gpt-oss-120b vs Mistral Medium 3.5
Updated October 21, 2026. 54 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick gpt-oss-120b for coding: it scores 34.5 on the Vibe Coding Index against 9.2 for Mistral Medium 3.5.
Pick Mistral Medium 3.5 when the context window is larger, 262K against 131K tokens, or when what matters is AA Coding Index or SWE-bench Verified (Vals AI), where it leads.
- Shared results
- 54
- gpt-oss-120b only
- 24
- Mistral Medium 3.5 only
- 26
- 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, four public coding benchmarks
gpt-oss-120b
gpt-oss-120b scores 34.5 against 9.2.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
Mistral Medium 3.5
Mistral Medium 3.5 leads, 14.2 against 11.6.
Same board, both measured
Agentic work
AA Agentic Index
Mistral Medium 3.5
Mistral Medium 3.5 leads, 7.0 against 3.7.
Same board, both measured
Reasoning
GPQA Diamond
gpt-oss-120b
gpt-oss-120b leads, 78.5% against 34.9%.
Same board, both measured
Math
AIME
No call
Only gpt-oss-120b has a AIME result, 92.6%.
Directional only
Knowledge
MMLU Pro
gpt-oss-120b
gpt-oss-120b leads, 79.2% against 75.3%.
Same board, both measured
Web design
Design Arena: websites
Mistral Medium 3.5
Mistral Medium 3.5 leads, 1142 against 968.
Same board, both measured
Long documents
Listed context window
Mistral Medium 3.5
Mistral Medium 3.5 takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
gpt-oss-120b
gpt-oss-120b $0.0004, Mistral Medium 3.5 $0.0053 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
gpt-oss-120b
gpt-oss-120b $0.51, Mistral Medium 3.5 $6.02.
Published attempt cost and pass rate
At a glance
- Coding index
- gpt-oss-120b 34.5 from 2 benchmarks, Mistral Medium 3.5 9.2 from 1 benchmark.
- Widest category
- Web design, 18.0 points apart on average over 6 shared benchmarks.
- Widest single gap
- GPQA Diamond: gpt-oss-120b 78.5% against Mistral Medium 3.5 34.9%, 43.7 points apart on a 0 to 100 scale.
- Output price
- gpt-oss-120b $0.60 against Mistral Medium 3.5 $7.50 per million output tokens, 12.5x apart.
- Context window
- gpt-oss-120b takes 131K tokens, Mistral Medium 3.5 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 Medium 3.5 | Ties |
|---|---|---|---|---|
| Coding | 0 | 7 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 2 | ||
| Reasoning | 3 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 3 | 5 | ||
| Web design | 0 | 6 | ||
| General | 2 | 19 | ||
| All shared boards | 8 | 46 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index07
| Benchmark | gpt-oss-120b | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| Aider polyglot | 41.8% | Not measured | Only gpt-oss-120b measured |
| SWE-bench Verified | 26.0% | Not measured | Only gpt-oss-120b measured |
| LMArena WebDev | Not measured | 1264 | Only Mistral Medium 3.5 measured |
| AA Coding Index | 30.4 | 46.9 | Mistral Medium 3.5 |
| LiveCodeBench (Vals AI) | 83.2% | Not measured | Only gpt-oss-120b measured |
| SWE-bench Verified (Vals AI) | 33.6% | 66.4% | Mistral Medium 3.5 |
| Vibe Code Bench (Vals AI) | Not measured | 2.9% | Only Mistral Medium 3.5 measured |
| Code migration (Vals AI) | Not measured | 5.1% | Only Mistral Medium 3.5 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 39.0% | Only Mistral Medium 3.5 measured |
| LiveCodeBench (Artificial Analysis run) | 87.8% | Not measured | Only gpt-oss-120b measured |
| SciCode | 34.0% | 40.2% | Mistral Medium 3.5 |
| Terminal-Bench Hard (Artificial Analysis run) | 23.5% | 33.3% | Mistral Medium 3.5 |
| 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 | 764.0 | Mistral Medium 3.5 |
| LMArena Text: coding | 1379 | 1460 | Mistral Medium 3.5 |
| LMArena Text: software and IT | 1387 | 1458 | Mistral Medium 3.5 |
