gpt-oss-120b vs Mistral Large 4
Updated October 21, 2026. 43 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Mistral Large 4 for coding: it scores 55.3 on the Vibe Coding Index against 34.5 for gpt-oss-120b.
Pick gpt-oss-120b when output tokens cost 7.0x less.
- Shared results
- 43
- gpt-oss-120b only
- 35
- Mistral Large 4 only
- 38
- Like-for-like categories
- 7 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, four public coding benchmarks
Mistral Large 4
Mistral Large 4 scores 55.3 against 34.5.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
Mistral Large 4
Mistral Large 4 leads, 38.4 against 11.6.
Same board, both measured
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
Mistral Large 4
Mistral Large 4 leads, 1258 against 968.
Same board, both measured
Long documents
Listed context window
Mistral Large 4
Mistral Large 4 takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-120b
gpt-oss-120b $0.0004, Mistral Large 4 $0.0034 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
- gpt-oss-120b 34.5 from 2 benchmarks, Mistral Large 4 55.3 from 2 benchmarks.
- Widest category
- Web design, 37.6 points apart on average over 6 shared benchmarks.
- Widest single gap
- Design Arena: 3D: gpt-oss-120b 901 against Mistral Large 4 1253, 44.0 points apart on a 0 to 100 scale.
- Output price
- gpt-oss-120b $0.60 against Mistral Large 4 $4.18 per million output tokens, 7.0x apart.
- Context window
- gpt-oss-120b takes 131K tokens, Mistral Large 4 1.05M.
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 | gpt-oss-120b | Bars | Mistral Large 4 | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 6 | ||
| General | 0 | 18 | ||
| All shared boards | 0 | 43 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index04
| Benchmark | gpt-oss-120b | Mistral Large 4 | Leader |
|---|---|---|---|
| Aider polyglot | 41.8% | Not measured | Only gpt-oss-120b measured |
| SWE-bench Verified | 26.0% | Not measured | Only gpt-oss-120b measured |
| LiveBench coding | Not measured | 60.0% | Only Mistral Large 4 measured |
| LMArena WebDev | Not measured | 1543 | Only Mistral Large 4 measured |
| AA Coding Index | 30.4 | Not measured | Only gpt-oss-120b measured |
| LiveCodeBench (Vals AI) | 83.2% | Not measured | Only gpt-oss-120b measured |
| SWE-bench Verified (Vals AI) | 33.6% | Not measured | Only gpt-oss-120b measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 27.8% | Only Mistral Large 4 measured |
| Vibe Code Bench (Vals AI) | Not measured | 78.4% | Only Mistral Large 4 measured |
| Code migration (Vals AI) | Not measured | 30.6% | Only Mistral Large 4 measured |
| IOI (Vals AI) | Not measured | 45.3% | Only Mistral Large 4 measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 33.1% | Only Mistral Large 4 measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 14.3% | Only Mistral Large 4 measured |
| LiveCodeBench (Artificial Analysis run) | 87.8% | Not measured | Only gpt-oss-120b measured |
| SciCode | 34.0% | 54.2% | Mistral Large 4 |
| Terminal-Bench Hard (Artificial Analysis run) | 23.5% | Not measured | Only gpt-oss-120b measured |
| Terminal-Bench | 18.7% | Not measured | Only gpt-oss-120b measured |
| FrontierCode | Not measured | 39.4% | Only Mistral Large 4 measured |
| WeirdML | 48.2% | Not measured | Only gpt-oss-120b measured |
| ALE-Bench | 575.6 | 1213.2 | Mistral Large 4 |
| LMArena Text: coding | 1379 | 1478 | Mistral Large 4 |
| LMArena Text: software and IT | 1387 | 1467 | Mistral Large 4 |
| LMArena WebDev: React | Not measured | 1559 | Only Mistral Large 4 measured |
| LMArena WebDev: HTML | Not measured | 1487 | Only Mistral Large 4 measured |
| Neither model has a result on the other 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | gpt-oss-120b | Mistral Large 4 | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.6 | 38.4 | Mistral Large 4 |
| Epoch Capabilities Index | 139.8 | 150.0 | Mistral Large 4 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | gpt-oss-120b | Mistral Large 4 | Leader |
|---|---|---|---|
| AA Agentic Index | 3.7 | Not measured | Only gpt-oss-120b measured |
