gpt-oss-20b vs Mistral Medium 3
Updated October 21, 2026. 42 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
gpt-oss-20b has the edge where output tokens cost 13.3x less, and where what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 42
- gpt-oss-20b only
- 18
- Mistral Medium 3 only
- 15
- Like-for-like categories
- 8 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, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
gpt-oss-20b
gpt-oss-20b leads, 10.0 against 9.0.
Same board, both measured
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
Mistral Medium 3
Mistral Medium 3 leads, 1079 against 853.
Same board, both measured
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-20b
gpt-oss-20b $0.0001, Mistral Medium 3 $0.0014 per turn.
List prices
At a glance
- Coding index
- gpt-oss-20b not benchmarked, Mistral Medium 3 not benchmarked.
- Widest category
- Agentic work, 36.0 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Math Index: gpt-oss-20b 89.3 against Mistral Medium 3 30.3, 59.0 points apart on a 0 to 100 scale.
- Output price
- gpt-oss-20b $0.15 against Mistral Medium 3 $2.00 per million output tokens, 13.3x 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-20b | Bars | Mistral Medium 3 | Ties |
|---|---|---|---|---|
| Coding | 2 | 2 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 3 | ||
| Math | 2 | 2 | ||
| Knowledge | 0 | 5 | ||
| Web design | 0 | 2 | ||
| General | 3 | 16 | ||
| All shared boards | 12 | 30 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index22
| Benchmark | gpt-oss-20b | Mistral Medium 3 | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | 80.4% | Not measured | Only gpt-oss-20b measured |
| LiveCodeBench (Artificial Analysis run) | 77.7% | 40.0% | gpt-oss-20b |
| SciCode | 38.9% | Not measured | Only gpt-oss-20b measured |
| Terminal-Bench Hard (Artificial Analysis run) | 10.6% | 3.8% | gpt-oss-20b |
| 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 | 1385 | Mistral Medium 3 |
| LMArena Text: software and IT | 1311 | 1385 | Mistral Medium 3 |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | gpt-oss-20b | Mistral Medium 3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 10.0 | 9.0 | gpt-oss-20b |
| Epoch Capabilities Index | 137.7 | 134.0 | gpt-oss-20b |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | gpt-oss-20b | Mistral Medium 3 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 60.2% | 24.3% | gpt-oss-20b |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index23
| Benchmark | gpt-oss-20b | Mistral Medium 3 | Leader |
|---|---|---|---|
| GPQA Diamond | 68.9% | Not measured | Only gpt-oss-20b measured |
| GPQA Diamond (Artificial Analysis run) | 68.8% | 57.8% | gpt-oss-20b |
| Humanity's Last Exam (Artificial Analysis run) | 11.0% | 4.1% | gpt-oss-20b |
| GPQA Diamond (Epoch AI run) | 60.8% | Not measured | Only gpt-oss-20b measured |
| LMArena Text: hard prompts | 1274 | 1364 | Mistral Medium 3 |
| LMArena Text: expert | 1258 | 1342 | Mistral Medium 3 |
| LMArena Text: hard prompts in English | 1292 | 1374 | Mistral Medium 3 |
| LMArena Vision: diagrams | Not measured | 1155 | Only Mistral Medium 3 measured |
| LMArena Vision: homework | Not measured | 1165 | Only Mistral Medium 3 measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | gpt-oss-20b | Mistral Medium 3 | 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 | 30.3 | gpt-oss-20b |
| AIME 2025 (Artificial Analysis run) | 89.3% | 30.3% | gpt-oss-20b |
| OTIS Mock AIME | 65.3% | Not measured | Only gpt-oss-20b measured |
| LMArena Text: math | 1317 | 1353 | Mistral Medium 3 |
| LMArena Text: mathematical work | 1305 | 1354 | Mistral Medium 3 |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | gpt-oss-20b | Mistral Medium 3 | 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% | 76.0% | Mistral Medium 3 |
| LMArena Text: science | 1296 | 1381 | Mistral Medium 3 |
| LMArena Text: medicine and healthcare | 1310 | 1381 | Mistral Medium 3 |