| LMArena WebDev: React | Not measured | 1254 | Only Mistral Medium 3.5 measured |
| LMArena WebDev: HTML | Not measured | 1308 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | gpt-oss-120b | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 11.6 | 14.2 | Mistral Medium 3.5 |
| Epoch Capabilities Index | 139.8 | 141.4 | Mistral Medium 3.5 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | gpt-oss-120b | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| AA Agentic Index | 3.7 | 7.0 | Mistral Medium 3.5 |
| Finance Agent (Vals AI) | Not measured | 32.1% | Only Mistral Medium 3.5 measured |
| Tax Agent Bench (Vals AI) | Not measured | 4.5% | Only Mistral Medium 3.5 measured |
| Legal Research Bench (Vals AI) | Not measured | 9.1% | Only Mistral Medium 3.5 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 11.2% | Only Mistral Medium 3.5 measured |
| τ²-Bench Telecom | 65.8% | 94.2% | Mistral Medium 3.5 |
| 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 |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index33
| Benchmark | gpt-oss-120b | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 78.5% | 34.9% | gpt-oss-120b |
| GPQA Diamond (Artificial Analysis run) | 78.2% | 74.8% | gpt-oss-120b |
| Humanity's Last Exam (Artificial Analysis run) | 19.6% | 13.8% | gpt-oss-120b |
| 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 | 1436 | Mistral Medium 3.5 |
| LMArena Text: expert | 1356 | 1432 | Mistral Medium 3.5 |
| LMArena Text: hard prompts in English | 1368 | 1445 | Mistral Medium 3.5 |
| LMArena Vision: diagrams | Not measured | 1254 | Only Mistral Medium 3.5 measured |
| LMArena Vision: homework | Not measured | 1230 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | gpt-oss-120b | Mistral Medium 3.5 | 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 |
| ProofBench | Not measured | 9.0% | Only Mistral Medium 3.5 measured |
| LMArena Text: math | 1389 | 1431 | Mistral Medium 3.5 |
| LMArena Text: mathematical work | 1386 | 1440 | Mistral Medium 3.5 |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index35
| Benchmark | gpt-oss-120b | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| MMLU Pro | 79.2% | 75.3% | gpt-oss-120b |
| LegalBench (Vals AI) | 75.9% | Not measured | Only gpt-oss-120b measured |
| CaseLaw (Vals AI) | 48.8% | 44.2% | gpt-oss-120b |
| CorpFin (Vals AI) | 58.2% | 58.8% | Mistral Medium 3.5 |
| TaxEval (Vals AI) | 71.6% | 68.0% | gpt-oss-120b |
| MortgageTax (Vals AI) | Not measured | 28.9% | Only Mistral Medium 3.5 measured |
| MedQA (Vals AI) | 91.4% | Not measured | Only gpt-oss-120b measured |
| MedCode (Vals AI) | Not measured | 33.8% | Only Mistral Medium 3.5 measured |
| MMLU Pro (Artificial Analysis run) | 80.8% | Not measured | Only gpt-oss-120b measured |
| LMArena Text: science | 1382 | 1428 | Mistral Medium 3.5 |
| LMArena Text: medicine and healthcare | 1388 | 1409 | Mistral Medium 3.5 |
| LMArena Text: legal and government | 1355 | 1437 | Mistral Medium 3.5 |
| LMArena Text: business and finance | 1359 | 1426 | Mistral Medium 3.5 |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | gpt-oss-120b | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 968 | 1142 | Mistral Medium 3.5 |
| Design Arena: UI components | 927 | 1079 | Mistral Medium 3.5 |
| Design Arena: game dev | 992 | 1079 | Mistral Medium 3.5 |
| Design Arena: data viz | 987 | 1120 | Mistral Medium 3.5 |
| Design Arena: 3D | 901 | 1061 | Mistral Medium 3.5 |
| Design Arena: all code categories | 963 | 1122 | Mistral Medium 3.5 |
| Design Arena: SVG | Not measured | 949 | Only Mistral Medium 3.5 measured |
| Design Arena: ASCII art | Not measured | 1062 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index219
