gpt-oss-20b vs Mistral Small 3.1 24B
gpt-oss-20b costs 3.7x less per output token than Mistral Small 3.1 24B ($0.15 vs $0.555 per million). Updated October 21, 2026; 27 benchmarks have a result for both.
gpt-oss-20b 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-20b for output tokens 3.7x cheaper ($0.15 vs $0.555 per million) and a larger context window (131K against 128K tokens).
- Shared results
- 27
- gpt-oss-20b only
- 35
- Mistral Small 3.1 24B only
- 11
- 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-20b has a AA Intelligence Index result, 10.0.
Directional only
Reasoning
GPQA Diamond
No call
Only gpt-oss-20b has a GPQA Diamond result, 68.9%.
Directional only
Math
AIME
No call
Only gpt-oss-20b has a AIME result, 86.0%.
Directional only
Knowledge
MMLU Pro
No call
Only gpt-oss-20b has a MMLU Pro result, 71.6%.
Directional only
Web design
Design Arena: websites
No call
Only gpt-oss-20b has a Design Arena: websites result, 852.
Directional only
Long documents
Listed context window
gpt-oss-20b
gpt-oss-20b takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-20b
gpt-oss-20b $0.0001, Mistral Small 3.1 24B $0.0006 per turn.
List prices
At a glance
- Coding index
- gpt-oss-20b not benchmarked, Mistral Small 3.1 24B not benchmarked.
- Widest category
- Math, 7.5 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: gpt-oss-20b 1315 against Mistral Small 3.1 24B 1253, 10.4 points apart on a 0 to 100 scale.
- Output price
- gpt-oss-20b $0.15 against Mistral Small 3.1 24B $0.555 per million output tokens, 3.7x apart.
- Context window
- gpt-oss-20b 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-20b | Bars | Mistral Small 3.1 24B | Ties |
|---|---|---|---|---|
| Coding | 1 | 1 | ||
| Reasoning | 2 | 1 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 8 | 8 | ||
| All shared boards | 17 | 10 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index11
| Benchmark | gpt-oss-20b | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | 80.4% | Not measured | Only gpt-oss-20b measured |
| LiveCodeBench (Artificial Analysis run) | 77.7% | Not measured | Only gpt-oss-20b measured |
| SciCode | 38.9% | Not measured | Only gpt-oss-20b measured |
| Terminal-Bench Hard (Artificial Analysis run) | 10.6% | Not measured | Only gpt-oss-20b measured |
| Terminal-Bench | 3.4% | Not measured | Only gpt-oss-20b measured |
| WeirdML | 40.9% | Not measured | Only gpt-oss-20b measured |
| ALE-Bench | 566.0 | Not measured | Only gpt-oss-20b measured |
| LMArena Text: coding | 1307 | 1309 | Mistral Small 3.1 24B |
| LMArena Text: software and IT | 1311 | 1301 | gpt-oss-20b |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | gpt-oss-20b | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.0 | Not measured | Only gpt-oss-20b measured |
| Epoch Capabilities Index | 137.7 | Not measured | Only gpt-oss-20b measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | gpt-oss-20b | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 60.2% | Not measured | Only gpt-oss-20b measured |
| BrowseComp-Plus | 50.6% | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | gpt-oss-20b | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| GPQA Diamond | 68.9% | Not measured | Only gpt-oss-20b measured |
| GPQA Diamond (Artificial Analysis run) | 68.8% | Not measured | Only gpt-oss-20b measured |
| Humanity's Last Exam (Artificial Analysis run) | 11.0% | Not measured | Only gpt-oss-20b measured |
| GPQA Diamond (Epoch AI run) | 60.8% | Not measured | Only gpt-oss-20b measured |
| LMArena Text: hard prompts | 1274 | 1278 | Mistral Small 3.1 24B |
| LMArena Text: expert | 1258 | 1257 | gpt-oss-20b |
| LMArena Text: hard prompts in English | 1292 | 1290 | gpt-oss-20b |
| 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 | 53.2% | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | gpt-oss-20b | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| AIME | 86.0% | Not measured | Only gpt-oss-20b measured |
| MATH 500 (Vals AI) | 94.2% | Not measured | Only gpt-oss-20b measured |
| MGSM (Vals AI) | 89.0% | Not measured | Only gpt-oss-20b measured |
| AA Math Index | 89.3 | Not measured | Only gpt-oss-20b measured |
| AIME 2025 (Artificial Analysis run) | 89.3% | Not measured | Only gpt-oss-20b measured |
