gpt-oss-120b vs Mistral Large 3 2512
gpt-oss-120b leads Mistral Large 3 2512 on the Artificial Analysis Intelligence Index, 11.6 to 9.3, and gpt-oss-120b costs 3.0x less per output token ($0.50 vs $1.50 per million). Updated October 22, 2026; 23 benchmarks have a result for both.
gpt-oss-120b or Mistral Large 3 2512: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick gpt-oss-120b for output tokens 3.0x cheaper ($0.50 vs $1.50 per million) and a lower cost per solved task ($0.51 against $0.77).
- Shared results
- 23
- gpt-oss-120b only
- 58
- Mistral Large 3 2512 only
- 5
- Like-for-like categories
- 8 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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
gpt-oss-120b
gpt-oss-120b leads, 11.6 against 9.3.
Same board, both measured
Agentic work
AA Agentic Index
gpt-oss-120b
gpt-oss-120b leads, 3.7 against 1.0.
Same board, both measured
Reasoning
GPQA Diamond
gpt-oss-120b
gpt-oss-120b leads, 78.5% against 68.4%.
Same board, both measured
Math
AIME
gpt-oss-120b
gpt-oss-120b leads, 92.6% against 42.9%.
Same board, both measured
Knowledge
MMLU Pro
Mistral Large 3 2512
Mistral Large 3 2512 leads, 79.8% against 79.2%.
Same board, both measured
Web design
Design Arena: websites
Mistral Large 3 2512
Mistral Large 3 2512 leads, 1162 against 967.
Same board, both measured
Long documents
Listed context window
Mistral Large 3 2512
Mistral Large 3 2512 takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-120b
gpt-oss-120b $0.0003, Mistral Large 3 2512 $0.0013 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
gpt-oss-120b
gpt-oss-120b $0.51, Mistral Large 3 2512 $0.77.
Published attempt cost and pass rate
At a glance
- Coding index
- gpt-oss-120b not benchmarked, Mistral Large 3 2512 not benchmarked.
- Widest category
- Math, 28.1 points apart on average over 2 shared benchmarks.
- Widest single gap
- AIME: gpt-oss-120b 92.6% against Mistral Large 3 2512 42.9%, 49.7 points apart on a 0 to 100 scale.
- Output price
- gpt-oss-120b $0.50 against Mistral Large 3 2512 $1.50 per million output tokens, 3.0x apart.
- Context window
- gpt-oss-120b takes 131K tokens, Mistral Large 3 2512 262K.
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 3 2512 | Ties |
|---|---|---|---|---|
| Coding | 3 | 1 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 1 | 5 | ||
| Web design | 0 | 6 | ||
| General | 2 | 0 | ||
| All shared boards | 11 | 12 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index31
| Benchmark | gpt-oss-120b | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| Aider polyglot | 41.8% | Not measured | Only gpt-oss-120b measured |
| SWE-bench Verified | 26.0% | Not measured | Only gpt-oss-120b measured |
| AA Coding Index | 30.4 | 20.1 | gpt-oss-120b |
| LiveCodeBench (Vals AI) | 83.2% | 55.3% | gpt-oss-120b |
| SWE-bench Verified (Vals AI) | 33.6% | 41.4% | Mistral Large 3 2512 |
| LiveCodeBench (Artificial Analysis run) | 87.8% | Not measured | Only gpt-oss-120b measured |
| SciCode | 34.0% | Not measured | Only gpt-oss-120b measured |
| 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 |
| WeirdML | 48.2% | Not measured | Only gpt-oss-120b measured |
| ALE-Bench | 575.6 | 264.7 | gpt-oss-120b |
| LMArena Text: coding | 1379 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: software and IT | 1387 | Not measured | Only gpt-oss-120b measured |
| SciCode | 7.7% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 25 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | gpt-oss-120b | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.6 | 9.3 | gpt-oss-120b |
| Epoch Capabilities Index | 139.8 | Not measured | Only gpt-oss-120b measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | gpt-oss-120b | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| AA Agentic Index | 3.7 | 1.0 | gpt-oss-120b |
| 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 |
| BrowseComp-Plus | 67.0% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | gpt-oss-120b | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| GPQA Diamond | 78.5% | 68.4% | gpt-oss-120b |
| MMMU Pro (Vals AI) | Not measured | 66.2% | Only Mistral Large 3 2512 measured |
| GPQA Diamond (Artificial Analysis run) | 78.2% | Not measured | Only gpt-oss-120b measured |
| Humanity's Last Exam (Artificial Analysis run) | 19.6% | Not measured | Only gpt-oss-120b measured |
| GPQA Diamond (Epoch AI run) | 75.8% | Not measured | Only gpt-oss-120b measured |
| SimpleBench | 22.1% | Not measured | Only gpt-oss-120b measured |
| Mystery game puzzles | 2.0% | Not measured | Only gpt-oss-120b measured |
| LMArena Text: hard prompts | 1364 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: expert | 1356 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: hard prompts in English | 1368 | Not measured | Only gpt-oss-120b measured |
| Kagi LLM Benchmark | 58.6% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | gpt-oss-120b | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| AIME | 92.6% | 42.9% | gpt-oss-120b |
| MATH 500 (Vals AI) | 94.8% | Not measured | Only gpt-oss-120b measured |
