Mistral Large vs Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads Mistral Large on the Artificial Analysis Intelligence Index, 45.4 to 5.8, at about the same output price ($6.00 vs $6.00 per million). Updated October 22, 2026; 37 benchmarks have a result for both.
vs boards won of 37 shared
Mistral Large or Qwen3.8 Max (0902): which to pick
Only Qwen3.8 Max (0902) has a coding score, 62.4 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.
Model AMistral LargeMistral AINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.8 Max (0902)Qwen62.4/1005 independent benchmarks$1.32 per solved task
- Shared results
- 37
- Mistral Large only
- 7
- Qwen3.8 Max (0902) only
- 76
- Like-for-like categories
- 6 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
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 Qwen3.8 Max (0902) has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 45.4 against 5.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.8 Max (0902) has a AA Agentic Index result, 56.0.
Directional only
Reasoning
GPQA Diamond
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 93.7% against 47.7%.
Same board, both measured
Math
AIME
No call
Only Mistral Large has a AIME result, 9.2%.
Directional only
Knowledge
MMLU Pro
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 88.6% against 69.7%.
Same board, both measured
Web design
Design Arena: websites
No call
Only Qwen3.8 Max (0902) has a Design Arena: websites result, 1275.
Directional only
Long documents
Listed context window
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) takes 1M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
No call
Mistral Large $0.0050, Qwen3.8 Max (0902) $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.8 Max (0902) has the number, $1.32.
Directional only
At a glance
- Coding index
- Mistral Large not benchmarked, Qwen3.8 Max (0902) 62.4 from 5 benchmarks.
- Widest category
- Reasoning, 45.4 points apart on average over 6 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): Mistral Large 35.1% against Qwen3.8 Max (0902) 92.8%, 57.7 points apart on a 0 to 100 scale.
- Output price
- Both cost $6.00 per million output tokens.
- Context window
- Mistral Large takes 128K tokens, Qwen3.8 Max (0902) 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 | Qwen3.8 Max (0902) | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 6 | ||
| General | 0 | 18 | ||
| All shared boards | 0 | 37 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | Mistral Large | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 60.3% | Only Qwen3.8 Max (0902) measured |
| LMArena WebDev | Not measured | 1674 | Only Qwen3.8 Max (0902) measured |
| AA Coding Index | Not measured | 76.2 | Only Qwen3.8 Max (0902) measured |
| LiveCodeBench (Vals AI) | 37.1% | 87.8% | Qwen3.8 Max (0902) |
| SWE-bench Verified (Vals AI) | Not measured | 85.6% | Only Qwen3.8 Max (0902) measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 34.3% | Only Qwen3.8 Max (0902) measured |
| Vibe Code Bench (Vals AI) | Not measured | 64.7% | Only Qwen3.8 Max (0902) measured |
| Code migration (Vals AI) | Not measured | 24.0% | Only Qwen3.8 Max (0902) measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Qwen3.8 Max (0902) measured |
| IOI (Vals AI) | Not measured | 68.9% | Only Qwen3.8 Max (0902) measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 67.4% | Only Qwen3.8 Max (0902) measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 42.5% | Only Qwen3.8 Max (0902) measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 12.8% | Only Qwen3.8 Max (0902) measured |
