Model comparison
gpt-oss-120b vs Mistral Medium
gpt-oss-120b and Mistral Medium score almost the same on the Noometry Index (36.3 vs 36.3), so choose on price, context window or the category you care about most.
Last verified . 29 shared benchmarks.
Mistral AISummary
- They share 29 benchmarks with published results for both. gpt-oss-120b scores higher in 2 categories and Mistral Medium in 7 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 52.5 to 28.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 88.9% for gpt-oss-120b and 32.2% for Mistral Medium.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $1.50 / $7.50 for Mistral Medium.
- Mistral Medium accepts more context: 262K tokens versus 131K.
Side by side
| gpt-oss-120b | Mistral Medium | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 36.3 | 36.3 |
| Released | 2025-08-05 | 2023-12-11 |
| Weights | Open | Open |
| Context window | 131K | 262K |
| Max output | 41K | 262K |
| Input $ / M tokens | $0.037 | $1.50 |
| Output $ / M tokens | $0.17 | $7.50 |
| Results tracked | 48 | 36 |
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Category by category
Coding Too close to call
gpt-oss-120b: 33.5 (#256), Mistral Medium: 34.2 (#243)
| Benchmark | gpt-oss-120b | Mistral Medium |
|---|---|---|
| SciCode | 36% | 40.2% |
| WeirdML | 48.2% | 43.7% |
| LMArena Coding | 1380 | 1434 |
| ALE-Bench | 575.62 | 763.98 |
| FrontierCode | — | 8% |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| AlgoTune | 1.41 | — |
Agentic & Tool Use Mistral Medium leads
gpt-oss-120b: 12.2 (#153), Mistral Medium: 28.3 (#90)
| Benchmark | gpt-oss-120b | Mistral Medium |
|---|---|---|
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| Berkeley Function Calling Leaderboard | — | 37.7% |
| METR Time Horizons | 56.6% | — |
| Vending-Bench 2 | -21.53 | — |
Reasoning Mistral Medium leads
gpt-oss-120b: 20.0 (#245), Mistral Medium: 24.0 (#167)
| Benchmark | gpt-oss-120b | Mistral Medium |
|---|---|---|
| Kagi LLM Benchmark | 58.6% | 50% |
| CritPt | 1.1% | 0% |
| LMArena Hard Prompts | 1364 | 1426 |
| DTBench | 76.3% | 75.5% |
| LMCA | 22.1% | 26.1% |
| Surface Evolver Bench | 25% | 26.9% |
| SimpleBench | 22.1% | — |
| Chess Puzzles | 20% | — |
| Mystery Game Puzzles | 2% | — |
| Epoch Capabilities Index | 139.93 | — |
Math gpt-oss-120b leads
gpt-oss-120b: 52.5 (#50), Mistral Medium: 28.1 (#245)
| Benchmark | gpt-oss-120b | Mistral Medium |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.9% | 32.2% |
| LMArena Math | 1389 | 1408 |
| ProofBench | — | 9% |
| Omni-MATH | 68.8% | — |
| MATH Level 5 | — | 81.6% |
| FrontierMath (Feb 2025 set) | — | 0.3% |
Knowledge gpt-oss-120b leads
gpt-oss-120b: 42.4 (#96), Mistral Medium: 25.0 (#265)
| Benchmark | gpt-oss-120b | Mistral Medium |
|---|---|---|
| GPQA Diamond | 75.8% | 59.5% |
| Vectara Hallucination Rate | 14.2% | 22.7% |
| LMArena Expert | 1356 | 1408 |
| Humanity's Last Exam | — | 4.5% |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| GPQA (HELM) | 68.4% | — |
Multimodal Not comparable
gpt-oss-120b: —, Mistral Medium: 35.3 (#88)
| Benchmark | gpt-oss-120b | Mistral Medium |
|---|---|---|
| LMArena Vision | — | 1172 |
Multilingual Mistral Medium leads
gpt-oss-120b: 48.0 (#147), Mistral Medium: 52.1 (#91)
| Benchmark | gpt-oss-120b | Mistral Medium |
|---|---|---|
| LMArena Non-English | 1351 | 1408 |
| LMArena Chinese | 1385 | 1447 |
| LMArena French | 1369 | 1459 |
| LMArena German | 1353 | 1432 |
| LMArena Japanese | 1331 | 1378 |
| LMArena Korean | 1282 | 1380 |
| LMArena Russian | 1343 | 1411 |
| LMArena Spanish | 1389 | 1433 |
Instruction Following Mistral Medium leads
gpt-oss-120b: 69.3 (#173), Mistral Medium: 73.7 (#116)
| Benchmark | gpt-oss-120b | Mistral Medium |
|---|---|---|
| LMArena Instruction Following | 1318 | 1398 |
| IFEval | 83.6% | — |
Long Context Mistral Medium leads
gpt-oss-120b: 31.4 (#278), Mistral Medium: 42.9 (#114)
| Benchmark | gpt-oss-120b | Mistral Medium |
|---|---|---|
| LMArena Longer Query | 1319 | 1406 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference Mistral Medium leads
gpt-oss-120b: 46.5 (#217), Mistral Medium: 60.0 (#103)
| Benchmark | gpt-oss-120b | Mistral Medium |
|---|---|---|
| LMArena Text | 1365 | 1424 |
| LMArena Creative Writing | 1275 | 1391 |
| Short-Story Creative Writing | 77.1% | 77.3% |
| LMArena Multi-Turn | 1340 | 1418 |
| EQ-Bench Creative Writing | 961 | — |
| WildBench | 84.5% | — |
Frequently asked questions
Is gpt-oss-120b better than Mistral Medium?
gpt-oss-120b and Mistral Medium score almost the same on the Noometry Index (36.3 vs 36.3), so choose on price, context window or the category you care about most.
Which is cheaper, gpt-oss-120b or Mistral Medium?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Mistral Medium lists at $1.50 and $7.50.
Is gpt-oss-120b or Mistral Medium better for coding?
They score almost the same on coding (33.5 vs 34.2); test both on your own repository before choosing.
Which has the bigger context window?
Mistral Medium does, with 262K tokens against 131K.
How many benchmarks do gpt-oss-120b and Mistral Medium share?
29 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and Mistral Medium has 36.