| OTIS Mock AIME | 65.3% | Not measured | Only gpt-oss-20b measured |
| LMArena Text: math | 1317 | 1261 | gpt-oss-20b |
| LMArena Text: mathematical work | 1305 | 1271 | gpt-oss-20b |
| 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-20b | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| MMLU Pro | 71.6% | Not measured | Only gpt-oss-20b measured |
| LegalBench (Vals AI) | 70.8% | Not measured | Only gpt-oss-20b measured |
| CaseLaw (Vals AI) | 43.8% | Not measured | Only gpt-oss-20b measured |
| CorpFin (Vals AI) | 53.1% | Not measured | Only gpt-oss-20b measured |
| TaxEval (Vals AI) | 63.7% | Not measured | Only gpt-oss-20b measured |
| MedQA (Vals AI) | 82.9% | Not measured | Only gpt-oss-20b measured |
| MMLU Pro (Artificial Analysis run) | 74.8% | Not measured | Only gpt-oss-20b measured |
| LMArena Text: science | 1296 | 1265 | gpt-oss-20b |
| LMArena Text: medicine and healthcare | 1310 | 1267 | gpt-oss-20b |
| LMArena Text: legal and government | 1280 | 1271 | gpt-oss-20b |
| LMArena Text: business and finance | 1284 | 1257 | gpt-oss-20b |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | gpt-oss-20b | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| Design Arena: websites | 852 | Not measured | Only gpt-oss-20b measured |
| Design Arena: data viz | 933 | Not measured | Only gpt-oss-20b measured |
| Neither model has a result on the other 7 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index88
| Benchmark | gpt-oss-20b | Mistral Small 3.1 24B | Leader |
|---|---|---|---|
| IFBench | 65.1% | Not measured | Only gpt-oss-20b measured |
| Long context reasoning (AA-LCR) | 34.7% | Not measured | Only gpt-oss-20b measured |
| DTBench | 68.0% | Not measured | Only gpt-oss-20b measured |
| LMCA | 14.5% | Not measured | Only gpt-oss-20b measured |
| LMArena Text | 1288 | 1277 | gpt-oss-20b |
| LMArena Text: instruction following | 1236 | 1263 | Mistral Small 3.1 24B |
| LMArena Text: creative writing | 1201 | 1252 | Mistral Small 3.1 24B |
| LMArena Vision | Not measured | 1136 | Only Mistral Small 3.1 24B measured |
| LMArena Text: English | 1303 | 1295 | gpt-oss-20b |
| LMArena Text: non-English | 1269 | 1255 | gpt-oss-20b |
| LMArena Text: Chinese | 1315 | 1253 | gpt-oss-20b |
| LMArena Text: German | 1255 | 1266 | Mistral Small 3.1 24B |
| LMArena Text: Japanese | 1244 | 1208 | gpt-oss-20b |
| LMArena Text: Korean | 1236 | 1207 | gpt-oss-20b |
| LMArena Text: French | Not measured | 1273 | Only Mistral Small 3.1 24B measured |
| LMArena Text: Spanish | 1267 | 1284 | Mistral Small 3.1 24B |
| LMArena Text: Russian | 1278 | 1263 | gpt-oss-20b |
| LMArena Text: Polish | 1252 | 1242 | gpt-oss-20b |
| LMArena Text: multi-turn | 1268 | 1270 | Mistral Small 3.1 24B |
| LMArena Text: longer queries | 1250 | 1299 | Mistral Small 3.1 24B |
| LMArena Text: writing and language | 1238 | 1268 | Mistral Small 3.1 24B |
| LMArena Text: entertainment and media | 1216 | 1250 | Mistral Small 3.1 24B |
| 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 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | gpt-oss-20b | 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-20b costs less
Repository review
50K input + 3K output tokens
gpt-oss-20b costs less
Agent loop
200K input + 10K output tokens
gpt-oss-20b 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-20b | 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 | $0.03 | $0.351 |
| Output $/Mtok | $0.15 | $0.555 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is gpt-oss-20b 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-20b for output tokens 3.7x cheaper ($0.15 vs $0.555 per million) and a larger context window (131K against 128K tokens).
Which is cheaper, gpt-oss-20b or Mistral Small 3.1 24B?
gpt-oss-20b costs $0.03 per million input tokens and $0.15 per million output tokens; Mistral Small 3.1 24B costs $0.351 and $0.555. Cost per solved task is known for neither of them.
Which has the bigger context window, gpt-oss-20b or Mistral Small 3.1 24B?
gpt-oss-20b, with 131K tokens against 128K.
How many benchmarks is the gpt-oss-20b vs Mistral Small 3.1 24B comparison based on?
27 benchmarks have a result for both models as of October 21, 2026. gpt-oss-20b's coding index rests on 0 independent benchmarks 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.