| LMArena Text: legal and government | 1280 | 1384 | Mistral Medium 3 |
| LMArena Text: business and finance | 1284 | 1352 | Mistral Medium 3 |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | gpt-oss-20b | Mistral Medium 3 | Leader |
|---|---|---|---|
| Design Arena: websites | 853 | 1079 | Mistral Medium 3 |
| Design Arena: UI components | Not measured | 1024 | Only Mistral Medium 3 measured |
| Design Arena: game dev | Not measured | 1018 | Only Mistral Medium 3 measured |
| Design Arena: data viz | 933 | 1034 | Mistral Medium 3 |
| Design Arena: 3D | Not measured | 1083 | Only Mistral Medium 3 measured |
| Design Arena: all code categories | Not measured | 1069 | Only Mistral Medium 3 measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index316
| Benchmark | gpt-oss-20b | Mistral Medium 3 | Leader |
|---|---|---|---|
| IFBench | 65.1% | 39.3% | gpt-oss-20b |
| Long context reasoning (AA-LCR) | 34.7% | 31.3% | gpt-oss-20b |
| DTBench | 68.0% | 62.3% | gpt-oss-20b |
| LMCA | 14.5% | Not measured | Only gpt-oss-20b measured |
| LMArena Text | 1288 | 1370 | Mistral Medium 3 |
| LMArena Text: instruction following | 1236 | 1338 | Mistral Medium 3 |
| LMArena Text: creative writing | 1201 | 1343 | Mistral Medium 3 |
| LMArena Vision | Not measured | 1157 | Only Mistral Medium 3 measured |
| LMArena Text: English | 1303 | 1385 | Mistral Medium 3 |
| LMArena Text: non-English | 1269 | 1353 | Mistral Medium 3 |
| LMArena Text: Chinese | 1315 | 1374 | Mistral Medium 3 |
| LMArena Text: German | 1255 | 1377 | Mistral Medium 3 |
| LMArena Text: Japanese | 1244 | 1316 | Mistral Medium 3 |
| LMArena Text: Korean | 1236 | 1304 | Mistral Medium 3 |
| LMArena Text: French | Not measured | 1368 | Only Mistral Medium 3 measured |
| LMArena Text: Spanish | 1267 | 1364 | Mistral Medium 3 |
| LMArena Text: Russian | 1278 | 1359 | Mistral Medium 3 |
| LMArena Text: Polish | 1252 | 1371 | Mistral Medium 3 |
| LMArena Text: multi-turn | 1268 | 1384 | Mistral Medium 3 |
| LMArena Text: longer queries | 1250 | 1360 | Mistral Medium 3 |
| LMArena Text: writing and language | 1238 | 1352 | Mistral Medium 3 |
| LMArena Text: entertainment and media | 1216 | 1335 | Mistral Medium 3 |
| LMArena Vision: English | Not measured | 1172 | Only Mistral Medium 3 measured |
| LMArena Vision: Chinese | Not measured | 1161 | Only Mistral Medium 3 measured |
| LMArena Vision: reading text | Not measured | 1154 | Only Mistral Medium 3 measured |
| LMArena Vision: humor | Not measured | 1145 | Only Mistral Medium 3 measured |
| LMArena Vision: captioning | Not measured | 1094 | Only Mistral Medium 3 measured |
| LMArena Vision: recognizing things | Not measured | 1141 | Only Mistral Medium 3 measured |
| LMArena Vision: creative writing | Not measured | 1146 | Only Mistral Medium 3 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 Medium 3 | 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 Medium 3 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | August 5, 2025 | May 7, 2025 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.03 | $0.40 |
| Output $/Mtok | $0.15 | $2.00 |
| 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 Medium 3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. gpt-oss-20b has the edge where output tokens cost 13.3x less, and where what matters is LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, gpt-oss-20b or Mistral Medium 3?
gpt-oss-20b costs $0.03 per million input tokens and $0.15 per million output tokens; Mistral Medium 3 costs $0.40 and $2.00. Cost per solved task is known for neither of them.
Which has the bigger context window, gpt-oss-20b or Mistral Medium 3?
Neither: both take 131K tokens.
How many benchmarks is the gpt-oss-20b vs Mistral Medium 3 comparison based on?
42 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 Medium 3's on 0. Where only one model has a result, the page says so instead of naming a winner.