Mistral Large vs Qwen-Plus
Updated October 21, 2026. 24 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 LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 24
- Mistral Large only
- 20
- Qwen-Plus only
- 4
- Like-for-like categories
- 5 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
No call
Only Mistral Large has a AA Intelligence Index result, 5.8.
Directional only
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
Qwen-Plus
Qwen-Plus takes 1M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen-Plus
Mistral Large $0.0050, Qwen-Plus $0.0006 per turn.
List prices
At a glance
- Coding index
- Mistral Large not benchmarked, Qwen-Plus not benchmarked.
- Widest category
- Knowledge, 13.6 points apart on average over 4 shared benchmarks.
- Widest single gap
- DTBench: Mistral Large 60.8% against Qwen-Plus 81.1%, 20.3 points apart on a 0 to 100 scale.
- Output price
- Mistral Large $6.00 against Qwen-Plus $0.78 per million output tokens, 7.7x apart.
- Context window
- Mistral Large takes 128K tokens, Qwen-Plus 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 Large | Bars | Qwen-Plus | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 13 | ||
| All shared boards | 0 | 24 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Mistral Large | Qwen-Plus | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | 37.1% | Not measured | Only Mistral Large measured |
| LiveCodeBench (Artificial Analysis run) | 17.8% | Not measured | Only Mistral Large measured |
| LMArena Text: coding | 1275 | 1328 | Qwen-Plus |
| LMArena Text: software and IT | 1274 | 1343 | Qwen-Plus |
| Neither model has a result on the other 34 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Mistral Large | Qwen-Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.8 | Not measured | Only Mistral Large measured |
| Epoch Capabilities Index | 122.1 | Not measured | Only Mistral Large measured |
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large | Qwen-Plus | Leader |
|---|---|---|---|
| Neither model has a result on 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Mistral Large | Qwen-Plus | Leader |
|---|---|---|---|
| GPQA Diamond | 47.7% | Not measured | Only Mistral Large measured |
| GPQA Diamond (Artificial Analysis run) | 35.1% | Not measured | Only Mistral Large measured |
| Humanity's Last Exam (Artificial Analysis run) | 3.5% | Not measured | Only Mistral Large measured |
| GPQA Diamond (Epoch AI run) | Not measured | 48.1% | Only Qwen-Plus measured |
| LMArena Text: hard prompts | 1257 | 1317 | Qwen-Plus |
| LMArena Text: expert | 1208 | 1328 | Qwen-Plus |
| LMArena Text: hard prompts in English | 1268 | 1333 | Qwen-Plus |
| 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 | Qwen-Plus | 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 |
| OTIS Mock AIME | Not measured | 17.8% | Only Qwen-Plus measured |
| MATH level 5 | Not measured | 65.3% | Only Qwen-Plus measured |
| LMArena Text: math | 1261 | 1326 | Qwen-Plus |
| LMArena Text: mathematical work | 1257 | 1332 | Qwen-Plus |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Mistral Large | Qwen-Plus | 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% | Not measured | Only Mistral Large measured |
| MMLU | 68.8% | Not measured | Only Mistral Large measured |
| LMArena Text: science | 1260 | 1352 | Qwen-Plus |
| LMArena Text: medicine and healthcare | 1255 | 1350 | Qwen-Plus |
| LMArena Text: legal and government | 1275 | 1349 | Qwen-Plus |
| LMArena Text: business and finance | 1238 | 1303 | Qwen-Plus |
| 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 | Qwen-Plus | 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 | Qwen-Plus | Leader |
|---|---|---|---|
| DTBench | 60.8% | 81.1% | Qwen-Plus |
| LMCA | Not measured | 24.0% | Only Qwen-Plus measured |
| LMArena Text | 1265 | 1326 | Qwen-Plus |
| LMArena Text: instruction following | 1249 | 1303 | Qwen-Plus |
| LMArena Text: creative writing | 1242 | 1292 | Qwen-Plus |
| LMArena Text: English | 1284 | 1339 | Qwen-Plus |
| LMArena Text: non-English | 1235 | 1310 | Qwen-Plus |
| LMArena Text: Chinese | 1239 | 1347 | Qwen-Plus |
| LMArena Text: German | 1242 | Not measured | Only Mistral Large measured |
| LMArena Text: Japanese | 1170 | 1251 | Qwen-Plus |
| 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 | 1323 | Qwen-Plus |
| LMArena Text: Polish | 1157 | Not measured | Only Mistral Large measured |
| LMArena Text: multi-turn | 1260 | 1336 | Qwen-Plus |
| LMArena Text: longer queries | 1261 | 1324 | Qwen-Plus |
| LMArena Text: writing and language | 1261 | 1296 | Qwen-Plus |
| LMArena Text: entertainment and media | 1230 | 1270 | Qwen-Plus |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large | Qwen-Plus | 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
Qwen-Plus costs less
Repository review
50K input + 3K output tokens
Qwen-Plus costs less
Agent loop
200K input + 10K output tokens
Qwen-Plus 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 | Qwen-Plus |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | February 26, 2024 | February 1, 2025 |
| Context window | 128K | 1M |
| Input $/Mtok | $2.00 | $0.26 |
| Output $/Mtok | $6.00 | $0.78 |
| 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 Qwen-Plus 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 LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, Mistral Large or Qwen-Plus?
Mistral Large costs $2.00 per million input tokens and $6.00 per million output tokens; Qwen-Plus costs $0.26 and $0.78. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Large or Qwen-Plus?
Qwen-Plus, with 1M tokens against 128K.
How many benchmarks is the Mistral Large vs Qwen-Plus comparison based on?
24 benchmarks have a result for both models as of October 21, 2026. Mistral Large's coding index rests on 0 independent benchmarks and Qwen-Plus's on 0. Where only one model has a result, the page says so instead of naming a winner.