Mistral Medium 3.5 vs Qwen3.7 Max
Qwen3.7 Max leads Mistral Medium 3.5 on coding, 54.1 to 25.5 on the Vibe Coding Index, and Qwen3.7 Max costs 1.7x less per output token ($4.425 vs $7.50 per million). Updated October 22, 2026; 40 benchmarks have a result for both.
Mistral Medium 3.5 or Qwen3.7 Max: which to pick
Pick Qwen3.7 Max for coding: it scores 54.1 on the Vibe Coding Index against 25.5 for Mistral Medium 3.5.
Mistral Medium 3.5 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
- Shared results
- 40
- Mistral Medium 3.5 only
- 41
- Qwen3.7 Max only
- 19
- Like-for-like categories
- 8 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
Qwen3.7 Max
Qwen3.7 Max scores 54.1 against 25.5.
3 benchmarks behind each score
Intelligence
AA Intelligence Index
Qwen3.7 Max
Qwen3.7 Max leads, 29.5 against 14.2.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.7 Max
Qwen3.7 Max leads, 22.5 against 7.0.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.7 Max
Qwen3.7 Max leads, 90.2% against 34.9%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.7 Max
Qwen3.7 Max leads, 89.3% against 75.3%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.7 Max
Qwen3.7 Max leads, 1274 against 1142.
Same board, both measured
Long documents
Listed context window
Qwen3.7 Max
Qwen3.7 Max takes 1M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.7 Max
Mistral Medium 3.5 $0.0053, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Qwen3.7 Max
Mistral Medium 3.5 $6.02, Qwen3.7 Max $4.69.
Published attempt cost and pass rate
At a glance
- Coding index
- Mistral Medium 3.5 25.5 from 3 benchmarks, Qwen3.7 Max 54.1 from 3 benchmarks.
- Widest category
- Reasoning, 33.2 points apart on average over 3 shared benchmarks.
- Widest single gap
- GPQA Diamond: Mistral Medium 3.5 34.9% against Qwen3.7 Max 90.2%, 55.3 points apart on a 0 to 100 scale.
- Output price
- Mistral Medium 3.5 $7.50 against Qwen3.7 Max $4.425 per million output tokens, 1.7x apart.
- Context window
- Mistral Medium 3.5 takes 262K tokens, Qwen3.7 Max 1M.
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 | Qwen3.7 Max | Ties |
|---|---|---|---|---|
| Coding | 0 | 11 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 6 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 1 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 8 | ||
| General | 0 | 5 | ||
| All shared boards | 0 | 40 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index011
| Benchmark | Mistral Medium 3.5 | Qwen3.7 Max | Leader |
|---|---|---|---|
| LMArena WebDev | 1264 | 1515 | Qwen3.7 Max |
| AA Coding Index | 46.9 | 66.0 | Qwen3.7 Max |
| LiveCodeBench (Vals AI) | Not measured | 87.1% | Only Qwen3.7 Max measured |
| SWE-bench Verified (Vals AI) | 66.4% | 68.8% | Qwen3.7 Max |
| Vibe Code Bench (Vals AI) | 2.9% | 47.7% | Qwen3.7 Max |
| Code migration (Vals AI) | 5.1% | 13.1% | Qwen3.7 Max |
| Terminal-Bench 2.1 (Vals AI) | 39.0% | 61.0% | Qwen3.7 Max |
| SciCode | 40.2% | 49.5% | Qwen3.7 Max |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | 50.8% | Qwen3.7 Max |
| ALE-Bench | 764.0 | 1189.4 | Qwen3.7 Max |
| LMArena Text: coding | 1460 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: software and IT | 1458 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena WebDev: React | 1254 | 1514 | Qwen3.7 Max |
| LMArena WebDev: HTML | 1308 | 1512 | Qwen3.7 Max |
| Neither model has a result on the other 25 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Medium 3.5 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.2 | 29.5 | Qwen3.7 Max |
| Epoch Capabilities Index | 141.4 | 154.0 | Qwen3.7 Max |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Mistral Medium 3.5 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Agentic Index | 7.0 | 22.5 | Qwen3.7 Max |
| Design Arena: agents building web apps | Not measured | 1208 | Only Qwen3.7 Max measured |