| LiveBench research | Not measured | 28.1% | Only Mistral Large 4 measured |
| LiveBench browser | Not measured | 8.0% | Only Mistral Large 4 measured |
| LiveBench long agentic | Not measured | 11.8% | Only Mistral Large 4 measured |
| Finance Agent (Vals AI) | Not measured | 54.7% | Only Mistral Large 4 measured |
| Tax Agent Bench (Vals AI) | Not measured | 30.4% | Only Mistral Large 4 measured |
| Legal Research Bench (Vals AI) | Not measured | 31.7% | Only Mistral Large 4 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 17.5% | Only Mistral Large 4 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 56.2% | Only Mistral Large 4 measured |
| BioMysteryBench (Vals AI) | Not measured | 67.0% | Only Mistral Large 4 measured |
| τ²-Bench Telecom | 65.8% | Not measured | Only gpt-oss-120b measured |
| APEX-Agents | 4.4% | Not measured | Only gpt-oss-120b measured |
| METR time horizons | 56.6% | Not measured | Only gpt-oss-120b measured |
| Vending-Bench 2 | -$22 | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | gpt-oss-120b | Mistral Large 4 | Leader |
|---|---|---|---|
| GPQA Diamond | 78.5% | Not measured | Only gpt-oss-120b measured |
| LiveBench reasoning | Not measured | 83.9% | Only Mistral Large 4 measured |
| MysteryMechanism (Vals AI) | Not measured | 12.2% | Only Mistral Large 4 measured |
| GPQA Diamond (Artificial Analysis run) | 78.2% | Not measured | Only gpt-oss-120b measured |
| Humanity's Last Exam (Artificial Analysis run) | 19.6% | 35.0% | Mistral Large 4 |
| GPQA Diamond (Epoch AI run) | 75.8% | 88.1% | Mistral Large 4 |
| SimpleBench | 22.1% | Not measured | Only gpt-oss-120b measured |
| Chess puzzles | Not measured | 33.0% | Only Mistral Large 4 measured |
| Mystery game puzzles | 2.0% | 17.0% | Mistral Large 4 |
| LMArena Text: hard prompts | 1364 | 1444 | Mistral Large 4 |
| LMArena Text: expert | 1356 | 1444 | Mistral Large 4 |
| LMArena Text: hard prompts in English | 1368 | 1442 | Mistral Large 4 |
| LMArena Vision: diagrams | Not measured | 1288 | Only Mistral Large 4 measured |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | gpt-oss-120b | Mistral Large 4 | Leader |
|---|---|---|---|
| AIME | 92.6% | Not measured | Only gpt-oss-120b measured |
| MATH 500 (Vals AI) | 94.8% | Not measured | Only gpt-oss-120b measured |
| MGSM (Vals AI) | 92.0% | Not measured | Only gpt-oss-120b measured |
| AA Math Index | 93.4 | Not measured | Only gpt-oss-120b measured |
| AIME 2025 (Artificial Analysis run) | 93.4% | Not measured | Only gpt-oss-120b measured |
| OTIS Mock AIME | 88.9% | 98.3% | Mistral Large 4 |
| FrontierMath, tiers 1 to 3 | Not measured | 58.6% | Only Mistral Large 4 measured |
| FrontierMath, tier 4 | Not measured | 26.8% | Only Mistral Large 4 measured |
| ProofBench | Not measured | 10.0% | Only Mistral Large 4 measured |
| LMArena Text: math | 1389 | 1469 | Mistral Large 4 |
| LMArena Text: mathematical work | 1386 | 1462 | Mistral Large 4 |
| Neither model has a result on the other 2 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | gpt-oss-120b | Mistral Large 4 | Leader |
|---|---|---|---|
| MMLU Pro | 79.2% | Not measured | Only gpt-oss-120b measured |
| LegalBench (Vals AI) | 75.9% | Not measured | Only gpt-oss-120b measured |
| CaseLaw (Vals AI) | 48.8% | Not measured | Only gpt-oss-120b measured |
| CorpFin (Vals AI) | 58.2% | Not measured | Only gpt-oss-120b measured |
| TaxEval (Vals AI) | 71.6% | Not measured | Only gpt-oss-120b measured |
| MedQA (Vals AI) | 91.4% | Not measured | Only gpt-oss-120b measured |
| MedCode (Vals AI) | Not measured | 40.7% | Only Mistral Large 4 measured |
| MMLU Pro (Artificial Analysis run) | 80.8% | Not measured | Only gpt-oss-120b measured |
| SimpleQA Verified | Not measured | 20.0% | Only Mistral Large 4 measured |
| LMArena Text: science | 1382 | 1448 | Mistral Large 4 |
| LMArena Text: medicine and healthcare | 1388 | 1433 | Mistral Large 4 |
| LMArena Text: legal and government | 1355 | 1425 | Mistral Large 4 |
| LMArena Text: business and finance | 1359 | 1439 | Mistral Large 4 |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | gpt-oss-120b | Mistral Large 4 | Leader |
