Mistral Large vs Qwen3.6 Plus
Updated October 21, 2026. 38 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Only Qwen3.6 Plus has a coding score, 44.1 on the Vibe Coding Index; Mistral Large has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 38
- Mistral Large only
- 6
- Qwen3.6 Plus only
- 45
- 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
Only Qwen3.6 Plus has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.6 Plus
Qwen3.6 Plus leads, 27.0 against 5.8.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.6 Plus
Qwen3.6 Plus leads, 87.4% against 47.7%.
Same board, both measured
Math
AIME
Qwen3.6 Plus
Qwen3.6 Plus leads, 94.6% against 9.2%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.6 Plus
Qwen3.6 Plus leads, 87.7% against 69.7%.
Same board, both measured
Web design
Design Arena: websites
No call
Only Qwen3.6 Plus has a Design Arena: websites result, 1243.
Directional only
Long documents
Listed context window
Qwen3.6 Plus
Qwen3.6 Plus takes 1M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.6 Plus
Mistral Large $0.0050, Qwen3.6 Plus $0.0013 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.6 Plus has the number, $1.19.
Directional only
At a glance
- Coding index
- Mistral Large not benchmarked, Qwen3.6 Plus 44.1 from 3 benchmarks.
- Widest category
- Math, 49.5 points apart on average over 3 shared benchmarks.
- Widest single gap
- AIME: Mistral Large 9.2% against Qwen3.6 Plus 94.6%, 85.4 points apart on a 0 to 100 scale.
- Output price
- Mistral Large $6.00 against Qwen3.6 Plus $1.95 per million output tokens, 3.1x apart.
- Context window
- Mistral Large takes 128K tokens, Qwen3.6 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 | Qwen3.6 Plus | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 6 | ||
| General | 0 | 18 | ||
| All shared boards | 0 | 38 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | Mistral Large | Qwen3.6 Plus | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1461 | Only Qwen3.6 Plus measured |
| AA Coding Index | Not measured | 54.5 | Only Qwen3.6 Plus measured |
| LiveCodeBench (Vals AI) | 37.1% | 86.0% | Qwen3.6 Plus |
| SWE-bench Verified (Vals AI) | Not measured | 73.4% | Only Qwen3.6 Plus measured |
| Vibe Code Bench (Vals AI) | Not measured | 25.6% | Only Qwen3.6 Plus measured |
| Code migration (Vals AI) | Not measured | 11.1% | Only Qwen3.6 Plus measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Qwen3.6 Plus measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 53.2% | Only Qwen3.6 Plus measured |
| LiveCodeBench (Artificial Analysis run) | 17.8% | Not measured | Only Mistral Large measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 43.9% | Only Qwen3.6 Plus measured |
| ALE-Bench | Not measured | 670.1 | Only Qwen3.6 Plus measured |
| LMArena Text: coding | 1275 | 1467 | Qwen3.6 Plus |
| LMArena Text: software and IT | 1274 | 1462 | Qwen3.6 Plus |
| LMArena WebDev: React | Not measured | 1454 | Only Qwen3.6 Plus measured |
| LMArena WebDev: HTML | Not measured | 1463 | Only Qwen3.6 Plus measured |
| LMArena WebDev: from an image | Not measured | 1472 | Only Qwen3.6 Plus measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Large | Qwen3.6 Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.8 | 27.0 | Qwen3.6 Plus |
| Epoch Capabilities Index | 122.1 | 147.3 | Qwen3.6 Plus |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Mistral Large | Qwen3.6 Plus | Leader |
