Model comparison
Codestral vs gpt-oss-120b
gpt-oss-120b is the stronger model overall, scoring 36.3 to 30.6 on the Noometry Index.
Last verified . 3 shared benchmarks.
Mistral AISummary
- They share 3 benchmarks with published results for both. Codestral scores higher in 0 categories and gpt-oss-120b in 2 categories; one gap is clear of the uncertainty.
- The widest gap is in coding, where gpt-oss-120b leads 33.5 to 27.3.
- The biggest single-benchmark swing is Aider Polyglot: 11.1% for Codestral and 41.8% for gpt-oss-120b.
- gpt-oss-120b is cheaper at $0.037 / $0.17 per million input/output tokens, against $0.30 / $0.90 for Codestral.
- Codestral accepts more context: 256K tokens versus 131K.
- gpt-oss-120b has downloadable open weights; the other is API-only.
Side by side
| Codestral | gpt-oss-120b | |
|---|---|---|
| Provider | Mistral AI | OpenAI |
| Noometry Index | 30.6 | 36.3 |
| Released | 2024-05-29 | 2025-08-05 |
| Weights | Proprietary | Open |
| Context window | 256K | 131K |
| Max output | 8K | 41K |
| Input $ / M tokens | $0.30 | $0.037 |
| Output $ / M tokens | $0.90 | $0.17 |
| Results tracked | 7 | 48 |
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Category by category
Coding gpt-oss-120b leads
Codestral: 27.3 (#321), gpt-oss-120b: 33.5 (#256)
| Benchmark | Codestral | gpt-oss-120b |
|---|---|---|
| Aider Polyglot | 11.1% | 41.8% |
| ALE-Bench | 137.78 | 575.62 |
| SWE-bench Verified (bash only) | — | 26% |
| SciCode | — | 36% |
| WeirdML | — | 48.2% |
| BigCodeBench Instruct | 41.8% | — |
| LMArena Coding | — | 1380 |
| BigCodeBench Complete | 52.5% | — |
| AlgoTune | — | 1.41 |
| HumanEval+ | 73.8% | — |
| MBPP+ | 61.9% | — |
Agentic & Tool Use Not comparable
Codestral: —, gpt-oss-120b: 12.2 (#153)
| Benchmark | Codestral | gpt-oss-120b |
|---|---|---|
| Terminal-Bench | — | 18.7% |
| APEX-Agents | — | 4.4% |
| METR Time Horizons | — | 56.6% |
| Vending-Bench 2 | — | -21.53 |
Reasoning Too close to call
Codestral: 19.8 (#251), gpt-oss-120b: 20.0 (#245)
| Benchmark | Codestral | gpt-oss-120b |
|---|---|---|
| Kagi LLM Benchmark | 32.5% | 58.6% |
| SimpleBench | — | 22.1% |
| CritPt | — | 1.1% |
| Chess Puzzles | — | 20% |
| LMArena Hard Prompts | — | 1364 |
| Mystery Game Puzzles | — | 2% |
| DTBench | — | 76.3% |
| LMCA | — | 22.1% |
| Surface Evolver Bench | — | 25% |
| Epoch Capabilities Index | — | 139.93 |
Math Not comparable
Codestral: —, gpt-oss-120b: 52.5 (#50)
| Benchmark | Codestral | gpt-oss-120b |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 88.9% |
| Omni-MATH | — | 68.8% |
| LMArena Math | — | 1389 |
Knowledge Not comparable
Codestral: —, gpt-oss-120b: 42.4 (#96)
| Benchmark | Codestral | gpt-oss-120b |
|---|---|---|
| GPQA Diamond | — | 75.8% |
| MMLU-Pro | — | 79.5% |
| Confabulations | — | 15.7% |
| Vectara Hallucination Rate | — | 14.2% |
| GPQA (HELM) | — | 68.4% |
| LMArena Expert | — | 1356 |
Multilingual Not comparable
Codestral: —, gpt-oss-120b: 48.0 (#147)
| Benchmark | Codestral | gpt-oss-120b |
|---|---|---|
| LMArena Non-English | — | 1351 |
| LMArena Chinese | — | 1385 |
| LMArena French | — | 1369 |
| LMArena German | — | 1353 |
| LMArena Japanese | — | 1331 |
| LMArena Korean | — | 1282 |
| LMArena Russian | — | 1343 |
| LMArena Spanish | — | 1389 |
Instruction Following Not comparable
Codestral: —, gpt-oss-120b: 69.3 (#173)
| Benchmark | Codestral | gpt-oss-120b |
|---|---|---|
| IFEval | — | 83.6% |
| LMArena Instruction Following | — | 1318 |
Long Context Not comparable
Codestral: —, gpt-oss-120b: 31.4 (#278)
| Benchmark | Codestral | gpt-oss-120b |
|---|---|---|
| Fiction.LiveBench | — | 44.4% |
| LMArena Longer Query | — | 1319 |
Writing & Preference Not comparable
Codestral: —, gpt-oss-120b: 46.5 (#217)
| Benchmark | Codestral | gpt-oss-120b |
|---|---|---|
| LMArena Text | — | 1365 |
| LMArena Creative Writing | — | 1275 |
| Short-Story Creative Writing | — | 77.1% |
| EQ-Bench Creative Writing | — | 961 |
| WildBench | — | 84.5% |
| LMArena Multi-Turn | — | 1340 |
Frequently asked questions
Is Codestral better than gpt-oss-120b?
gpt-oss-120b is the stronger model overall, scoring 36.3 to 30.6 on the Noometry Index.
Which is cheaper, Codestral or gpt-oss-120b?
gpt-oss-120b is cheaper. It lists at $0.037 per million input tokens and $0.17 per million output tokens; Codestral lists at $0.30 and $0.90.
Is Codestral or gpt-oss-120b better for coding?
gpt-oss-120b scores higher on coding benchmarks: 33.5 versus 27.3 in the Noometry coding category.
Which has the bigger context window?
Codestral does, with 256K tokens against 131K.
How many benchmarks do Codestral and gpt-oss-120b share?
3 benchmarks have published results for both models. Codestral has 7 scored results on Noometry and gpt-oss-120b has 48.