Mistral Medium 3.5 vs Qwen3 32B
Updated October 21, 2026. 34 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Qwen3 32B for coding: it scores 40.0 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 SciCode or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 34
- Mistral Medium 3.5 only
- 46
- Qwen3 32B only
- 8
- Like-for-like categories
- 7 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
Qwen3 32B
Qwen3 32B scores 40.0 against 9.2.
One benchmark behind each score
Intelligence
AA Intelligence Index
Mistral Medium 3.5
Mistral Medium 3.5 leads, 14.2 against 7.3.
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
No call
Only Mistral Medium 3.5 has a GPQA Diamond result, 34.9%.
Directional only
Knowledge
MMLU Pro
No call
Only Mistral Medium 3.5 has a MMLU Pro result, 75.3%.
Directional only
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 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 32B
Mistral Medium 3.5 $0.0053, Qwen3 32B $0.0002 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 9.2 from 1 benchmark, Qwen3 32B 40.0 from 1 benchmark.
- Widest category
- Agentic work, 64.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Mistral Medium 3.5 69.3% against Qwen3 32B 0.0%, 69.3 points apart on a 0 to 100 scale.
- Output price
- Mistral Medium 3.5 $7.50 against Qwen3 32B $0.28 per million output tokens, 26.8x apart.
- Context window
- Mistral Medium 3.5 takes 262K tokens, Qwen3 32B 131K.
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 32B | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 16 | 0 | ||
| All shared boards | 34 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index40
| Benchmark | Mistral Medium 3.5 | Qwen3 32B | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 40.0% | Only Qwen3 32B measured |
| LMArena WebDev | 1264 | Not measured | Only Mistral Medium 3.5 measured |
| AA Coding Index | 46.9 | Not measured | Only Mistral Medium 3.5 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 | 54.6% | Only Qwen3 32B measured |
| SciCode | 40.2% | 36.0% | Mistral Medium 3.5 |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | 3.0% | Mistral Medium 3.5 |
| ALE-Bench | 764.0 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: coding | 1460 | 1357 | 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 |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Mistral Medium 3.5 | Qwen3 32B | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.2 | 7.3 | Mistral Medium 3.5 |
| Epoch Capabilities Index | 141.4 | 138.4 | Mistral Medium 3.5 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mistral Medium 3.5 | Qwen3 32B | 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% | 29.8% | Mistral Medium 3.5 |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Mistral Medium 3.5 | Qwen3 32B | Leader |
|---|---|---|---|
| GPQA Diamond | 34.9% | Not measured | Only Mistral Medium 3.5 measured |
| GPQA Diamond (Artificial Analysis run) | 74.8% | 66.8% | Mistral Medium 3.5 |
| Humanity's Last Exam (Artificial Analysis run) | 13.8% | 7.4% | Mistral Medium 3.5 |
| GPQA Diamond (Epoch AI run) | Not measured | 65.7% | Only Qwen3 32B measured |
| LMArena Text: hard prompts | 1436 | 1334 | Mistral Medium 3.5 |
| LMArena Text: expert | 1432 | 1362 | Mistral Medium 3.5 |
| LMArena Text: hard prompts in English | 1445 | 1350 | 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 |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Mistral Medium 3.5 | Qwen3 32B | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 73.0 | Only Qwen3 32B measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 73.0% | Only Qwen3 32B measured |
| OTIS Mock AIME | Not measured | 66.9% | Only Qwen3 32B measured |
| ProofBench | 9.0% | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: math | 1431 | 1399 | Mistral Medium 3.5 |
| LMArena Text: mathematical work | 1440 | 1420 | Mistral Medium 3.5 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Mistral Medium 3.5 | Qwen3 32B | Leader |
|---|---|---|---|
