gpt-oss-120b vs Mistral Nemo
Mistral Nemo costs 3.3x less per output token than gpt-oss-120b ($0.15 vs $0.50 per million). Updated October 21, 2026; 2 benchmarks have a result for both.
gpt-oss-120b or Mistral Nemo: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 2
- gpt-oss-120b only
- 79
- Mistral Nemo only
- 1
- Like-for-like categories
- 2 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
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
No call
Only gpt-oss-120b has a Design Arena: websites result, 968.
Directional only
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Nemo
gpt-oss-120b $0.0003, Mistral Nemo $0.0002 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 not benchmarked, Mistral Nemo not benchmarked.
- Widest category
- General, 27.7 points apart on average over 1 shared benchmark.
- Widest single gap
- DTBench: gpt-oss-120b 76.3% against Mistral Nemo 48.6%, 27.7 points apart on a 0 to 100 scale.
- Output price
- gpt-oss-120b $0.50 against Mistral Nemo $0.15 per million output tokens, 3.3x apart.
- Context window
- Both take 131K tokens.
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 Nemo | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 2 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index14 boards, none shared
| Benchmark | gpt-oss-120b | Mistral Nemo | 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 | 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 |
| 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 | Not measured | Only gpt-oss-120b measured |
| 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 Nemo | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.6 | Not measured | Only gpt-oss-120b measured |
| Epoch Capabilities Index | 139.8 | 118.7 | gpt-oss-120b |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | gpt-oss-120b | Mistral Nemo | Leader |
|---|---|---|---|
| AA Agentic Index | 3.7 | Not measured | Only gpt-oss-120b 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 |
| BrowseComp-Plus | 67.0% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | gpt-oss-120b | Mistral Nemo | Leader |
|---|---|---|---|
| GPQA Diamond | 78.5% | Not measured | Only gpt-oss-120b 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 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | gpt-oss-120b | Mistral Nemo | 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% | 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 | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | gpt-oss-120b | Mistral Nemo | 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 |
| MMLU Pro (Artificial Analysis run) | 80.8% | Not measured | Only gpt-oss-120b measured |
| MMLU | Not measured | 65.3% | Only Mistral Nemo 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 designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | gpt-oss-120b | Mistral Nemo | Leader |
|---|---|---|---|
| Design Arena: websites | 968 | Not measured | Only gpt-oss-120b measured |
| Design Arena: UI components | 927 | Not measured | Only gpt-oss-120b measured |
| Design Arena: game dev | 992 | Not measured | Only gpt-oss-120b measured |
| Design Arena: data viz | 987 | Not measured | Only gpt-oss-120b measured |
| Design Arena: 3D | 901 | Not measured | Only gpt-oss-120b measured |
| Design Arena: all code categories | 963 | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | gpt-oss-120b | Mistral Nemo | Leader |
|---|---|---|---|
| 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% | 48.6% | gpt-oss-120b |
| LMCA | 22.1% | Not measured | Only gpt-oss-120b measured |
| 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 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | gpt-oss-120b | Mistral Nemo | 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
Mistral Nemo 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 Nemo |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | Open weights | Open weights |
| Released | August 5, 2025 | July 19, 2024 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.10 | $0.15 |
| Output $/Mtok | $0.50 | $0.15 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | $0.51 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is gpt-oss-120b better than Mistral Nemo for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, gpt-oss-120b or Mistral Nemo?
gpt-oss-120b costs $0.10 per million input tokens and $0.50 per million output tokens; Mistral Nemo costs $0.15 and $0.15. Cost per solved task is known for gpt-oss-120b only.
Which has the bigger context window, gpt-oss-120b or Mistral Nemo?
Neither: both take 131K tokens.
How many benchmarks is the gpt-oss-120b vs Mistral Nemo comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. gpt-oss-120b's coding index rests on 1 independent benchmark and Mistral Nemo's on 0. Where only one model has a result, the page says so instead of naming a winner.