| SkillsBench (Vals AI) | Not measured | 42.0% | Only Qwen3.8 Max (0902) measured |
| CyberBench (Vals AI) | Not measured | 28.6% | Only Qwen3.8 Max (0902) measured |
| LiveCodeBench (Artificial Analysis run) | 17.8% | Not measured | Only Mistral Large measured |
| SciCode | Not measured | 53.2% | Only Qwen3.8 Max (0902) measured |
| FrontierSWE | Not measured | 17.8% | Only Qwen3.8 Max (0902) measured |
| LMArena Text: coding | 1275 | 1502 | Qwen3.8 Max (0902) |
| LMArena Text: software and IT | 1274 | 1498 | Qwen3.8 Max (0902) |
| LMArena WebDev: React | Not measured | 1692 | Only Qwen3.8 Max (0902) measured |
| LMArena WebDev: HTML | Not measured | 1649 | Only Qwen3.8 Max (0902) measured |
| LMArena WebDev: from an image | Not measured | 1637 | Only Qwen3.8 Max (0902) measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Large | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.8 | 45.4 | Qwen3.8 Max (0902) |
| Epoch Capabilities Index | 122.1 | 157.4 | Qwen3.8 Max (0902) |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index18 boards, none shared
| Benchmark | Mistral Large | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 56.0 | Only Qwen3.8 Max (0902) measured |
| Design Arena: agents building web apps | Not measured | 1300 | Only Qwen3.8 Max (0902) measured |
| Design Arena: agents building full-stack apps | Not measured | 1305 | Only Qwen3.8 Max (0902) measured |
| Design Arena: agents building mobile apps | Not measured | 1224 | Only Qwen3.8 Max (0902) measured |
| Design Arena: agents building Android apps | Not measured | 1196 | Only Qwen3.8 Max (0902) measured |
| Design Arena: agents building Godot games | Not measured | 1234 | Only Qwen3.8 Max (0902) measured |
| Design Arena: PowerPoint slides | Not measured | 1233 | Only Qwen3.8 Max (0902) measured |
| Design Arena: spreadsheets | Not measured | 967 | Only Qwen3.8 Max (0902) measured |
| LiveBench research | Not measured | 69.9% | Only Qwen3.8 Max (0902) measured |
| LiveBench browser | Not measured | 32.0% | Only Qwen3.8 Max (0902) measured |
| LiveBench long agentic | Not measured | 42.3% | Only Qwen3.8 Max (0902) measured |
| Finance Agent (Vals AI) | Not measured | 50.6% | Only Qwen3.8 Max (0902) measured |
| Tax Agent Bench (Vals AI) | Not measured | 32.1% | Only Qwen3.8 Max (0902) measured |
| Legal Research Bench (Vals AI) | Not measured | 47.6% | Only Qwen3.8 Max (0902) measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 60.1% | Only Qwen3.8 Max (0902) measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 12.9% | Only Qwen3.8 Max (0902) measured |
| APEX-Agents | Not measured | 63.3% | Only Qwen3.8 Max (0902) measured |
| LMArena Search | Not measured | 1261 | Only Qwen3.8 Max (0902) measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Mistral Large | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| GPQA Diamond | 47.7% | 93.7% | Qwen3.8 Max (0902) |
| LiveBench reasoning | Not measured | 88.2% | Only Qwen3.8 Max (0902) measured |
| MysteryMechanism (Vals AI) | Not measured | 23.9% | Only Qwen3.8 Max (0902) measured |
| MMMU Pro (Vals AI) | Not measured | 88.0% | Only Qwen3.8 Max (0902) measured |
| GPQA Diamond (Artificial Analysis run) | 35.1% | 92.8% | Qwen3.8 Max (0902) |
| Humanity's Last Exam (Artificial Analysis run) | 3.5% | 43.1% | Qwen3.8 Max (0902) |
| GPQA Diamond (Epoch AI run) | Not measured | 92.7% | Only Qwen3.8 Max (0902) measured |
| Chess puzzles | Not measured | 46.0% | Only Qwen3.8 Max (0902) measured |