| Benchmark | gpt-oss-120b | Mistral Medium 3.5 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 37.6% | Only Mistral Medium 3.5 measured |
| MedScribe (Vals AI) | Not measured | 67.7% | Only Mistral Medium 3.5 measured |
| Vals Multimodal Index | Not measured | 34.8% | Only Mistral Medium 3.5 measured |
| IFBench | 69.0% | 68.8% | gpt-oss-120b |
| Long context reasoning (AA-LCR) | 52.0% | 69.3% | Mistral Medium 3.5 |
| DTBench | 76.3% | 75.5% | gpt-oss-120b |
| LMCA | 22.1% | 26.1% | Mistral Medium 3.5 |
| 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% | 26.9% | Mistral Medium 3.5 |
| LMArena Text | 1366 | 1421 | Mistral Medium 3.5 |
| LMArena Text: instruction following | 1318 | 1415 | Mistral Medium 3.5 |
| LMArena Text: creative writing | 1274 | 1374 | Mistral Medium 3.5 |
| LMArena Vision | Not measured | 1222 | Only Mistral Medium 3.5 measured |
| LMArena Text: English | 1375 | 1433 | Mistral Medium 3.5 |
| LMArena Text: non-English | 1351 | 1404 | Mistral Medium 3.5 |
| LMArena Text: Chinese | 1385 | 1444 | Mistral Medium 3.5 |
| LMArena Text: German | 1353 | 1453 | Mistral Medium 3.5 |
| LMArena Text: Japanese | 1331 | Not measured | Only gpt-oss-120b measured |
| LMArena Text: Korean | 1282 | 1384 | Mistral Medium 3.5 |
| LMArena Text: French | 1370 | 1448 | Mistral Medium 3.5 |
| LMArena Text: Spanish | 1389 | 1411 | Mistral Medium 3.5 |
| LMArena Text: Russian | 1343 | 1395 | Mistral Medium 3.5 |
| LMArena Text: Polish | 1353 | 1430 | Mistral Medium 3.5 |
| LMArena Text: multi-turn | 1340 | 1423 | Mistral Medium 3.5 |
| LMArena Text: longer queries | 1319 | 1415 | Mistral Medium 3.5 |
| LMArena Text: writing and language | 1306 | 1384 | Mistral Medium 3.5 |
| LMArena Text: entertainment and media | 1292 | 1382 | Mistral Medium 3.5 |
| LMArena Vision: English | Not measured | 1240 | Only Mistral Medium 3.5 measured |
| LMArena Vision: Chinese | Not measured | 1244 | Only Mistral Medium 3.5 measured |
| LMArena Vision: reading text | Not measured | 1233 | Only Mistral Medium 3.5 measured |
| LMArena Vision: humor | Not measured | 1216 | Only Mistral Medium 3.5 measured |
| LMArena Vision: creative writing | Not measured | 1198 | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | gpt-oss-120b | Mistral Medium 3.5 | 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 Medium 3.5 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | August 5, 2025 | April 30, 2026 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.15 | $1.50 |
| Output $/Mtok | $0.60 | $7.50 |
| Coding index | 34.5 | 9.2 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $0.51 | $6.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is gpt-oss-120b better than Mistral Medium 3.5 for coding?
Pick gpt-oss-120b for coding: it scores 34.5 on the Vibe Coding Index against 9.2 for Mistral Medium 3.5. Pick Mistral Medium 3.5 when the context window is larger, 262K against 131K tokens, or when what matters is AA Coding Index or SWE-bench Verified (Vals AI), where it leads.
Which is cheaper, gpt-oss-120b or Mistral Medium 3.5?
gpt-oss-120b costs $0.15 per million input tokens and $0.60 per million output tokens; Mistral Medium 3.5 costs $1.50 and $7.50. Per solved SWE-bench Verified task, gpt-oss-120b costs $0.51 and Mistral Medium 3.5 $6.02.
Which has the bigger context window, gpt-oss-120b or Mistral Medium 3.5?
Mistral Medium 3.5, with 262K tokens against 131K.
How many benchmarks is the gpt-oss-120b vs Mistral Medium 3.5 comparison based on?
54 benchmarks have a result for both models as of October 21, 2026. gpt-oss-120b's coding index rests on 2 independent benchmarks and Mistral Medium 3.5's on 1. Where only one model has a result, the page says so instead of naming a winner.