|---|---|---|---|
| Design Arena: websites | 968 | 1258 | Mistral Large 4 |
| Design Arena: UI components | 927 | 1275 | Mistral Large 4 |
| Design Arena: game dev | 992 | 1226 | Mistral Large 4 |
| Design Arena: data viz | 987 | 1274 | Mistral Large 4 |
| Design Arena: 3D | 901 | 1253 | Mistral Large 4 |
| Design Arena: all code categories | 963 | 1259 | Mistral Large 4 |
| Design Arena: SVG | Not measured | 1232 | Only Mistral Large 4 measured |
| Design Arena: ASCII art | Not measured | 1203 | Only Mistral Large 4 measured |
| LiveBench design | Not measured | 65.6% | Only Mistral Large 4 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
| Benchmark | gpt-oss-120b | Mistral Large 4 | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 76.5% | Only Mistral Large 4 measured |
| MedScribe (Vals AI) | Not measured | 80.4% | Only Mistral Large 4 measured |
| IFBench | 69.0% | Not measured | Only gpt-oss-120b measured |
| Long context reasoning (AA-LCR) | 52.0% | 81.3% | Mistral Large 4 |
| DTBench | 76.3% | 88.2% | Mistral Large 4 |
| LMCA | 22.1% | 35.8% | Mistral Large 4 |
| Fiction.LiveBench | 44.4% | Not measured | Only gpt-oss-120b measured |
| Creative writing (Lech Mazur) | 77.1% | Not measured | Only gpt-oss-120b measured |
| Surface Evolver Bench | 25.0% | 28.1% | Mistral Large 4 |
| Furniture assembly | Not measured | 20.8% | Only Mistral Large 4 measured |
| LMArena Text | 1366 | 1431 | Mistral Large 4 |
| LMArena Text: instruction following | 1318 | 1422 | Mistral Large 4 |
| LMArena Text: creative writing | 1274 | 1363 | Mistral Large 4 |
| LMArena Vision | Not measured | 1272 | Only Mistral Large 4 measured |
| LMArena Text: English | 1375 | 1433 | Mistral Large 4 |
| LMArena Text: non-English | 1351 | 1422 | Mistral Large 4 |
| LMArena Text: Chinese | 1385 | 1492 | Mistral Large 4 |
| LMArena Text: German | 1353 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: Japanese | 1331 | 1415 | Mistral Large 4 |
| LMArena Text: Korean | 1282 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: French | 1370 | 1492 | Mistral Large 4 |
| LMArena Text: Spanish | 1389 | 1455 | Mistral Large 4 |
| LMArena Text: Russian | 1343 | 1417 | Mistral Large 4 |
| LMArena Text: Polish | 1353 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: multi-turn | 1340 | 1435 | Mistral Large 4 |
| LMArena Text: longer queries | 1319 | 1428 | Mistral Large 4 |
| LMArena Text: writing and language | 1306 | 1386 | Mistral Large 4 |
| LMArena Text: entertainment and media | 1292 | 1378 | Mistral Large 4 |
| LMArena Vision: English | Not measured | 1283 | Only Mistral Large 4 measured |
| LMArena Vision: reading text | Not measured | 1284 | Only Mistral Large 4 measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | gpt-oss-120b | Mistral Large 4 | 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 | OpenAI gpt-oss-120b | Mistral Large 4 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | August 5, 2025 | October 6, 2026 |
| Context window | 131K | 1.05M |
| Input $/Mtok | $0.15 | $1.36 |
| Output $/Mtok | $0.60 | $4.18 |
| Coding index | 34.5 | 55.3 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $0.51 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is gpt-oss-120b better than Mistral Large 4 for coding?
Pick Mistral Large 4 for coding: it scores 55.3 on the Vibe Coding Index against 34.5 for gpt-oss-120b. Pick gpt-oss-120b when output tokens cost 7.0x less.
Which is cheaper, gpt-oss-120b or Mistral Large 4?
gpt-oss-120b costs $0.15 per million input tokens and $0.60 per million output tokens; Mistral Large 4 costs $1.36 and $4.18. Cost per solved task is known for gpt-oss-120b only.
Which has the bigger context window, gpt-oss-120b or Mistral Large 4?
Mistral Large 4, with 1.05M tokens against 131K.
How many benchmarks is the gpt-oss-120b vs Mistral Large 4 comparison based on?
43 benchmarks have a result for both models as of October 21, 2026. gpt-oss-120b's coding index rests on 2 independent benchmarks and Mistral Large 4's on 2. Where only one model has a result, the page says so instead of naming a winner.