| Mystery game puzzles | Not measured | 38.0% | Only Qwen3.8 Max (0902) measured |
| LMArena Text: hard prompts | 1257 | 1496 | Qwen3.8 Max (0902) |
| LMArena Text: expert | 1208 | 1507 | Qwen3.8 Max (0902) |
| LMArena Text: hard prompts in English | 1268 | 1503 | Qwen3.8 Max (0902) |
| LMArena Vision: diagrams | Not measured | 1323 | Only Qwen3.8 Max (0902) measured |
| LMArena Vision: homework | Not measured | 1336 | Only Qwen3.8 Max (0902) measured |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Large | Qwen3.8 Max (0902) | 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 | 100.0% | Only Qwen3.8 Max (0902) measured |
| FrontierMath, tiers 1 to 3 | Not measured | 74.7% | Only Qwen3.8 Max (0902) measured |
| FrontierMath, tier 4 | Not measured | 46.3% | Only Qwen3.8 Max (0902) measured |
| ProofBench | Not measured | 58.0% | Only Qwen3.8 Max (0902) measured |
| LMArena Text: math | 1261 | 1501 | Qwen3.8 Max (0902) |
| LMArena Text: mathematical work | 1257 | 1491 | Qwen3.8 Max (0902) |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Mistral Large | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| MMLU Pro | 69.7% | 88.6% | Qwen3.8 Max (0902) |
| LegalBench (Vals AI) | Not measured | 83.6% | Only Qwen3.8 Max (0902) measured |
| CorpFin (Vals AI) | Not measured | 65.8% | Only Qwen3.8 Max (0902) measured |
| TaxEval (Vals AI) | 63.8% | 75.5% | Qwen3.8 Max (0902) |
| MortgageTax (Vals AI) | Not measured | 64.0% | Only Qwen3.8 Max (0902) measured |
| MedQA (Vals AI) | 76.2% | Not measured | Only Mistral Large measured |
| MedCode (Vals AI) | Not measured | 40.7% | Only Qwen3.8 Max (0902) measured |
| MMLU Pro (Artificial Analysis run) | 51.5% | Not measured | Only Mistral Large measured |
| SimpleQA Verified | Not measured | 47.3% | Only Qwen3.8 Max (0902) measured |
| MMLU | 68.8% | Not measured | Only Mistral Large measured |
| LMArena Text: science | 1260 | 1512 | Qwen3.8 Max (0902) |
| LMArena Text: medicine and healthcare | 1255 | 1503 | Qwen3.8 Max (0902) |
| LMArena Text: legal and government | 1275 | 1492 | Qwen3.8 Max (0902) |
| LMArena Text: business and finance | 1238 | 1480 | Qwen3.8 Max (0902) |
| Neither model has a result on the other 4 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Mistral Large | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1275 | Only Qwen3.8 Max (0902) measured |
| Design Arena: UI components | Not measured | 1338 | Only Qwen3.8 Max (0902) measured |
| Design Arena: game dev | Not measured | 1312 | Only Qwen3.8 Max (0902) measured |
| Design Arena: data viz | Not measured | 1281 | Only Qwen3.8 Max (0902) measured |
| Design Arena: 3D | Not measured | 1336 | Only Qwen3.8 Max (0902) measured |
| Design Arena: all code categories | Not measured | 1293 | Only Qwen3.8 Max (0902) measured |
| Design Arena: ASCII art | Not measured | 1200 | Only Qwen3.8 Max (0902) measured |
| LiveBench design | Not measured | 78.6% | Only Qwen3.8 Max (0902) 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 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 78.4% | Only Qwen3.8 Max (0902) measured |
| Public Benefits Bench (Vals AI) | Not measured | 67.1% | Only Qwen3.8 Max (0902) measured |
| SAGE (Vals AI) | Not measured | 51.2% | Only Qwen3.8 Max (0902) measured |
| MedScribe (Vals AI) | Not measured | 85.0% | Only Qwen3.8 Max (0902) measured |
| Vals Multimodal Index | Not measured | 65.4% | Only Qwen3.8 Max (0902) measured |
| Long context reasoning (AA-LCR) | Not measured | 80.3% | Only Qwen3.8 Max (0902) measured |
| DTBench | 60.8% | 92.0% | Qwen3.8 Max (0902) |