| MGSM (Vals AI) | 92.0% | 85.4% | gpt-oss-120b |
| 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% | Not measured | Only gpt-oss-120b measured |
| LMArena Text: math | 1389 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: mathematical work | 1386 | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | gpt-oss-120b | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| MMLU Pro | 79.2% | 79.8% | Mistral Large 3 2512 |
| LegalBench (Vals AI) | 75.9% | 79.1% | Mistral Large 3 2512 |
| CaseLaw (Vals AI) | 48.8% | 61.4% | Mistral Large 3 2512 |
| CorpFin (Vals AI) | 58.2% | 61.0% | Mistral Large 3 2512 |
| TaxEval (Vals AI) | 71.6% | 73.1% | Mistral Large 3 2512 |
| MortgageTax (Vals AI) | Not measured | 52.1% | Only Mistral Large 3 2512 measured |
| MedQA (Vals AI) | 91.4% | 82.2% | gpt-oss-120b |
| MMLU Pro (Artificial Analysis run) | 80.8% | Not measured | Only gpt-oss-120b measured |
| LMArena Text: science | 1382 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: medicine and healthcare | 1388 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: legal and government | 1355 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: business and finance | 1359 | Not measured | 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 index06
| Benchmark | gpt-oss-120b | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| Design Arena: websites | 967 | 1162 | Mistral Large 3 2512 |
| Design Arena: UI components | 924 | 1099 | Mistral Large 3 2512 |
| Design Arena: game dev | 992 | 1080 | Mistral Large 3 2512 |
| Design Arena: data viz | 987 | 1136 | Mistral Large 3 2512 |
| Design Arena: 3D | 901 | 1099 | Mistral Large 3 2512 |
| Design Arena: all code categories | 962 | 1141 | Mistral Large 3 2512 |
| Design Arena: SVG | Not measured | 992 | Only Mistral Large 3 2512 measured |
| Design Arena: ASCII art | Not measured | 1069 | Only Mistral Large 3 2512 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | gpt-oss-120b | Mistral Large 3 2512 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 24.6% | Only Mistral Large 3 2512 measured |
| IFBench | 69.0% | Not measured | Only gpt-oss-120b measured |
| Long context reasoning (AA-LCR) | 52.0% | Not measured | Only gpt-oss-120b measured |
| DTBench | 76.3% | 65.1% | gpt-oss-120b |
| LMCA | 22.1% | 16.7% | gpt-oss-120b |
| 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% | Not measured | Only gpt-oss-120b measured |
| LMArena Text | 1366 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: instruction following | 1318 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: creative writing | 1274 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: English | 1375 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: non-English | 1351 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: Chinese | 1385 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: German | 1353 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: Japanese | 1331 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: Korean | 1282 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: French | 1370 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: Spanish | 1389 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: Russian | 1343 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: Polish | 1353 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: multi-turn | 1340 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: longer queries | 1319 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: writing and language | 1306 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: entertainment and media | 1292 | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | gpt-oss-120b | Mistral Large 3 2512 | 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 3 2512 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | August 5, 2025 | December 1, 2025 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.10 | $0.50 |
| Output $/Mtok | $0.50 | $1.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $0.51 | $0.77 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is gpt-oss-120b better than Mistral Large 3 2512 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick gpt-oss-120b for output tokens 3.0x cheaper ($0.50 vs $1.50 per million) and a lower cost per solved task ($0.51 against $0.77).
Which is cheaper, gpt-oss-120b or Mistral Large 3 2512?
gpt-oss-120b costs $0.10 per million input tokens and $0.50 per million output tokens; Mistral Large 3 2512 costs $0.50 and $1.50. Per solved SWE-bench Verified task, gpt-oss-120b costs $0.51 and Mistral Large 3 2512 $0.77.
Which has the bigger context window, gpt-oss-120b or Mistral Large 3 2512?
Mistral Large 3 2512, with 262K tokens against 131K.
How many benchmarks is the gpt-oss-120b vs Mistral Large 3 2512 comparison based on?
23 benchmarks have a result for both models as of October 22, 2026. gpt-oss-120b's coding index rests on 1 independent benchmark and Mistral Large 3 2512's on 1. Where only one model has a result, the page says so instead of naming a winner.