| MMLU Pro | 75.3% | Not measured | Only Mistral Medium 3.5 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% | Not measured | Only Mistral Medium 3.5 measured |
| MortgageTax (Vals AI) | 28.9% | Not measured | Only Mistral Medium 3.5 measured |
| MedCode (Vals AI) | 33.8% | Not measured | Only Mistral Medium 3.5 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 79.8% | Only Qwen3 32B measured |
| LMArena Text: science | 1428 | 1372 | Mistral Medium 3.5 |
| LMArena Text: medicine and healthcare | 1409 | 1368 | Mistral Medium 3.5 |
| LMArena Text: legal and government | 1437 | 1324 | Mistral Medium 3.5 |
| LMArena Text: business and finance | 1426 | 1318 | Mistral Medium 3.5 |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Mistral Medium 3.5 | Qwen3 32B | 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 | 1122 | Not measured | Only Mistral Medium 3.5 measured |
| Design Arena: SVG | 949 | Not measured | Only Mistral Medium 3.5 measured |
| Design Arena: ASCII art | 1062 | 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 index160
| Benchmark | Mistral Medium 3.5 | Qwen3 32B | 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% | 36.3% | Mistral Medium 3.5 |
| Long context reasoning (AA-LCR) | 69.3% | 0.0% | Mistral Medium 3.5 |
| DTBench | 75.5% | 67.5% | Mistral Medium 3.5 |
| LMCA | 26.1% | 17.3% | Mistral Medium 3.5 |
| Fiction.LiveBench | Not measured | 74.2% | Only Qwen3 32B measured |
| Surface Evolver Bench | 26.9% | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text | 1421 | 1340 | Mistral Medium 3.5 |
| LMArena Text: instruction following | 1415 | 1305 | Mistral Medium 3.5 |
| LMArena Text: creative writing | 1374 | 1297 | Mistral Medium 3.5 |
| LMArena Vision | 1222 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: English | 1433 | 1359 | Mistral Medium 3.5 |
| LMArena Text: non-English | 1404 | 1317 | Mistral Medium 3.5 |
| LMArena Text: Chinese | 1444 | 1357 | Mistral Medium 3.5 |
| LMArena Text: German | 1453 | 1341 | Mistral Medium 3.5 |
| 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 | 1311 | Mistral Medium 3.5 |
| LMArena Text: Polish | 1430 | Not measured | Only Mistral Medium 3.5 measured |
| LMArena Text: multi-turn | 1423 | 1331 | Mistral Medium 3.5 |
| LMArena Text: longer queries | 1415 | 1327 | Mistral Medium 3.5 |
| LMArena Text: writing and language | 1384 | 1298 | Mistral Medium 3.5 |
| LMArena Text: entertainment and media | 1382 | 1279 | Mistral Medium 3.5 |
| 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 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Medium 3.5 | Qwen3 32B | 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 32B costs less
Repository review
50K input + 3K output tokens
Qwen3 32B costs less
Agent loop
200K input + 10K output tokens
Qwen3 32B 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 32B |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | April 30, 2026 | April 28, 2025 |
| Context window | 262K | 131K |
| Input $/Mtok | $1.50 | $0.08 |
| Output $/Mtok | $7.50 | $0.28 |
| Coding index | 9.2 | 40.0 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $6.02 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Medium 3.5 better than Qwen3 32B for coding?
Pick Qwen3 32B for coding: it scores 40.0 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 SciCode or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Mistral Medium 3.5 or Qwen3 32B?
Mistral Medium 3.5 costs $1.50 per million input tokens and $7.50 per million output tokens; Qwen3 32B costs $0.08 and $0.28. Cost per solved task is known for Mistral Medium 3.5 only.
Which has the bigger context window, Mistral Medium 3.5 or Qwen3 32B?
Mistral Medium 3.5, with 262K tokens against 131K.
How many benchmarks is the Mistral Medium 3.5 vs Qwen3 32B comparison based on?
34 benchmarks have a result for both models as of October 21, 2026. Mistral Medium 3.5's coding index rests on 1 independent benchmark and Qwen3 32B's on 1. Where only one model has a result, the page says so instead of naming a winner.