Mistral Large vs Qwen2.5 Coder 32B Instruct
Updated October 21, 2026. 29 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 the context window is larger, 128K against 33K tokens, and where what matters is LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
- Shared results
- 29
- Mistral Large only
- 15
- Qwen2.5 Coder 32B Instruct only
- 6
- 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
Qwen2.5 Coder 32B Instruct
Qwen2.5 Coder 32B Instruct leads, 6.7 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
Mistral Large
Mistral Large takes 128K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 Coder 32B Instruct
Mistral Large $0.0050, Qwen2.5 Coder 32B Instruct $0.0012 per turn.
List prices
At a glance
- Coding index
- Mistral Large not benchmarked, Qwen2.5 Coder 32B Instruct not benchmarked.
- Widest category
- Knowledge, 7.6 points apart on average over 6 shared benchmarks.
- Widest single gap
- LMArena Text: entertainment and media: Mistral Large 1230 against Qwen2.5 Coder 32B Instruct 1150, 13.4 points apart on a 0 to 100 scale.
- Output price
- Mistral Large $6.00 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 6.0x apart.
- Context window
- Mistral Large takes 128K tokens, Qwen2.5 Coder 32B Instruct 33K.
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 | Qwen2.5 Coder 32B Instruct | Ties |
|---|---|---|---|---|
| Coding | 1 | 2 | ||
| Intelligence | 1 | 1 | ||
| Reasoning | 1 | 3 | 1 tie | |
| Math | 2 | 0 | ||
| Knowledge | 4 | 2 | ||
| General | 11 | 0 | ||
| All shared boards | 20 | 8 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index12
| Benchmark | Mistral Large | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | Not measured | 16.4% | Only Qwen2.5 Coder 32B Instruct measured |
| SWE-bench Verified | Not measured | 47.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LiveCodeBench (Vals AI) | 37.1% | Not measured | Only Mistral Large measured |
| LiveCodeBench (Artificial Analysis run) | 17.8% | 29.5% | Qwen2.5 Coder 32B Instruct |
| LMArena Text: coding | 1275 | 1276 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: software and IT | 1274 | 1261 | Mistral Large |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Mistral Large | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.8 | 6.7 | Qwen2.5 Coder 32B Instruct |
| Epoch Capabilities Index | 122.1 | 119.5 | Mistral Large |
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index13
| Benchmark | Mistral Large | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 47.7% | Not measured | Only Mistral Large measured |
| GPQA Diamond (Artificial Analysis run) | 35.1% | 41.7% | Qwen2.5 Coder 32B Instruct |
| Humanity's Last Exam (Artificial Analysis run) | 3.5% | 3.5% | Tie |
| LMArena Text: hard prompts | 1257 | 1251 | Mistral Large |
| LMArena Text: expert | 1208 | 1221 | Qwen2.5 Coder 32B Instruct |
| LMArena Text: hard prompts in English | 1268 | 1269 | Qwen2.5 Coder 32B Instruct |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Mistral Large | Qwen2.5 Coder 32B Instruct | 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 |
| GSM8K | Not measured | 93.0% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: math | 1261 | 1251 | Mistral Large |
| LMArena Text: mathematical work | 1257 | 1243 | Mistral Large |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index42
| Benchmark | Mistral Large | Qwen2.5 Coder 32B Instruct | 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% | 63.5% | Qwen2.5 Coder 32B Instruct |
| MMLU | 68.8% | 79.1% | Qwen2.5 Coder 32B Instruct |
| AI2 Reasoning Challenge | Not measured | 70.5% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text: science | 1260 | 1221 | Mistral Large |
| LMArena Text: medicine and healthcare | 1255 | 1187 | Mistral Large |
| LMArena Text: legal and government | 1275 | 1257 | Mistral Large |
| LMArena Text: business and finance | 1238 | 1224 | Mistral Large |
| Neither model has a result on the other 7 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index110
| Benchmark | Mistral Large | Qwen2.5 Coder 32B Instruct | Leader |
|---|---|---|---|
| DTBench | 60.8% | Not measured | Only Mistral Large measured |
| HellaSwag | Not measured | 83.0% | Only Qwen2.5 Coder 32B Instruct measured |
| WinoGrande | Not measured | 80.8% | Only Qwen2.5 Coder 32B Instruct measured |
| LMArena Text | 1265 | 1230 | Mistral Large |
| LMArena Text: instruction following | 1249 | 1223 | Mistral Large |
| LMArena Text: creative writing | 1242 | 1174 | Mistral Large |
| LMArena Text: English | 1284 | 1248 | Mistral Large |
| LMArena Text: non-English | 1235 | 1205 | Mistral Large |
| LMArena Text: Chinese | 1239 | 1222 | Mistral Large |
| LMArena Text: German | 1242 | Not measured | Only Mistral Large measured |
| 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 | 1228 | Mistral Large |
| LMArena Text: Polish | 1157 | Not measured | Only Mistral Large measured |
| LMArena Text: multi-turn | 1260 | 1222 | Mistral Large |
| LMArena Text: longer queries | 1261 | 1251 | Mistral Large |
| LMArena Text: writing and language | 1261 | 1191 | Mistral Large |
| LMArena Text: entertainment and media | 1230 | 1150 | Mistral Large |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large | Qwen2.5 Coder 32B Instruct | 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
Qwen2.5 Coder 32B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 Coder 32B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 Coder 32B Instruct 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 | Qwen2.5 Coder 32B Instruct |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | February 26, 2024 | November 11, 2024 |
| Context window | 128K | 33K |
| Input $/Mtok | $2.00 | $0.66 |
| Output $/Mtok | $6.00 | $1.00 |
| 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 Qwen2.5 Coder 32B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Mistral Large has the edge where the context window is larger, 128K against 33K tokens, and where what matters is LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
Which is cheaper, Mistral Large or Qwen2.5 Coder 32B Instruct?
Mistral Large costs $2.00 per million input tokens and $6.00 per million output tokens; Qwen2.5 Coder 32B Instruct costs $0.66 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Large or Qwen2.5 Coder 32B Instruct?
Mistral Large, with 128K tokens against 33K.
How many benchmarks is the Mistral Large vs Qwen2.5 Coder 32B Instruct comparison based on?
29 benchmarks have a result for both models as of October 21, 2026. Mistral Large's coding index rests on 0 independent benchmarks and Qwen2.5 Coder 32B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.