Mistral Medium 3.5 vs o1
o1 leads Mistral Medium 3.5 on the Artificial Analysis Intelligence Index, 15.2 to 14.2, and Mistral Medium 3.5 costs 8.0x less per output token ($7.50 vs $60.00 per million). Updated October 21, 2026; 43 benchmarks have a result for both.
Mistral Medium 3.5 or o1: which to pick
Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; o1 has none, so the two cannot be ranked on coding.
Pick o1 for work measured by AA Intelligence Index or Epoch Capabilities Index, where it leads.
- Shared results
- 43
- Mistral Medium 3.5 only
- 38
- o1 only
- 26
- Like-for-like categories
- 7 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
Only Mistral Medium 3.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
o1
o1 leads, 15.2 against 14.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mistral Medium 3.5 has a AA Agentic Index result, 7.0.
Directional only
Reasoning
GPQA Diamond
o1
o1 leads, 73.2% against 34.9%.
Same board, both measured
Math
AIME
No call
Only o1 has a AIME result, 71.5%.
Directional only
Knowledge
MMLU Pro
o1
o1 leads, 83.5% against 75.3%.
Same board, both measured
Web design
Design Arena: websites
No call
Only Mistral Medium 3.5 has a Design Arena: websites result, 1142.
Directional only
Long documents
Listed context window
Mistral Medium 3.5
Mistral Medium 3.5 takes 262K tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Medium 3.5
Mistral Medium 3.5 $0.0053, o1 $0.045 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Mistral Medium 3.5 has the number, $6.02.
Directional only
At a glance
- Coding index
- Mistral Medium 3.5 25.5 from 3 benchmarks, o1 not benchmarked.
- Widest category
- Agentic work, 31.6 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond: Mistral Medium 3.5 34.9% against o1 73.2%, 38.4 points apart on a 0 to 100 scale.
- Output price
- Mistral Medium 3.5 $7.50 against o1 $60.00 per million output tokens, 8.0x apart.
- Context window
- Mistral Medium 3.5 takes 262K tokens, o1 200K.
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 | Mistral Medium 3.5 | Bars | o1 | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 1 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 2 | ||
| General | 20 | 2 | ||
| All shared boards | 36 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
| Benchmark | Mistral Medium 3.5 | o1 | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 61.7% | Only o1 measured |
| LMArena WebDev | 1264 | Not measured | Only Mistral Medium 3.5 measured |
| AA Coding Index | 46.9 | 39.7 | Mistral Medium 3.5 |
| LiveCodeBench (Vals AI) | Not measured | 50.3% | Only o1 measured |
| SWE-bench Verified (Vals AI) | 66.4% | Not measured | Only Mistral Medium 3.5 measured |
| Vibe Code Bench (Vals AI) | 2.9% | Not measured | Only Mistral Medium 3.5 measured |
| Code migration (Vals AI) | 5.1% | Not measured | Only Mistral Medium 3.5 measured |
| Terminal-Bench 2.1 (Vals AI) | 39.0% | Not measured | Only Mistral Medium 3.5 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 67.9% | Only o1 measured |
| SciCode | 40.2% | Not measured | Only Mistral Medium 3.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | 12.9% | Mistral Medium 3.5 |
| WeirdML | Not measured | 46.1% | Only o1 measured |
| ALE-Bench | 764.0 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: coding | 1460 | 1367 | Mistral Medium 3.5 |
| LMArena Text: software and IT | 1458 | 1366 | Mistral Medium 3.5 |
| LMArena WebDev: React | 1254 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena WebDev: HTML | 1308 | Not measured | Only Mistral Medium 3.5 measured |
| SciCode | Not measured | 4.6% | Only o1 measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Medium 3.5 | o1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.2 | 15.2 | o1 |
| Epoch Capabilities Index | 141.4 | 141.7 | o1 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mistral Medium 3.5 | o1 | Leader |
|---|---|---|---|
| AA Agentic Index | 7.0 | Not measured | Only Mistral Medium 3.5 measured |
