Model comparison
gpt-oss-120b vs Mistral Large 4
Mistral Large 4 is the stronger model overall, scoring 43.1 to 36.3 on the Noometry Index. gpt-oss-120b costs 15× less per token, which makes it the better buy when Mistral Large 4's lead doesn't matter for your workload.
Last verified . 12 shared benchmarks.
Mistral AISummary
- They share 12 benchmarks with published results for both. gpt-oss-120b scores higher in 2 categories and Mistral Large 4 in 6 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where Mistral Large 4 leads 48.6 to 33.5.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.68 / $2.09 for Mistral Large 4.
- Mistral Large 4 accepts more context: 1.05M tokens versus 131K.
- gpt-oss-120b has downloadable open weights; the other is API-only.
Side by side
| gpt-oss-120b | Mistral Large 4 | |
|---|---|---|
| Provider | OpenAI | Mistral AI |
| Noometry Index | 36.3 | 43.1 |
| Released | 2025-08-05 | 2026-10-06 |
| Weights | Open | Proprietary |
| Context window | 131K | 1.05M |
| Max output | 41K | 262K |
| Input $ / M tokens | $0.037 | $0.68 |
| Output $ / M tokens | $0.17 | $2.09 |
| Results tracked | 48 | 15 |
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Category by category
Coding Mistral Large 4 leads
gpt-oss-120b: 33.5 (#256), Mistral Large 4: 48.6 (#57)
| Benchmark | gpt-oss-120b | Mistral Large 4 |
|---|---|---|
| LMArena Coding | 1380 | 1475 |
| SWE-bench Verified (bash only) | 26% | — |
| Aider Polyglot | 41.8% | — |
| LMArena WebDev | — | 1541 |
| SciCode | 36% | — |
| WeirdML | 48.2% | — |
| ALE-Bench | 575.62 | — |
| AlgoTune | 1.41 | — |
Agentic & Tool Use Not comparable
gpt-oss-120b: 12.2 (#153), Mistral Large 4: —
| Benchmark | gpt-oss-120b | Mistral Large 4 |
|---|---|---|
| Terminal-Bench | 18.7% | — |
| APEX-Agents | 4.4% | — |
| METR Time Horizons | 56.6% | — |
| Vending-Bench 2 | -21.53 | — |
Reasoning Mistral Large 4 leads
gpt-oss-120b: 20.0 (#245), Mistral Large 4: 22.5 (#192)
| Benchmark | gpt-oss-120b | Mistral Large 4 |
|---|---|---|
| LMArena Hard Prompts | 1364 | 1444 |
| SimpleBench | 22.1% | — |
| Kagi LLM Benchmark | 58.6% | — |
| NYT Connections (extended) | — | 27.4% |
| CritPt | 1.1% | — |
| Chess Puzzles | 20% | — |
| Mystery Game Puzzles | 2% | — |
| DTBench | 76.3% | — |
| LMCA | 22.1% | — |
| Surface Evolver Bench | 25% | — |
| Epoch Capabilities Index | 139.93 | — |
Math gpt-oss-120b leads
gpt-oss-120b: 52.5 (#50), Mistral Large 4: 40.4 (#91)
| Benchmark | gpt-oss-120b | Mistral Large 4 |
|---|---|---|
| LMArena Math | 1389 | 1488 |
| OTIS Mock AIME 2024-2025 | 88.9% | — |
| Omni-MATH | 68.8% | — |
Knowledge gpt-oss-120b leads
gpt-oss-120b: 42.4 (#96), Mistral Large 4: 36.6 (#166)
| Benchmark | gpt-oss-120b | Mistral Large 4 |
|---|---|---|
| LMArena Expert | 1356 | 1447 |
| GPQA Diamond | 75.8% | — |
| SimpleQA Verified | — | 20% |
| MMLU-Pro | 79.5% | — |
| Confabulations | 15.7% | — |
| Vectara Hallucination Rate | 14.2% | — |
| GPQA (HELM) | 68.4% | — |
Multilingual Mistral Large 4 leads
gpt-oss-120b: 48.0 (#147), Mistral Large 4: 52.6 (#82)
| Benchmark | gpt-oss-120b | Mistral Large 4 |
|---|---|---|
| LMArena Non-English | 1351 | 1415 |
| LMArena Chinese | 1385 | 1491 |
| LMArena Russian | 1343 | 1414 |
| LMArena French | 1369 | — |
| LMArena German | 1353 | — |
| LMArena Japanese | 1331 | — |
| LMArena Korean | 1282 | — |
| LMArena Spanish | 1389 | — |
Instruction Following Mistral Large 4 leads
gpt-oss-120b: 69.3 (#173), Mistral Large 4: 75.0 (#76)
| Benchmark | gpt-oss-120b | Mistral Large 4 |
|---|---|---|
| LMArena Instruction Following | 1318 | 1424 |
| IFEval | 83.6% | — |
Long Context Mistral Large 4 leads
gpt-oss-120b: 31.4 (#278), Mistral Large 4: 43.6 (#89)
| Benchmark | gpt-oss-120b | Mistral Large 4 |
|---|---|---|
| LMArena Longer Query | 1319 | 1429 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference Mistral Large 4 leads
gpt-oss-120b: 46.5 (#217), Mistral Large 4: 60.4 (#97)
| Benchmark | gpt-oss-120b | Mistral Large 4 |
|---|---|---|
| LMArena Text | 1365 | 1427 |
| LMArena Creative Writing | 1275 | 1361 |
| LMArena Multi-Turn | 1340 | 1424 |
| Short-Story Creative Writing | 77.1% | — |
| EQ-Bench Creative Writing | 961 | — |
| WildBench | 84.5% | — |
Frequently asked questions
Is gpt-oss-120b better than Mistral Large 4?
Mistral Large 4 is the stronger model overall, scoring 43.1 to 36.3 on the Noometry Index. gpt-oss-120b costs 15× less per token, which makes it the better buy when Mistral Large 4's lead doesn't matter for your workload.
Which is cheaper, gpt-oss-120b or Mistral Large 4?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Mistral Large 4 lists at $0.68 and $2.09.
Is gpt-oss-120b or Mistral Large 4 better for coding?
Mistral Large 4 scores higher on coding benchmarks: 48.6 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
Mistral Large 4 does, with 1.05M tokens against 131K.
How many benchmarks do gpt-oss-120b and Mistral Large 4 share?
12 benchmarks have published results for both models. gpt-oss-120b has 48 scored results on Noometry and Mistral Large 4 has 15.