| Design Arena: agents building full-stack apps | Not measured | 1191 | Only Qwen3.7 Max measured |
| Design Arena: agents building mobile apps | Not measured | 1155 | Only Qwen3.7 Max measured |
| Design Arena: agents building Android apps | Not measured | 1159 | Only Qwen3.7 Max measured |
| Design Arena: agents building Godot games | Not measured | 1187 | Only Qwen3.7 Max measured |
| Design Arena: agents building games | Not measured | 1161 | Only Qwen3.7 Max measured |
| Design Arena: HTML slides | Not measured | 1108 | Only Qwen3.7 Max measured |
| Design Arena: PowerPoint slides | Not measured | 1171 | Only Qwen3.7 Max measured |
| Finance Agent (Vals AI) | 32.1% | 47.8% | Qwen3.7 Max |
| Tax Agent Bench (Vals AI) | 4.5% | 15.8% | Qwen3.7 Max |
| Legal Research Bench (Vals AI) | 9.1% | 25.5% | Qwen3.7 Max |
| Excel Modeling Benchmark (Vals AI) | 11.2% | 57.0% | Qwen3.7 Max |
| τ²-Bench Telecom | 94.2% | 94.7% | Qwen3.7 Max |
| Neither model has a result on the other 27 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Mistral Medium 3.5 | Qwen3.7 Max | Leader |
|---|---|---|---|
| GPQA Diamond | 34.9% | 90.2% | Qwen3.7 Max |
| GPQA Diamond (Artificial Analysis run) | 74.8% | 92.3% | Qwen3.7 Max |
| Humanity's Last Exam (Artificial Analysis run) | 13.8% | 40.5% | Qwen3.7 Max |
| GPQA Diamond (Epoch AI run) | Not measured | 90.9% | Only Qwen3.7 Max measured |
| SimpleBench | Not measured | 70.4% | Only Qwen3.7 Max measured |
| Chess puzzles | Not measured | 26.0% | Only Qwen3.7 Max measured |
| Mystery game puzzles | Not measured | 32.0% | Only Qwen3.7 Max measured |
| EBR-bench | Not measured | 9.5% | Only Qwen3.7 Max measured |
| LMArena Text: hard prompts | 1436 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: expert | 1432 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: hard prompts in English | 1445 | Not measured | Only Mistral Medium 3.5 measured |
| 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 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Medium 3.5 | Qwen3.7 Max | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 95.6% | Only Qwen3.7 Max measured |
| FrontierMath, tiers 1 to 3 | Not measured | 64.6% | Only Qwen3.7 Max measured |
| FrontierMath, tier 4 | Not measured | 34.1% | Only Qwen3.7 Max measured |
| ProofBench | 9.0% | 26.0% | Qwen3.7 Max |
| LMArena Text: math | 1431 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: mathematical work | 1440 | Not measured | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Mistral Medium 3.5 | Qwen3.7 Max | Leader |
|---|---|---|---|
| MMLU Pro | 75.3% | 89.3% | Qwen3.7 Max |
| LegalBench (Vals AI) | Not measured | 84.9% | Only Qwen3.7 Max measured |
| CaseLaw (Vals AI) | 44.2% | Not measured | Only Mistral Medium 3.5 measured |
| CorpFin (Vals AI) | 58.8% | 63.7% | Qwen3.7 Max |
| TaxEval (Vals AI) | 68.0% | 75.3% | Qwen3.7 Max |
| MortgageTax (Vals AI) | 28.9% | Not measured | Only Mistral Medium 3.5 measured |
| MedCode (Vals AI) | 33.8% | 38.8% | Qwen3.7 Max |
| SimpleQA Verified | Not measured | 55.8% | Only Qwen3.7 Max measured |
| LMArena Text: science | 1428 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: medicine and healthcare | 1409 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: legal and government | 1437 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: business and finance | 1426 | Not measured | Only Mistral Medium 3.5 measured |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | Mistral Medium 3.5 | Qwen3.7 Max | Leader |
|---|---|---|---|
| Design Arena: websites | 1142 | 1274 | Qwen3.7 Max |
| Design Arena: UI components | 1076 | 1267 | Qwen3.7 Max |
| Design Arena: game dev | 1079 | 1264 | Qwen3.7 Max |
| Design Arena: data viz | 1120 | 1275 | Qwen3.7 Max |
| Design Arena: 3D | 1060 | 1265 | Qwen3.7 Max |