| LMCA | Not measured | 46.2% | Only Qwen3.8 Max (0902) measured |
| Furniture assembly | Not measured | 20.0% | Only Qwen3.8 Max (0902) measured |
| LMArena Text | 1265 | 1483 | Qwen3.8 Max (0902) |
| LMArena Text: instruction following | 1249 | 1481 | Qwen3.8 Max (0902) |
| LMArena Text: creative writing | 1242 | 1477 | Qwen3.8 Max (0902) |
| LMArena Vision | Not measured | 1313 | Only Qwen3.8 Max (0902) measured |
| LMArena Text: English | 1284 | 1491 | Qwen3.8 Max (0902) |
| LMArena Text: non-English | 1235 | 1473 | Qwen3.8 Max (0902) |
| LMArena Text: Chinese | 1239 | 1539 | Qwen3.8 Max (0902) |
| LMArena Text: German | 1242 | 1478 | Qwen3.8 Max (0902) |
| LMArena Text: Japanese | 1170 | 1473 | Qwen3.8 Max (0902) |
| LMArena Text: Korean | 1202 | 1460 | Qwen3.8 Max (0902) |
| LMArena Text: French | 1325 | 1498 | Qwen3.8 Max (0902) |
| LMArena Text: Spanish | 1268 | 1489 | Qwen3.8 Max (0902) |
| LMArena Text: Russian | 1252 | 1482 | Qwen3.8 Max (0902) |
| LMArena Text: Polish | 1157 | 1471 | Qwen3.8 Max (0902) |
| LMArena Text: multi-turn | 1260 | 1489 | Qwen3.8 Max (0902) |
| LMArena Text: longer queries | 1261 | 1488 | Qwen3.8 Max (0902) |
| LMArena Text: writing and language | 1261 | 1477 | Qwen3.8 Max (0902) |
| LMArena Text: entertainment and media | 1230 | 1464 | Qwen3.8 Max (0902) |
| LMArena Vision: English | Not measured | 1311 | Only Qwen3.8 Max (0902) measured |
| LMArena Vision: Chinese | Not measured | 1358 | Only Qwen3.8 Max (0902) measured |
| LMArena Vision: reading text | Not measured | 1322 | Only Qwen3.8 Max (0902) measured |
| LMArena Vision: humor | Not measured | 1304 | Only Qwen3.8 Max (0902) measured |
| LMArena Vision: creative writing | Not measured | 1312 | Only Qwen3.8 Max (0902) measured |
| Neither model has a result on the other 14 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large | Qwen3.8 Max (0902) | 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
Mistral Large$0.0050Fits in one request
Qwen3.8 Max (0902)$0.0050Fits in one request
Same cost
Repository review
50K input + 3K output tokens
Mistral Large$0.118Fits in one request
Qwen3.8 Max (0902)$0.118Fits in one request
Same cost
Agent loop
200K input + 10K output tokens
Mistral Large$0.460Exceeds the context window
Qwen3.8 Max (0902)$0.460Fits in one request
Same cost
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 | Qwen3.8 Max (0902) |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | February 26, 2024 | September 3, 2026 |
| Context window | 128K | 1M |
| Input $/Mtok | $2.00 | $2.00 |
| Output $/Mtok | $6.00 | $6.00 |
| Coding index | Not benchmarked | 62.4 |
| Independent measurements behind it | 0 | 5 |
| Cost per solved task | Not computable | $1.32 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Large better than Qwen3.8 Max (0902) for coding?
Only Qwen3.8 Max (0902) has a coding score, 62.4 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 Qwen3.8 Max (0902)?
Mistral Large costs $2.00 per million input tokens and $6.00 per million output tokens; Qwen3.8 Max (0902) costs $2.00 and $6.00. Cost per solved task is known for Qwen3.8 Max (0902) only.
Which has the bigger context window, Mistral Large or Qwen3.8 Max (0902)?
Qwen3.8 Max (0902), with 1M tokens against 128K.
How many benchmarks is the Mistral Large vs Qwen3.8 Max (0902) comparison based on?
37 benchmarks have a result for both models as of October 22, 2026. Mistral Large's coding index rests on 0 independent benchmarks and Qwen3.8 Max (0902)'s on 5. Where only one model has a result, the page says so instead of naming a winner.