 3.2 has 6.