Model comparison
gpt-oss-120b vs Mistral 7B
gpt-oss-120b is the stronger model overall, scoring 36.3 to 23.0 on the Noometry Index.
Last verified . 21 shared benchmarks.
Mistral AISummary
- They share 21 benchmarks with published results for both. gpt-oss-120b scores higher in 7 categories and Mistral 7B in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 52.5 to 8.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 88.9% for gpt-oss-120b and 0.3% for Mistral 7B.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.25 / $0.25 for Mistral 7B.
- gpt-oss-120b accepts more context: 131K tokens versus 8K.
Side by side
| gpt-oss-120b | Mistral 7B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 36.3 | 23.0 |
| Released | 2025-08-05 | 2023-09-27 |
| Weights | Open | Open |
| Context window | 131K | 8K |
| Max output | 41K | 8K |
| Input $ / M tokens | $0.037 | $0.25 |
| Output $ / M tokens | $0.17 | $0.25 |
| Results tracked | 48 | 37 |
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Category by category
Coding gpt-oss-120b leads
gpt-oss-120b: 33.5 (#256), Mistral 7B: 26.4 (#326)
| Benchmark | gpt-oss-120b | Mistral 7B |
|---|---|---|
| LMArena Coding | 1380 | 1082 |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| SciCode | 36% | — |
| WeirdML | 48.2% | — |
| BigCodeBench Instruct | — | 19.5% |
| BigCodeBench Complete | — | 27.3% |
| ALE-Bench | 575.62 | — |
| AlgoTune | 1.41 | — |
| HumanEval+ | — | 36% |
| MBPP+ | — | 42.1% |
Agentic & Tool Use Not comparable
gpt-oss-120b: 12.2 (#153), Mistral 7B: —
| Benchmark | gpt-oss-120b | Mistral 7B |
|---|---|---|
| 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 7B: 13.1 (#336)
| Benchmark | gpt-oss-120b | Mistral 7B |
|---|---|---|
| Chess Puzzles | 20% | 0% |
| LMArena Hard Prompts | 1364 | 1067 |
| DTBench | 76.3% | 42.5% |
| Epoch Capabilities Index | 139.93 | 112.21 |
| SimpleBench | 22.1% | — |
| Kagi LLM Benchmark | 58.6% | — |
| CritPt | 1.1% | — |
| Mystery Game Puzzles | 2% | — |
| LMCA | 22.1% | — |
| Surface Evolver Bench | 25% | — |
| Adversarial NLI | — | 47.1% |
| BIG-Bench Hard | — | 56.1% |
| HellaSwag | — | 81% |
| PIQA | — | 83% |
| WinoGrande | — | 75.3% |
Math gpt-oss-120b leads
gpt-oss-120b: 52.5 (#50), Mistral 7B: 8.1 (#325)
| Benchmark | gpt-oss-120b | Mistral 7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 88.9% | 0.3% |
| LMArena Math | 1389 | 1085 |
| Omni-MATH | 68.8% | — |
| MATH Level 5 | — | 3.7% |
| GSM8K | — | 54.4% |
Knowledge gpt-oss-120b leads
gpt-oss-120b: 42.4 (#96), Mistral 7B: 7.4 (#311)
| Benchmark | gpt-oss-120b | Mistral 7B |
|---|---|---|
| GPQA Diamond | 75.8% | 15.2% |
| LMArena Expert | 1356 | 1036 |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| Vectara Hallucination Rate | 14.2% | — |
| GPQA (HELM) | 68.4% | — |
| ARC (AI2) Challenge | — | 78.6% |
| BoolQ | — | 87.4% |
| MMLU | — | 62.5% |
| OpenBookQA | — | 79.8% |
| TriviaQA | — | 75.2% |
Multilingual gpt-oss-120b leads
gpt-oss-120b: 48.0 (#147), Mistral 7B: 25.8 (#283)
| Benchmark | gpt-oss-120b | Mistral 7B |
|---|---|---|
| LMArena Non-English | 1351 | 1012 |
| LMArena Chinese | 1385 | 1009 |
| LMArena French | 1369 | 1037 |
| LMArena German | 1353 | 987 |
| LMArena Japanese | 1331 | 878 |
| LMArena Russian | 1343 | 1018 |
| LMArena Spanish | 1389 | 1026 |
| LMArena Korean | 1282 | — |
Instruction Following gpt-oss-120b leads
gpt-oss-120b: 69.3 (#173), Mistral 7B: 54.2 (#280)
| Benchmark | gpt-oss-120b | Mistral 7B |
|---|---|---|
| LMArena Instruction Following | 1318 | 1060 |
| IFEval | 83.6% | — |
Long Context Too close to call
gpt-oss-120b: 31.4 (#278), Mistral 7B: 32.2 (#271)
| Benchmark | gpt-oss-120b | Mistral 7B |
|---|---|---|
| LMArena Longer Query | 1319 | 1060 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference gpt-oss-120b leads
gpt-oss-120b: 46.5 (#217), Mistral 7B: 30.7 (#286)
| Benchmark | gpt-oss-120b | Mistral 7B |
|---|---|---|
| LMArena Text | 1365 | 1090 |
| LMArena Creative Writing | 1275 | 1068 |
| LMArena Multi-Turn | 1340 | 1062 |
| Short-Story Creative Writing | 77.1% | — |
| EQ-Bench Creative Writing | 961 | — |
| WildBench | 84.5% | — |
Frequently asked questions
Is gpt-oss-120b better than Mistral 7B?
gpt-oss-120b is the stronger model overall, scoring 36.3 to 23.0 on the Noometry Index.
Which is cheaper, gpt-oss-120b or Mistral 7B?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Mistral 7B lists at $0.25 and $0.25.
Is gpt-oss-120b or Mistral 7B better for coding?
gpt-oss-120b scores higher on coding benchmarks: 33.5 versus 26.4 in the Noometry coding category.
Which has the bigger context window?
gpt-oss-120b does, with 131K tokens against 8K.
How many benchmarks do gpt-oss-120b and Mistral 7B share?
21 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and Mistral 7B has 37.