Mistral Large vs Qwen3 32B
Updated October 21, 2026. 30 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.
Mistral Large has the edge where what matters is LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
- Shared results
- 30
- Mistral Large only
- 14
- Qwen3 32B only
- 12
- Like-for-like categories
- 6 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
Qwen3 32B
Qwen3 32B leads, 7.3 against 5.8.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Mistral Large has a GPQA Diamond result, 47.7%.
Directional only
Math
AIME
No call
Only Mistral Large has a AIME result, 9.2%.
Directional only
Knowledge
MMLU Pro
No call
Only Mistral Large has a MMLU Pro result, 69.7%.
Directional only
Long documents
Listed context window
Qwen3 32B
Qwen3 32B takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 32B
Mistral Large $0.0050, Qwen3 32B $0.0003 per turn.
List prices
At a glance
- Coding index
- Mistral Large not benchmarked, Qwen3 32B not benchmarked.
- Widest category
- Math, 25.0 points apart on average over 2 shared benchmarks.
- Widest single gap
- LiveCodeBench (Artificial Analysis run): Mistral Large 17.8% against Qwen3 32B 54.6%, 36.8 points apart on a 0 to 100 scale.
- Output price
- Mistral Large $6.00 against Qwen3 32B $0.40 per million output tokens, 15.0x apart.
- Context window
- Mistral Large takes 128K 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 Large | Bars | Qwen3 32B | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 5 | ||
| General | 0 | 13 | ||
| All shared boards | 0 | 30 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | Mistral Large | Qwen3 32B | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 40.0% | Only Qwen3 32B measured |
| LiveCodeBench (Vals AI) | 37.1% | Not measured | Only Mistral Large measured |
| LiveCodeBench (Artificial Analysis run) | 17.8% | 54.6% | Qwen3 32B |
| SciCode | Not measured | 36.0% | Only Qwen3 32B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 3.0% | Only Qwen3 32B measured |
| LMArena Text: coding | 1275 | 1357 | Qwen3 32B |
| LMArena Text: software and IT | 1274 | 1366 | Qwen3 32B |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Large | Qwen3 32B | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.8 | 7.3 | Qwen3 32B |
| Epoch Capabilities Index | 122.1 | 138.4 | Qwen3 32B |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mistral Large | Qwen3 32B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 29.8% | Only Qwen3 32B measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Mistral Large | Qwen3 32B | Leader |
|---|---|---|---|
| GPQA Diamond | 47.7% | Not measured | Only Mistral Large measured |
| GPQA Diamond (Artificial Analysis run) | 35.1% | 66.8% | Qwen3 32B |
| Humanity's Last Exam (Artificial Analysis run) | 3.5% | 7.4% | Qwen3 32B |
| GPQA Diamond (Epoch AI run) | Not measured | 65.7% | Only Qwen3 32B measured |
| LMArena Text: hard prompts | 1257 | 1334 | Qwen3 32B |
| LMArena Text: expert | 1208 | 1362 | Qwen3 32B |
| LMArena Text: hard prompts in English | 1268 | 1350 | Qwen3 32B |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Large | Qwen3 32B | Leader |
|---|---|---|---|
| AIME | 9.2% | Not measured | Only Mistral Large measured |
| MATH 500 (Vals AI) | 74.4% | Not measured | Only Mistral Large measured |
| MGSM (Vals AI) | 87.2% | Not measured | Only Mistral Large measured |
| 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 |
| LMArena Text: math | 1261 | 1399 | Qwen3 32B |
| LMArena Text: mathematical work | 1257 | 1420 | Qwen3 32B |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Mistral Large | Qwen3 32B | Leader |
|---|---|---|---|
| MMLU Pro | 69.7% | Not measured | Only Mistral Large measured |
| TaxEval (Vals AI) | 63.8% | Not measured | Only Mistral Large measured |
| MedQA (Vals AI) | 76.2% | Not measured | Only Mistral Large measured |
| MMLU Pro (Artificial Analysis run) | 51.5% | 79.8% | Qwen3 32B |
| MMLU | 68.8% | Not measured | Only Mistral Large measured |
| LMArena Text: science | 1260 | 1372 | Qwen3 32B |
| LMArena Text: medicine and healthcare | 1255 | 1368 | Qwen3 32B |
| LMArena Text: legal and government | 1275 | 1324 | Qwen3 32B |
| LMArena Text: business and finance | 1238 | 1318 | Qwen3 32B |
| Neither model has a result on the other 8 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large | Qwen3 32B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index013
| Benchmark | Mistral Large | Qwen3 32B | Leader |
|---|---|---|---|
| IFBench | Not measured | 36.3% | Only Qwen3 32B measured |
| Long context reasoning (AA-LCR) | Not measured | 0.0% | Only Qwen3 32B measured |
| DTBench | 60.8% | 67.5% | Qwen3 32B |
| LMCA | Not measured | 17.3% | Only Qwen3 32B measured |
| Fiction.LiveBench | Not measured | 74.2% | Only Qwen3 32B measured |
| LMArena Text | 1265 | 1340 | Qwen3 32B |
| LMArena Text: instruction following | 1249 | 1305 | Qwen3 32B |
| LMArena Text: creative writing | 1242 | 1297 | Qwen3 32B |
| LMArena Text: English | 1284 | 1359 | Qwen3 32B |
| LMArena Text: non-English | 1235 | 1317 | Qwen3 32B |
| LMArena Text: Chinese | 1239 | 1357 | Qwen3 32B |
| LMArena Text: German | 1242 | 1341 | Qwen3 32B |
| LMArena Text: Japanese | 1170 | Not measured | Only Mistral Large measured |
| LMArena Text: Korean | 1202 | Not measured | Only Mistral Large measured |
| LMArena Text: French | 1325 | Not measured | Only Mistral Large measured |
| LMArena Text: Spanish | 1268 | Not measured | Only Mistral Large measured |
| LMArena Text: Russian | 1252 | 1311 | Qwen3 32B |
| LMArena Text: Polish | 1157 | Not measured | Only Mistral Large measured |
| LMArena Text: multi-turn | 1260 | 1331 | Qwen3 32B |
| LMArena Text: longer queries | 1261 | 1327 | Qwen3 32B |
| LMArena Text: writing and language | 1261 | 1298 | Qwen3 32B |
| LMArena Text: entertainment and media | 1230 | 1279 | Qwen3 32B |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large | 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 Large | Qwen3 32B |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | February 26, 2024 | April 28, 2025 |
| Context window | 128K | 131K |
| Input $/Mtok | $2.00 | $0.14 |
| Output $/Mtok | $6.00 | $0.40 |
| 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 Mistral Large better than Qwen3 32B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Mistral Large has the edge where what matters is LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
Which is cheaper, Mistral Large or Qwen3 32B?
Mistral Large costs $2.00 per million input tokens and $6.00 per million output tokens; Qwen3 32B costs $0.14 and $0.40. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Large or Qwen3 32B?
Qwen3 32B, with 131K tokens against 128K.
How many benchmarks is the Mistral Large vs Qwen3 32B comparison based on?
30 benchmarks have a result for both models as of October 21, 2026. Mistral Large's coding index rests on 0 independent benchmarks and Qwen3 32B's on 0. Where only one model has a result, the page says so instead of naming a winner.