Model comparison
gpt-oss-120b vs Mistral Small 3
gpt-oss-120b is the stronger model overall, scoring 36.0 to 30.9 on the Noometry Index.
Last verified . 22 shared benchmarks.
Mistral AISummary
- They share 22 benchmarks with published results for both. gpt-oss-120b scores higher in 6 categories and Mistral Small 3 in 2 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 50.6 to 15.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 88.9% for gpt-oss-120b and 6.7% for Mistral Small 3.
- Mistral Small 3 is cheaper at $0.05 / $0.08 per million input/output tokens, against $0.037 / $0.17 for gpt-oss-120b.
- gpt-oss-120b accepts more context: 131K tokens versus 33K.
Side by side
| gpt-oss-120b | Mistral Small 3 | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 36.0 | 30.9 |
| Released | 2025-08-05 | 2025-01-30 |
| Weights | Open | Open |
| Context window | 131K | 33K |
| Max output | 41K | 16K |
| Input $ / M tokens | $0.037 | $0.05 |
| Output $ / M tokens | $0.17 | $0.08 |
| Results tracked | 48 | 24 |
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Category by category
Coding Mistral Small 3 leads
gpt-oss-120b: 33.2 (#251), Mistral Small 3: 36.0 (#203)
| Benchmark | gpt-oss-120b | Mistral Small 3 |
|---|---|---|
| LMArena Coding | 1380 | 1246 |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| SciCode | 36% | — |
| WeirdML | 48.2% | — |
| BigCodeBench Instruct | — | 45.3% |
| BigCodeBench Complete | — | 50.4% |
| ALE-Bench | 575.62 | — |
| AlgoTune | 1.41 | — |
Agentic & Tool Use Not comparable
gpt-oss-120b: 12.1 (#145), Mistral Small 3: —
| Benchmark | gpt-oss-120b | Mistral Small 3 |
|---|---|---|
| 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: 19.5 (#236), Mistral Small 3: 18.2 (#269)
| Benchmark | gpt-oss-120b | Mistral Small 3 |
|---|---|---|
| Chess Puzzles | 20% | 0% |
| LMArena Hard Prompts | 1364 | 1233 |
| Epoch Capabilities Index | 139.94 | 127.06 |
| 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: 50.6 (#43), Mistral Small 3: 15.3 (#290)
| Benchmark | gpt-oss-120b | Mistral Small 3 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.9% | 6.7% |
| LMArena Math | 1388 | 1240 |
| Omni-MATH | 68.8% | — |
Knowledge gpt-oss-120b leads
gpt-oss-120b: 42.3 (#89), Mistral Small 3: 25.0 (#258)
| Benchmark | gpt-oss-120b | Mistral Small 3 |
|---|---|---|
| GPQA Diamond | 75.8% | 47.3% |
| Confabulations | 15.7% | 25.2% |
| LMArena Expert | 1356 | 1202 |
| MMLU-Pro | 79.5% | — |
| Vectara Hallucination Rate | 14.2% | — |
| GPQA (HELM) | 68.4% | — |
Multilingual gpt-oss-120b leads
gpt-oss-120b: 48.2 (#142), Mistral Small 3: 37.5 (#231)
| Benchmark | gpt-oss-120b | Mistral Small 3 |
|---|---|---|
| LMArena Non-English | 1350 | 1198 |
| LMArena Chinese | 1384 | 1204 |
| LMArena French | 1370 | 1202 |
| LMArena German | 1355 | 1211 |
| LMArena Japanese | 1332 | 1111 |
| LMArena Korean | 1283 | 1188 |
| LMArena Russian | 1343 | 1216 |
| LMArena Spanish | 1388 | — |
Instruction Following gpt-oss-120b leads
gpt-oss-120b: 69.5 (#168), Mistral Small 3: 63.9 (#224)
| Benchmark | gpt-oss-120b | Mistral Small 3 |
|---|---|---|
| LMArena Instruction Following | 1318 | 1214 |
| IFEval | 83.6% | — |
Long Context Mistral Small 3 leads
gpt-oss-120b: 31.4 (#273), Mistral Small 3: 37.8 (#206)
| Benchmark | gpt-oss-120b | Mistral Small 3 |
|---|---|---|
| LMArena Longer Query | 1319 | 1246 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference gpt-oss-120b leads
gpt-oss-120b: 46.7 (#213), Mistral Small 3: 32.4 (#275)
| Benchmark | gpt-oss-120b | Mistral Small 3 |
|---|---|---|
| LMArena Text | 1366 | 1234 |
| LMArena Creative Writing | 1274 | 1195 |
| EQ-Bench Creative Writing | 961 | 707 |
| LMArena Multi-Turn | 1340 | 1217 |
| Short-Story Creative Writing | 77.1% | — |
| WildBench | 84.5% | — |
Frequently asked questions
Is gpt-oss-120b better than Mistral Small 3?
gpt-oss-120b is the stronger model overall, scoring 36.0 to 30.9 on the Noometry Index.
Which is cheaper, gpt-oss-120b or Mistral Small 3?
Mistral Small 3 is cheaper. It lists at $0.05 per million input tokens and $0.08 per million output tokens; gpt-oss-120b lists at $0.037 and $0.17.
Is gpt-oss-120b or Mistral Small 3 better for coding?
Mistral Small 3 scores higher on coding benchmarks: 36.0 versus 33.2 in the Noometry coding category.
Which has the bigger context window?
gpt-oss-120b does, with 131K tokens against 33K.
How many benchmarks do gpt-oss-120b and Mistral Small 3 share?
22 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and Mistral Small 3 has 24.