| Finance Agent (Vals AI) | 32.1% | Not measured | Only Mistral Medium 3.5 measured |
| Tax Agent Bench (Vals AI) | 4.5% | Not measured | Only Mistral Medium 3.5 measured |
| Legal Research Bench (Vals AI) | 9.1% | Not measured | Only Mistral Medium 3.5 measured |
| Excel Modeling Benchmark (Vals AI) | 11.2% | Not measured | Only Mistral Medium 3.5 measured |
| τ²-Bench Telecom | 94.2% | 62.6% | Mistral Medium 3.5 |
| METR time horizons | Not measured | 51.1% | Only o1 measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index51
| Benchmark | Mistral Medium 3.5 | o1 | Leader |
|---|---|---|---|
| GPQA Diamond | 34.9% | 73.2% | o1 |
| MMMU Pro (Vals AI) | Not measured | 77.4% | Only o1 measured |
| GPQA Diamond (Artificial Analysis run) | 74.8% | 74.7% | Mistral Medium 3.5 |
| Humanity's Last Exam (Artificial Analysis run) | 13.8% | 7.0% | Mistral Medium 3.5 |
| GPQA Diamond (Epoch AI run) | Not measured | 76.8% | Only o1 measured |
| ARC-AGI | Not measured | 30.7% | Only o1 measured |
| SimpleBench | Not measured | 40.1% | Only o1 measured |
| Chess puzzles | Not measured | 10.0% | Only o1 measured |
| VPCT | Not measured | 37.0% | Only o1 measured |
| LMArena Text: hard prompts | 1436 | 1371 | Mistral Medium 3.5 |
| LMArena Text: expert | 1432 | 1361 | Mistral Medium 3.5 |
| LMArena Text: hard prompts in English | 1445 | 1372 | Mistral Medium 3.5 |
| LMArena Vision: diagrams | 1254 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Vision: homework | 1230 | Not measured | Only Mistral Medium 3.5 measured |
| Kagi LLM Benchmark | 41.4% | Not measured | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Mistral Medium 3.5 | o1 | Leader |
|---|---|---|---|
| AIME | Not measured | 71.5% | Only o1 measured |
| MATH 500 (Vals AI) | Not measured | 90.4% | Only o1 measured |
| MGSM (Vals AI) | Not measured | 89.3% | Only o1 measured |
| OTIS Mock AIME | Not measured | 73.3% | Only o1 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 14.7% | Only o1 measured |
| ProofBench | 9.0% | Not measured | Only Mistral Medium 3.5 measured |
| MATH level 5 | Not measured | 94.7% | Only o1 measured |
| LMArena Text: math | 1431 | 1388 | Mistral Medium 3.5 |
| LMArena Text: mathematical work | 1440 | 1390 | 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 index42
| Benchmark | Mistral Medium 3.5 | o1 | Leader |
|---|---|---|---|
| MMLU Pro | 75.3% | 83.5% | o1 |
| LegalBench (Vals AI) | Not measured | 80.4% | Only o1 measured |
| CaseLaw (Vals AI) | 44.2% | Not measured | Only Mistral Medium 3.5 measured |
| CorpFin (Vals AI) | 58.8% | Not measured | Only Mistral Medium 3.5 measured |
| TaxEval (Vals AI) | 68.0% | 74.3% | o1 |
| MortgageTax (Vals AI) | 28.9% | Not measured | Only Mistral Medium 3.5 measured |
| MedQA (Vals AI) | Not measured | 96.5% | Only o1 measured |
| MedCode (Vals AI) | 33.8% | Not measured | Only Mistral Medium 3.5 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 84.1% | Only o1 measured |
| SimpleQA Verified | Not measured | 41.1% | Only o1 measured |
| GeoBench | Not measured | 80.0% | Only o1 measured |
| LMArena Text: science | 1428 | 1369 | Mistral Medium 3.5 |
| LMArena Text: medicine and healthcare | 1409 | 1355 | Mistral Medium 3.5 |
| LMArena Text: legal and government | 1437 | 1368 | Mistral Medium 3.5 |
| LMArena Text: business and finance | 1426 | 1328 | Mistral Medium 3.5 |
| Neither model has a result on the other 3 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Mistral Medium 3.5 | o1 | Leader |
|---|---|---|---|
| Design Arena: websites | 1142 | Not measured | Only Mistral Medium 3.5 measured |
| Design Arena: UI components | 1079 | Not measured | Only Mistral Medium 3.5 measured |
| Design Arena: game dev | 1079 | Not measured | Only Mistral Medium 3.5 measured |
| Design Arena: data viz | 1120 | Not measured | Only Mistral Medium 3.5 measured |
| Design Arena: 3D | 1061 | Not measured | Only Mistral Medium 3.5 measured |
| Design Arena: all code categories | 1121 | Not measured | Only Mistral Medium 3.5 measured |
