Codestral 2508 vs gpt-oss-120b
Updated October 21, 2026. 7 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Codestral 2508 has the edge where the context window is larger, 256K against 131K tokens, and where what matters is ALE-Bench or Design Arena: game dev, where it leads.
- Shared results
- 7
- Codestral 2508 only
- 0
- gpt-oss-120b only
- 71
- Like-for-like categories
- 2 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only gpt-oss-120b has a AA Intelligence Index result, 11.6.
Directional only
Agentic work
AA Agentic Index
No call
Only gpt-oss-120b has a AA Agentic Index result, 3.7.
Directional only
Reasoning
GPQA Diamond
No call
Only gpt-oss-120b has a GPQA Diamond result, 78.5%.
Directional only
Math
AIME
No call
Only gpt-oss-120b has a AIME result, 92.6%.
Directional only
Knowledge
MMLU Pro
No call
Only gpt-oss-120b has a MMLU Pro result, 79.2%.
Directional only
Web design
Design Arena: websites
Codestral 2508
Codestral 2508 leads, 1013 against 968.
Same board, both measured
Long documents
Listed context window
Codestral 2508
Codestral 2508 takes 256K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-120b
Codestral 2508 $0.0008, gpt-oss-120b $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only gpt-oss-120b has the number, $0.51.
Directional only
At a glance
- Coding index
- Codestral 2508 not benchmarked, gpt-oss-120b not benchmarked.
- Widest category
- Coding, 14.6 points apart on average over 1 shared benchmark.
- Widest single gap
- Design Arena: 3D: Codestral 2508 1020 against gpt-oss-120b 901, 14.9 points apart on a 0 to 100 scale.
- Output price
- Codestral 2508 $0.90 against gpt-oss-120b $0.50 per million output tokens, 1.8x apart.
- Context window
- Codestral 2508 takes 256K tokens, gpt-oss-120b 131K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | Codestral 2508 | Bars | gpt-oss-120b | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Web design | 5 | 1 | ||
| All shared boards | 5 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Codestral 2508 | gpt-oss-120b | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 41.8% | Only gpt-oss-120b measured |
| SWE-bench Verified | Not measured | 26.0% | Only gpt-oss-120b measured |
| AA Coding Index | Not measured | 30.4 | Only gpt-oss-120b measured |
| LiveCodeBench (Vals AI) | Not measured | 83.2% | Only gpt-oss-120b measured |
| SWE-bench Verified (Vals AI) | Not measured | 33.6% | Only gpt-oss-120b measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 87.8% | Only gpt-oss-120b measured |
| SciCode | Not measured | 34.0% | Only gpt-oss-120b measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 23.5% | Only gpt-oss-120b measured |
| Terminal-Bench | Not measured | 18.7% | Only gpt-oss-120b measured |
| WeirdML | Not measured | 48.2% | Only gpt-oss-120b measured |
| ALE-Bench | 137.8 | 575.6 | gpt-oss-120b |
| LMArena Text: coding | Not measured | 1379 | Only gpt-oss-120b measured |
| LMArena Text: software and IT | Not measured | 1387 | Only gpt-oss-120b measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Codestral 2508 | gpt-oss-120b | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 11.6 | Only gpt-oss-120b measured |
| Epoch Capabilities Index | Not measured | 139.8 | Only gpt-oss-120b measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Codestral 2508 | gpt-oss-120b | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 3.7 | Only gpt-oss-120b measured |
| τ²-Bench Telecom | Not measured | 65.8% | Only gpt-oss-120b measured |
| APEX-Agents | Not measured | 4.4% | Only gpt-oss-120b measured |
| METR time horizons | Not measured | 56.6% | Only gpt-oss-120b measured |
| Vending-Bench 2 | Not measured | -$22 | Only gpt-oss-120b measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | Codestral 2508 | gpt-oss-120b | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 78.5% | Only gpt-oss-120b measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 78.2% | Only gpt-oss-120b measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 19.6% | Only gpt-oss-120b measured |
| GPQA Diamond (Epoch AI run) | Not measured | 75.8% | Only gpt-oss-120b measured |
| SimpleBench | Not measured | 22.1% | Only gpt-oss-120b measured |
| Mystery game puzzles | Not measured | 2.0% | Only gpt-oss-120b measured |
| LMArena Text: hard prompts | Not measured | 1364 | Only gpt-oss-120b measured |
| LMArena Text: expert | Not measured | 1356 | Only gpt-oss-120b measured |
| LMArena Text: hard prompts in English | Not measured | 1368 | Only gpt-oss-120b measured |
| Neither model has a result on the other 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Codestral 2508 | gpt-oss-120b | Leader |
|---|---|---|---|
| AIME | Not measured | 92.6% | Only gpt-oss-120b measured |
| MATH 500 (Vals AI) | Not measured | 94.8% | Only gpt-oss-120b measured |
| MGSM (Vals AI) | Not measured | 92.0% | Only gpt-oss-120b measured |
| AA Math Index | Not measured | 93.4 | Only gpt-oss-120b measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 93.4% | Only gpt-oss-120b measured |
| OTIS Mock AIME | Not measured | 88.9% | Only gpt-oss-120b measured |
| LMArena Text: math | Not measured | 1389 | Only gpt-oss-120b measured |