| Design Arena: all code categories | 1121 | 1275 | Qwen3.7 Max |
| Design Arena: SVG | 949 | 1216 | Qwen3.7 Max |
| Design Arena: ASCII art | 1062 | 1211 | Qwen3.7 Max |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Mistral Medium 3.5 | Qwen3.7 Max | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 37.6% | Not measured | Only Mistral Medium 3.5 measured |
| MedScribe (Vals AI) | 67.7% | 79.4% | Qwen3.7 Max |
| Vals Multimodal Index | 34.8% | Not measured | Only Mistral Medium 3.5 measured |
| IFBench | 68.8% | 80.5% | Qwen3.7 Max |
| Long context reasoning (AA-LCR) | 69.3% | 79.0% | Qwen3.7 Max |
| DTBench | 75.5% | 92.3% | Qwen3.7 Max |
| LMCA | 26.1% | 44.0% | Qwen3.7 Max |
| Surface Evolver Bench | 26.9% | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text | 1421 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: instruction following | 1415 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: creative writing | 1374 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Vision | 1222 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: English | 1433 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: non-English | 1404 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: Chinese | 1444 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: German | 1453 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: Korean | 1384 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: French | 1448 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: Spanish | 1411 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: Russian | 1395 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: Polish | 1430 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: multi-turn | 1423 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: longer queries | 1415 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: writing and language | 1384 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: entertainment and media | 1382 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Vision: English | 1240 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Vision: Chinese | 1244 | Not measured | Only Mistral Medium 3.5 measured |
| 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 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Medium 3.5 | Qwen3.7 Max | 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
Qwen3.7 Max costs less
Repository review
50K input + 3K output tokens
Qwen3.7 Max costs less
Agent loop
200K input + 10K output tokens
Qwen3.7 Max 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 | Qwen3.7 Max |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | April 30, 2026 | May 21, 2026 |
| Context window | 262K | 1M |
| Input $/Mtok | $1.50 | $1.475 |
| Output $/Mtok | $7.50 | $4.425 |
| Coding index | 25.5 | 54.1 |
| Independent measurements behind it | 3 | 3 |
| Cost per solved task | $6.02 | $4.69 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Medium 3.5 better than Qwen3.7 Max for coding?
Pick Qwen3.7 Max for coding: it scores 54.1 on the Vibe Coding Index against 25.5 for Mistral Medium 3.5. Mistral Medium 3.5 is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
Which is cheaper, Mistral Medium 3.5 or Qwen3.7 Max?
Mistral Medium 3.5 costs $1.50 per million input tokens and $7.50 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Per solved SWE-bench Verified task, Mistral Medium 3.5 costs $6.02 and Qwen3.7 Max $4.69.
Which has the bigger context window, Mistral Medium 3.5 or Qwen3.7 Max?
Qwen3.7 Max, with 1M tokens against 262K.
How many benchmarks is the Mistral Medium 3.5 vs Qwen3.7 Max comparison based on?
40 benchmarks have a result for both models as of October 22, 2026. Mistral Medium 3.5's coding index rests on 3 independent benchmarks and Qwen3.7 Max's on 3. Where only one model has a result, the page says so instead of naming a winner.