|---|---|---|---|
| Finance Agent (Vals AI) | Not measured | 40.9% | Only Qwen3.6 Plus measured |
| Tax Agent Bench (Vals AI) | Not measured | 7.7% | Only Qwen3.6 Plus measured |
| Legal Research Bench (Vals AI) | Not measured | 14.9% | Only Qwen3.6 Plus measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 32.9% | Only Qwen3.6 Plus measured |
| τ²-Bench Telecom | Not measured | 97.7% | Only Qwen3.6 Plus measured |
| Vending-Bench 2 | Not measured | $5,115 | Only Qwen3.6 Plus measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Mistral Large | Qwen3.6 Plus | Leader |
|---|---|---|---|
| GPQA Diamond | 47.7% | 87.4% | Qwen3.6 Plus |
| MMMU Pro (Vals AI) | Not measured | 84.2% | Only Qwen3.6 Plus measured |
| GPQA Diamond (Artificial Analysis run) | 35.1% | 88.2% | Qwen3.6 Plus |
| Humanity's Last Exam (Artificial Analysis run) | 3.5% | 27.8% | Qwen3.6 Plus |
| GPQA Diamond (Epoch AI run) | Not measured | 88.4% | Only Qwen3.6 Plus measured |
| Chess puzzles | Not measured | 14.0% | Only Qwen3.6 Plus measured |
| Mystery game puzzles | Not measured | 12.0% | Only Qwen3.6 Plus measured |
| LMArena Text: hard prompts | 1257 | 1448 | Qwen3.6 Plus |
| LMArena Text: expert | 1208 | 1454 | Qwen3.6 Plus |
| LMArena Text: hard prompts in English | 1268 | 1455 | Qwen3.6 Plus |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Mistral Large | Qwen3.6 Plus | Leader |
|---|---|---|---|
| AIME | 9.2% | 94.6% | Qwen3.6 Plus |
| 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 | 93.3% | Only Qwen3.6 Plus measured |
| FrontierMath, tiers 1 to 3 | Not measured | 38.2% | Only Qwen3.6 Plus measured |
| LMArena Text: math | 1261 | 1449 | Qwen3.6 Plus |
| LMArena Text: mathematical work | 1257 | 1448 | Qwen3.6 Plus |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Mistral Large | Qwen3.6 Plus | Leader |
|---|---|---|---|
| MMLU Pro | 69.7% | 87.7% | Qwen3.6 Plus |
| LegalBench (Vals AI) | Not measured | 84.2% | Only Qwen3.6 Plus measured |
| CaseLaw (Vals AI) | Not measured | 51.5% | Only Qwen3.6 Plus measured |
| CorpFin (Vals AI) | Not measured | 61.9% | Only Qwen3.6 Plus measured |
| TaxEval (Vals AI) | 63.8% | 74.7% | Qwen3.6 Plus |
| MortgageTax (Vals AI) | Not measured | 68.0% | Only Qwen3.6 Plus measured |
| MedQA (Vals AI) | 76.2% | Not measured | Only Mistral Large measured |
| MedCode (Vals AI) | Not measured | 36.9% | Only Qwen3.6 Plus measured |
| MMLU Pro (Artificial Analysis run) | 51.5% | Not measured | Only Mistral Large measured |
| SimpleQA Verified | Not measured | 44.1% | Only Qwen3.6 Plus measured |
| MMLU | 68.8% | Not measured | Only Mistral Large measured |
| LMArena Text: science | 1260 | 1439 | Qwen3.6 Plus |
| LMArena Text: medicine and healthcare | 1255 | 1442 | Qwen3.6 Plus |
| LMArena Text: legal and government | 1275 | 1437 | Qwen3.6 Plus |
| LMArena Text: business and finance | 1238 | 1430 | Qwen3.6 Plus |
| Neither model has a result on the other 2 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Mistral Large | Qwen3.6 Plus | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1243 | Only Qwen3.6 Plus measured |
| Design Arena: UI components | Not measured | 1233 | Only Qwen3.6 Plus measured |
| Design Arena: game dev | Not measured | 1211 | Only Qwen3.6 Plus measured |
| Design Arena: data viz | Not measured | 1228 | Only Qwen3.6 Plus measured |
| Design Arena: 3D | Not measured | 1202 | Only Qwen3.6 Plus measured |