| LMArena Text: mathematical work | Not measured | 1386 | Only gpt-oss-120b measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | Codestral 2508 | gpt-oss-120b | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 79.2% | Only gpt-oss-120b measured |
| LegalBench (Vals AI) | Not measured | 75.9% | Only gpt-oss-120b measured |
| CaseLaw (Vals AI) | Not measured | 48.8% | Only gpt-oss-120b measured |
| CorpFin (Vals AI) | Not measured | 58.2% | Only gpt-oss-120b measured |
| TaxEval (Vals AI) | Not measured | 71.6% | Only gpt-oss-120b measured |
| MedQA (Vals AI) | Not measured | 91.4% | Only gpt-oss-120b measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 80.8% | Only gpt-oss-120b measured |
| LMArena Text: science | Not measured | 1382 | Only gpt-oss-120b measured |
| LMArena Text: medicine and healthcare | Not measured | 1388 | Only gpt-oss-120b measured |
| LMArena Text: legal and government | Not measured | 1355 | Only gpt-oss-120b measured |
| LMArena Text: business and finance | Not measured | 1359 | Only gpt-oss-120b measured |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index51
| Benchmark | Codestral 2508 | gpt-oss-120b | Leader |
|---|---|---|---|
| Design Arena: websites | 1013 | 968 | Codestral 2508 |
| Design Arena: UI components | 1017 | 927 | Codestral 2508 |
| Design Arena: game dev | 968 | 992 | gpt-oss-120b |
| Design Arena: data viz | 1017 | 987 | Codestral 2508 |
| Design Arena: 3D | 1020 | 901 | Codestral 2508 |
| Design Arena: all code categories | 1007 | 963 | Codestral 2508 |
| Neither model has a result on the other 3 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index24 boards, none shared
| Benchmark | Codestral 2508 | gpt-oss-120b | Leader |
|---|---|---|---|
| IFBench | Not measured | 69.0% | Only gpt-oss-120b measured |
| Long context reasoning (AA-LCR) | Not measured | 52.0% | Only gpt-oss-120b measured |
| DTBench | Not measured | 76.3% | Only gpt-oss-120b measured |
| LMCA | Not measured | 22.1% | Only gpt-oss-120b measured |
| Fiction.LiveBench | Not measured | 44.4% | Only gpt-oss-120b measured |
| Creative writing (Lech Mazur) | Not measured | 77.1% | Only gpt-oss-120b measured |
| Surface Evolver Bench | Not measured | 25.0% | Only gpt-oss-120b measured |
| LMArena Text | Not measured | 1366 | Only gpt-oss-120b measured |
| LMArena Text: instruction following | Not measured | 1318 | Only gpt-oss-120b measured |
| LMArena Text: creative writing | Not measured | 1274 | Only gpt-oss-120b measured |
| LMArena Text: English | Not measured | 1375 | Only gpt-oss-120b measured |
| LMArena Text: non-English | Not measured | 1351 | Only gpt-oss-120b measured |
| LMArena Text: Chinese | Not measured | 1385 | Only gpt-oss-120b measured |
| LMArena Text: German | Not measured | 1353 | Only gpt-oss-120b measured |
| LMArena Text: Japanese | Not measured | 1331 | Only gpt-oss-120b measured |
| LMArena Text: Korean | Not measured | 1282 | Only gpt-oss-120b measured |
| LMArena Text: French | Not measured | 1370 | Only gpt-oss-120b measured |
| LMArena Text: Spanish | Not measured | 1389 | Only gpt-oss-120b measured |
| LMArena Text: Russian | Not measured | 1343 | Only gpt-oss-120b measured |
| LMArena Text: Polish | Not measured | 1353 | Only gpt-oss-120b measured |
| LMArena Text: multi-turn | Not measured | 1340 | Only gpt-oss-120b measured |
| LMArena Text: longer queries | Not measured | 1319 | Only gpt-oss-120b measured |
| LMArena Text: writing and language | Not measured | 1306 | Only gpt-oss-120b measured |
| LMArena Text: entertainment and media | Not measured | 1292 | Only gpt-oss-120b measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Codestral 2508 | gpt-oss-120b | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
gpt-oss-120b costs less
Repository review
50K input + 3K output tokens
gpt-oss-120b costs less
Agent loop
200K input + 10K output tokens
gpt-oss-120b costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Mistral Codestral 2508 | OpenAI gpt-oss-120b |
|---|---|---|
| Maker | Mistral AI | OpenAI |
| Weights | No public weights listed | Open weights |
| Released | August 1, 2025 | August 5, 2025 |
| Context window | 256K | 131K |
| Input $/Mtok | $0.30 | $0.10 |
| Output $/Mtok | $0.90 | $0.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | $0.51 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Codestral 2508 better than gpt-oss-120b for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Codestral 2508 has the edge where the context window is larger, 256K against 131K tokens, and where what matters is ALE-Bench or Design Arena: game dev, where it leads.
Which is cheaper, Codestral 2508 or gpt-oss-120b?
Codestral 2508 costs $0.30 per million input tokens and $0.90 per million output tokens; gpt-oss-120b costs $0.10 and $0.50. Cost per solved task is known for gpt-oss-120b only.
Which has the bigger context window, Codestral 2508 or gpt-oss-120b?
Codestral 2508, with 256K tokens against 131K.
How many benchmarks is the Codestral 2508 vs gpt-oss-120b comparison based on?
7 benchmarks have a result for both models as of October 21, 2026. Codestral 2508's coding index rests on 0 independent benchmarks and gpt-oss-120b's on 1. Where only one model has a result, the page says so instead of naming a winner.