| Design Arena: SVG | 950 | Not measured | Only Mistral Medium 3.5 measured |
| Design Arena: ASCII art | 1063 | Not measured | 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 index202
| Benchmark | Mistral Medium 3.5 | o1 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 37.6% | Not measured | Only Mistral Medium 3.5 measured |
| MedScribe (Vals AI) | 67.7% | Not measured | Only Mistral Medium 3.5 measured |
| Vals Multimodal Index | 34.8% | Not measured | Only Mistral Medium 3.5 measured |
| IFBench | 68.8% | 70.3% | o1 |
| Long context reasoning (AA-LCR) | 69.3% | 65.0% | Mistral Medium 3.5 |
| DTBench | 75.5% | 74.7% | Mistral Medium 3.5 |
| LMCA | 26.1% | 22.3% | Mistral Medium 3.5 |
| Fiction.LiveBench | Not measured | 83.3% | Only o1 measured |
| Creative writing (Lech Mazur) | Not measured | 70.2% | Only o1 measured |
| Surface Evolver Bench | 26.9% | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text | 1421 | 1366 | Mistral Medium 3.5 |
| LMArena Text: instruction following | 1415 | 1367 | Mistral Medium 3.5 |
| LMArena Text: creative writing | 1374 | 1347 | Mistral Medium 3.5 |
| LMArena Vision | 1222 | 1168 | Mistral Medium 3.5 |
| LMArena Text: English | 1433 | 1372 | Mistral Medium 3.5 |
| LMArena Text: non-English | 1404 | 1358 | Mistral Medium 3.5 |
| LMArena Text: Chinese | 1444 | 1394 | Mistral Medium 3.5 |
| LMArena Text: German | 1453 | 1337 | Mistral Medium 3.5 |
| LMArena Text: Japanese | Not measured | 1346 | Only o1 measured |
| LMArena Text: Korean | 1384 | 1396 | o1 |
| LMArena Text: French | 1448 | 1344 | Mistral Medium 3.5 |
| LMArena Text: Spanish | 1411 | 1345 | Mistral Medium 3.5 |
| LMArena Text: Russian | 1395 | 1356 | Mistral Medium 3.5 |
| LMArena Text: Polish | 1430 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: multi-turn | 1423 | 1355 | Mistral Medium 3.5 |
| LMArena Text: longer queries | 1415 | 1378 | Mistral Medium 3.5 |
| LMArena Text: writing and language | 1384 | 1360 | Mistral Medium 3.5 |
| LMArena Text: entertainment and media | 1382 | 1358 | Mistral Medium 3.5 |
| LMArena Vision: English | 1240 | 1162 | Mistral Medium 3.5 |
| LMArena Vision: Chinese | 1244 | 1235 | Mistral Medium 3.5 |
| LMArena Vision: reading text | 1233 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Vision: humor | 1216 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Vision: creative writing | 1198 | Not measured | 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 | Mistral Medium 3.5 | o1 | 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 Medium 3.5 costs less
Repository review
50K input + 3K output tokens
Mistral Medium 3.5 costs less
Agent loop
200K input + 10K output tokens
Mistral Medium 3.5 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 | Mistral Medium 3.5 | OpenAI o1 |
|---|---|---|
| Maker | Mistral AI | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | April 30, 2026 | December 17, 2024 |
| Context window | 262K | 200K |
| Input $/Mtok | $1.50 | $15.00 |
| Output $/Mtok | $7.50 | $60.00 |
| Coding index | 25.5 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $6.02 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Medium 3.5 better than o1 for coding?
Only Mistral Medium 3.5 has a coding score, 25.5 on the Vibe Coding Index; o1 has none, so the two cannot be ranked on coding. Pick o1 for work measured by AA Intelligence Index or Epoch Capabilities Index, where it leads.
Which is cheaper, Mistral Medium 3.5 or o1?
Mistral Medium 3.5 costs $1.50 per million input tokens and $7.50 per million output tokens; o1 costs $15.00 and $60.00. Cost per solved task is known for Mistral Medium 3.5 only.
Which has the bigger context window, Mistral Medium 3.5 or o1?
Mistral Medium 3.5, with 262K tokens against 200K.
How many benchmarks is the Mistral Medium 3.5 vs o1 comparison based on?
43 benchmarks have a result for both models as of October 21, 2026. Mistral Medium 3.5's coding index rests on 3 independent benchmarks and o1's on 0. Where only one model has a result, the page says so instead of naming a winner.