| Design Arena: all code categories | Not measured | 1236 | Only Qwen3.6 Plus measured |
| Design Arena: SVG | Not measured | 1161 | Only Qwen3.6 Plus measured |
| Design Arena: ASCII art | Not measured | 1114 | Only Qwen3.6 Plus measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
| Benchmark | Mistral Large | Qwen3.6 Plus | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 44.9% | Only Qwen3.6 Plus measured |
| MedScribe (Vals AI) | Not measured | 77.0% | Only Qwen3.6 Plus measured |
| Vals Multimodal Index | Not measured | 51.5% | Only Qwen3.6 Plus measured |
| IFBench | Not measured | 75.2% | Only Qwen3.6 Plus measured |
| Long context reasoning (AA-LCR) | Not measured | 78.3% | Only Qwen3.6 Plus measured |
| DTBench | 60.8% | 81.9% | Qwen3.6 Plus |
| LMCA | Not measured | 33.1% | Only Qwen3.6 Plus measured |
| CL-bench | Not measured | 20.3% | Only Qwen3.6 Plus measured |
| LMArena Text | 1265 | 1436 | Qwen3.6 Plus |
| LMArena Text: instruction following | 1249 | 1424 | Qwen3.6 Plus |
| LMArena Text: creative writing | 1242 | 1404 | Qwen3.6 Plus |
| LMArena Text: English | 1284 | 1445 | Qwen3.6 Plus |
| LMArena Text: non-English | 1235 | 1424 | Qwen3.6 Plus |
| LMArena Text: Chinese | 1239 | 1477 | Qwen3.6 Plus |
| LMArena Text: German | 1242 | 1451 | Qwen3.6 Plus |
| LMArena Text: Japanese | 1170 | 1390 | Qwen3.6 Plus |
| LMArena Text: Korean | 1202 | 1379 | Qwen3.6 Plus |
| LMArena Text: French | 1325 | 1455 | Qwen3.6 Plus |
| LMArena Text: Spanish | 1268 | 1432 | Qwen3.6 Plus |
| LMArena Text: Russian | 1252 | 1434 | Qwen3.6 Plus |
| LMArena Text: Polish | 1157 | 1433 | Qwen3.6 Plus |
| LMArena Text: multi-turn | 1260 | 1438 | Qwen3.6 Plus |
| LMArena Text: longer queries | 1261 | 1439 | Qwen3.6 Plus |
| LMArena Text: writing and language | 1261 | 1418 | Qwen3.6 Plus |
| LMArena Text: entertainment and media | 1230 | 1402 | Qwen3.6 Plus |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large | Qwen3.6 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
Qwen3.6 Plus costs less
Repository review
50K input + 3K output tokens
Qwen3.6 Plus costs less
Agent loop
200K input + 10K output tokens
Qwen3.6 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 | Qwen3.6 Plus |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | February 26, 2024 | April 2, 2026 |
| Context window | 128K | 1M |
| Input $/Mtok | $2.00 | $0.325 |
| Output $/Mtok | $6.00 | $1.95 |
| Coding index | Not benchmarked | 44.1 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $1.19 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Large better than Qwen3.6 Plus for coding?
Only Qwen3.6 Plus has a coding score, 44.1 on the Vibe Coding Index; Mistral Large has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Mistral Large or Qwen3.6 Plus?
Mistral Large costs $2.00 per million input tokens and $6.00 per million output tokens; Qwen3.6 Plus costs $0.325 and $1.95. Cost per solved task is known for Qwen3.6 Plus only.
Which has the bigger context window, Mistral Large or Qwen3.6 Plus?
Qwen3.6 Plus, with 1M tokens against 128K.
How many benchmarks is the Mistral Large vs Qwen3.6 Plus comparison based on?
38 benchmarks have a result for both models as of October 21, 2026. Mistral Large's coding index rests on 0 independent benchmarks and Qwen3.6 Plus's on 3. Where only one model has a result, the page says so instead of naming a winner.