Model comparison
gpt-oss-120b vs Mixtral 8x7B
gpt-oss-120b is the stronger model overall, scoring 36.0 to 26.8 on the Noometry Index.
Last verified . 25 shared benchmarks.
Mistral AISummary
- They share 25 benchmarks with published results for both. gpt-oss-120b scores higher in 7 categories and Mixtral 8x7B in 1 category; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where gpt-oss-120b leads 50.6 to 17.2.
- The biggest single-benchmark swing is Omni-MATH: 68.8% for gpt-oss-120b and 10.5% for Mixtral 8x7B.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.70 / $0.70 for Mixtral 8x7B.
- gpt-oss-120b accepts more context: 131K tokens versus 32K.
Side by side
| gpt-oss-120b | Mixtral 8x7B | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 36.0 | 26.8 |
| Released | 2025-08-05 | 2023-12-11 |
| Weights | Open | Open |
| Context window | 131K | 32K |
| Max output | 41K | 32K |
| Input $ / M tokens | $0.037 | $0.70 |
| Output $ / M tokens | $0.17 | $0.70 |
| Results tracked | 48 | 38 |
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Category by category
Coding gpt-oss-120b leads
gpt-oss-120b: 33.2 (#251), Mixtral 8x7B: 32.2 (#265)
| Benchmark | gpt-oss-120b | Mixtral 8x7B |
|---|---|---|
| LMArena Coding | 1380 | 1127 |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| SciCode | 36% | — |
| WeirdML | 48.2% | — |
| ALE-Bench | 575.62 | — |
| AlgoTune | 1.41 | — |
| HumanEval+ | — | 39.6% |
| MBPP+ | — | 49.7% |
Agentic & Tool Use Not comparable
gpt-oss-120b: 12.1 (#145), Mixtral 8x7B: —
| Benchmark | gpt-oss-120b | Mixtral 8x7B |
|---|---|---|
| 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), Mixtral 8x7B: 17.4 (#283)
| Benchmark | gpt-oss-120b | Mixtral 8x7B |
|---|---|---|
| LMArena Hard Prompts | 1364 | 1115 |
| DTBench | 76.3% | 49.6% |
| Epoch Capabilities Index | 139.94 | 118.38 |
| SimpleBench | 22.1% | — |
| Kagi LLM Benchmark | 58.6% | — |
| CritPt | 1.1% | — |
| Chess Puzzles | 20% | — |
| Mystery Game Puzzles | 2% | — |
| LMCA | 22.1% | — |
| Surface Evolver Bench | 25% | — |
| Adversarial NLI | — | 55.2% |
| ForecastBench | — | 56.3 |
| HellaSwag | — | 86.7% |
| PIQA | — | 83.6% |
| WinoGrande | — | 77.2% |
Math gpt-oss-120b leads
gpt-oss-120b: 50.6 (#43), Mixtral 8x7B: 17.2 (#284)
| Benchmark | gpt-oss-120b | Mixtral 8x7B |
|---|---|---|
| Omni-MATH | 68.8% | 10.5% |
| LMArena Math | 1388 | 1147 |
| OTIS Mock AIME 2024-2025 | 88.9% | — |
| MATH Level 5 | — | 10% |
| GSM8K | — | 74.4% |
Knowledge gpt-oss-120b leads
gpt-oss-120b: 42.3 (#89), Mixtral 8x7B: 11.0 (#296)
| Benchmark | gpt-oss-120b | Mixtral 8x7B |
|---|---|---|
| GPQA Diamond | 75.8% | 30.6% |
| MMLU-Pro | 79.5% | 33.5% |
| GPQA (HELM) | 68.4% | 29.6% |
| LMArena Expert | 1356 | 1088 |
| Confabulations | 15.7% | — |
| Vectara Hallucination Rate | 14.2% | — |
| ARC (AI2) Challenge | — | 87.3% |
| MMLU | — | 70.6% |
| OpenBookQA | — | 85.8% |
| TriviaQA | — | 82.2% |
Multilingual gpt-oss-120b leads
gpt-oss-120b: 48.2 (#142), Mixtral 8x7B: 29.8 (#261)
| Benchmark | gpt-oss-120b | Mixtral 8x7B |
|---|---|---|
| LMArena Non-English | 1350 | 1077 |
| LMArena Chinese | 1384 | 1055 |
| LMArena French | 1370 | 1165 |
| LMArena German | 1355 | 1114 |
| LMArena Japanese | 1332 | 931 |
| LMArena Korean | 1283 | 968 |
| LMArena Russian | 1343 | 1090 |
| LMArena Spanish | 1388 | 1110 |
Instruction Following gpt-oss-120b leads
gpt-oss-120b: 69.5 (#168), Mixtral 8x7B: 51.2 (#292)
| Benchmark | gpt-oss-120b | Mixtral 8x7B |
|---|---|---|
| IFEval | 83.6% | 57.5% |
| LMArena Instruction Following | 1318 | 1110 |
Long Context Mixtral 8x7B leads
gpt-oss-120b: 31.4 (#273), Mixtral 8x7B: 33.4 (#255)
| Benchmark | gpt-oss-120b | Mixtral 8x7B |
|---|---|---|
| LMArena Longer Query | 1319 | 1103 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference gpt-oss-120b leads
gpt-oss-120b: 46.7 (#213), Mixtral 8x7B: 34.4 (#265)
| Benchmark | gpt-oss-120b | Mixtral 8x7B |
|---|---|---|
| LMArena Text | 1366 | 1132 |
| LMArena Creative Writing | 1274 | 1109 |
| WildBench | 84.5% | 67.3% |
| LMArena Multi-Turn | 1340 | 1115 |
| Short-Story Creative Writing | 77.1% | — |
| EQ-Bench Creative Writing | 961 | — |
Frequently asked questions
Is gpt-oss-120b better than Mixtral 8x7B?
gpt-oss-120b is the stronger model overall, scoring 36.0 to 26.8 on the Noometry Index.
Which is cheaper, gpt-oss-120b or Mixtral 8x7B?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Mixtral 8x7B lists at $0.70 and $0.70.
Is gpt-oss-120b or Mixtral 8x7B better for coding?
gpt-oss-120b scores higher on coding benchmarks: 33.2 versus 32.2 in the Noometry coding category.
Which has the bigger context window?
gpt-oss-120b does, with 131K tokens against 32K.
How many benchmarks do gpt-oss-120b and Mixtral 8x7B share?
25 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and Mixtral 8x7B has 38.