gpt-oss-120b vs Mistral Small 3.1 24B
gpt-oss-120b and Mistral Small 3.1 24B cost about the same per output token ($0.50 vs $0.555 per million). Updated October 22, 2026; 28 benchmarks have a result for both.
gpt-oss-120b or Mistral Small 3.1 24B: 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 1.1x cheaper ($0.50 vs $0.555 per million) and a larger context window (131K against 128K tokens).
- Shared results
- 28
- gpt-oss-120b only
- 53
- Mistral Small 3.1 24B only
- 10
- Like-for-like categories
- 5 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, 967.
Directional only
Long documents
Listed context window
gpt-oss-120b
gpt-oss-120b takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-120b
gpt-oss-120b $0.0003, Mistral Small 3.1 24B $0.0006 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 Small 3.1 24B not benchmarked.
- Widest category
- Math, 20.2 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: gpt-oss-120b 1385 against Mistral Small 3.1 24B 1253, 22.1 points apart on a 0 to 100 scale.
- Output price
- gpt-oss-120b $0.50 against Mistral Small 3.1 24B $0.555 per million output tokens, 1.1x apart.
- Context window
- gpt-oss-120b takes 131K tokens, Mistral Small 3.1 24B 128K.
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 Small 3.1 24B | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Reasoning | 3 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 17 | 0 | ||
| All shared boards | 28 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | gpt-oss-120b | Mistral Small 3.1 24B | 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 | 1309 | gpt-oss-120b |
| LMArena Text: software and IT | 1387 | 1301 | gpt-oss-120b |
| SciCode | 7.7% | Not measured | 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 | gpt-oss-120b | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.6 | Not measured | Only gpt-oss-120b measured |
| Epoch Capabilities Index | 139.8 | Not measured | Only gpt-oss-120b measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | gpt-oss-120b | Mistral Small 3.1 24B | 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 | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | gpt-oss-120b | Mistral Small 3.1 24B | 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 | 1278 | gpt-oss-120b |
| LMArena Text: expert | 1356 | 1257 | gpt-oss-120b |
| LMArena Text: hard prompts in English | 1368 | 1290 | gpt-oss-120b |
| LMArena Vision: diagrams | Not measured | 1119 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: homework | Not measured | 1158 | Only Mistral Small 3.1 24B measured |
| Kagi LLM Benchmark | 58.6% | Not measured | Only gpt-oss-120b measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | gpt-oss-120b | Mistral Small 3.1 24B | 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 | 1261 | gpt-oss-120b |
| LMArena Text: mathematical work | 1386 | 1271 | gpt-oss-120b |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | gpt-oss-120b | Mistral Small 3.1 24B | 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 |
| LMArena Text: science | 1382 | 1265 | gpt-oss-120b |
| LMArena Text: medicine and healthcare | 1388 | 1267 | gpt-oss-120b |
| LMArena Text: legal and government | 1355 | 1271 | gpt-oss-120b |
| LMArena Text: business and finance | 1359 | 1257 | gpt-oss-120b |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | gpt-oss-120b | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| Design Arena: websites | 967 | Not measured | Only gpt-oss-120b measured |
| Design Arena: UI components | 924 | 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 | 962 | 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 index170
| Benchmark | gpt-oss-120b | Mistral Small 3.1 24B | 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% | Not measured | Only gpt-oss-120b measured |
| 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 | 1277 | gpt-oss-120b |
| LMArena Text: instruction following | 1318 | 1263 | gpt-oss-120b |
| LMArena Text: creative writing | 1274 | 1252 | gpt-oss-120b |
| LMArena Vision | Not measured | 1136 | Only Mistral Small 3.1 24B measured |
| LMArena Text: English | 1375 | 1295 | gpt-oss-120b |
| LMArena Text: non-English | 1351 | 1255 | gpt-oss-120b |
| LMArena Text: Chinese | 1385 | 1253 | gpt-oss-120b |
| LMArena Text: German | 1353 | 1266 | gpt-oss-120b |
| LMArena Text: Japanese | 1331 | 1208 | gpt-oss-120b |
| LMArena Text: Korean | 1282 | 1207 | gpt-oss-120b |
| LMArena Text: French | 1370 | 1273 | gpt-oss-120b |
| LMArena Text: Spanish | 1389 | 1284 | gpt-oss-120b |
| LMArena Text: Russian | 1343 | 1263 | gpt-oss-120b |
| LMArena Text: Polish | 1353 | 1242 | gpt-oss-120b |
| LMArena Text: multi-turn | 1340 | 1270 | gpt-oss-120b |
| LMArena Text: longer queries | 1319 | 1299 | gpt-oss-120b |
| LMArena Text: writing and language | 1306 | 1268 | gpt-oss-120b |
| LMArena Text: entertainment and media | 1292 | 1250 | gpt-oss-120b |
| LMArena Vision: English | Not measured | 1157 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: Chinese | Not measured | 1155 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: reading text | Not measured | 1132 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: humor | Not measured | 1142 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: captioning | Not measured | 1153 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: recognizing things | Not measured | 1155 | Only Mistral Small 3.1 24B measured |
| LMArena Vision: creative writing | Not measured | 1123 | Only Mistral Small 3.1 24B measured |
| Neither model has a result on the other 14 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | gpt-oss-120b | Mistral Small 3.1 24B | 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 Small 3.1 24B |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | Open weights | Open weights |
| Released | August 5, 2025 | March 17, 2025 |
| Context window | 131K | 128K |
| Input $/Mtok | $0.10 | $0.351 |
| Output $/Mtok | $0.50 | $0.555 |
| 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 Small 3.1 24B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick gpt-oss-120b for output tokens 1.1x cheaper ($0.50 vs $0.555 per million) and a larger context window (131K against 128K tokens).
Which is cheaper, gpt-oss-120b or Mistral Small 3.1 24B?
gpt-oss-120b costs $0.10 per million input tokens and $0.50 per million output tokens; Mistral Small 3.1 24B costs $0.351 and $0.555. Cost per solved task is known for gpt-oss-120b only.
Which has the bigger context window, gpt-oss-120b or Mistral Small 3.1 24B?
gpt-oss-120b, with 131K tokens against 128K.
How many benchmarks is the gpt-oss-120b vs Mistral Small 3.1 24B comparison based on?
28 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 Small 3.1 24B's on 0. Where only one model has a result, the page says so instead of naming a winner.