Mistral Large 4 vs Qwen3.8 Max (0902)
Updated October 21, 2026. 75 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Qwen3.8 Max (0902) for coding: it scores 63.5 on the Vibe Coding Index against 55.3 for Mistral Large 4.
Pick Mistral Large 4 when output tokens cost 1.4x less, or when the context window is larger, 1.05M against 1M tokens.
- Shared results
- 75
- Mistral Large 4 only
- 6
- Qwen3.8 Max (0902) only
- 38
- Like-for-like categories
- 8 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, four public coding benchmarks
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) scores 63.5 against 55.3.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 45.4 against 38.4.
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
No call
Only Qwen3.8 Max (0902) has a GPQA Diamond result, 93.7%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.8 Max (0902) has a MMLU Pro result, 88.6%.
Directional only
Web design
Design Arena: websites
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 1275 against 1258.
Same board, both measured
Long documents
Listed context window
Mistral Large 4
Mistral Large 4 takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Large 4
Mistral Large 4 $0.0034, 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 4 55.3 from 2 benchmarks, Qwen3.8 Max (0902) 63.5 from 2 benchmarks.
- Widest category
- Agentic work, 17.4 points apart on average over 7 shared benchmarks.
- Widest single gap
- ProofBench: Mistral Large 4 10.0% against Qwen3.8 Max (0902) 58.0%, 48.0 points apart on a 0 to 100 scale.
- Output price
- Mistral Large 4 $4.18 against Qwen3.8 Max (0902) $6.00 per million output tokens, 1.4x apart.
- Context window
- Mistral Large 4 takes 1.05M 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 4 | Bars | Qwen3.8 Max (0902) | Ties |
|---|---|---|---|---|
| Coding | 4 | 9 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 1 | 6 | ||
| Reasoning | 0 | 10 | ||
| Math | 0 | 6 | ||
| Knowledge | 1 | 5 | ||
| Web design | 1 | 7 | ||
| General | 2 | 21 | ||
| All shared boards | 9 | 66 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index49
| Benchmark | Mistral Large 4 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| LiveBench coding | 60.0% | 60.3% | Qwen3.8 Max (0902) |
| LMArena WebDev | 1543 | 1674 | Qwen3.8 Max (0902) |
| AA Coding Index | Not measured | 76.2 | Only Qwen3.8 Max (0902) measured |
| LiveCodeBench (Vals AI) | Not measured | 87.8% | Only Qwen3.8 Max (0902) measured |
| SWE-bench Verified (Vals AI) | Not measured | 85.6% | Only Qwen3.8 Max (0902) measured |
| Terminal-Bench 4 (Vals AI) | 27.8% | 34.3% | Qwen3.8 Max (0902) |
| Vibe Code Bench (Vals AI) | 78.4% | 64.7% | Mistral Large 4 |
| Code migration (Vals AI) | 30.6% | 24.0% | Mistral Large 4 |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Qwen3.8 Max (0902) measured |
| IOI (Vals AI) | 45.3% | 68.9% | Qwen3.8 Max (0902) |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 67.4% | Only Qwen3.8 Max (0902) measured |
| SWE-bench Multimodal (Vals AI) | 33.1% | 42.5% | Qwen3.8 Max (0902) |
| Vibe Code Bench 1-100 (Vals AI) | 14.3% | 12.8% | Mistral Large 4 |
| 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 |
| SciCode | 54.2% | 53.2% | Mistral Large 4 |
| FrontierCode | 39.4% | Not measured | Only Mistral Large 4 measured |
| FrontierSWE | Not measured | 17.8% | Only Qwen3.8 Max (0902) measured |
| ALE-Bench | 1213.2 | Not measured | Only Mistral Large 4 measured |
| LMArena Text: coding | 1478 | 1502 | Qwen3.8 Max (0902) |
| LMArena Text: software and IT | 1467 | 1498 | Qwen3.8 Max (0902) |
| LMArena WebDev: React | 1559 | 1692 | Qwen3.8 Max (0902) |
| LMArena WebDev: HTML | 1487 | 1649 | Qwen3.8 Max (0902) |
| LMArena WebDev: from an image | Not measured | 1637 | Only Qwen3.8 Max (0902) measured |
| Neither model has a result on the other 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Large 4 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| AA Intelligence Index | 38.4 | 45.4 | Qwen3.8 Max (0902) |
| Epoch Capabilities Index | 150.0 | 157.4 | Qwen3.8 Max (0902) |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index16
| Benchmark | Mistral Large 4 | 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 | 1301 | 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 | 1223 | Only Qwen3.8 Max (0902) measured |
| Design Arena: agents building Android apps | Not measured | 1207 | 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 | 1257 | Only Qwen3.8 Max (0902) measured |
| Design Arena: spreadsheets | Not measured | 868 | Only Qwen3.8 Max (0902) measured |
| LiveBench research | 28.1% | 69.9% | Qwen3.8 Max (0902) |
| LiveBench browser | 8.0% | 32.0% | Qwen3.8 Max (0902) |
| LiveBench long agentic | 11.8% | 42.3% | Qwen3.8 Max (0902) |
| Finance Agent (Vals AI) | 54.7% | 50.6% | Mistral Large 4 |
| Tax Agent Bench (Vals AI) | 30.4% | 32.1% | Qwen3.8 Max (0902) |
| Legal Research Bench (Vals AI) | 31.7% | 47.6% | Qwen3.8 Max (0902) |
| Harvey's Legal Agent Benchmark (Vals AI) | 17.5% | Not measured | Only Mistral Large 4 measured |
| Excel Modeling Benchmark (Vals AI) | 56.2% | 60.1% | Qwen3.8 Max (0902) |
| BioMysteryBench (Vals AI) | 67.0% | Not measured | Only Mistral Large 4 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 19 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index010
| Benchmark | Mistral Large 4 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 93.7% | Only Qwen3.8 Max (0902) measured |
| LiveBench reasoning | 83.9% | 88.2% | Qwen3.8 Max (0902) |
| MysteryMechanism (Vals AI) | 12.2% | 23.9% | Qwen3.8 Max (0902) |
| MMMU Pro (Vals AI) | Not measured | 88.0% | Only Qwen3.8 Max (0902) measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 92.8% | Only Qwen3.8 Max (0902) measured |
| Humanity's Last Exam (Artificial Analysis run) | 35.0% | 43.1% | Qwen3.8 Max (0902) |
| GPQA Diamond (Epoch AI run) | 88.1% | 92.7% | Qwen3.8 Max (0902) |
| Chess puzzles | 33.0% | 46.0% | Qwen3.8 Max (0902) |
| Mystery game puzzles | 17.0% | 38.0% | Qwen3.8 Max (0902) |
| LMArena Text: hard prompts | 1444 | 1496 | Qwen3.8 Max (0902) |
| LMArena Text: expert | 1444 | 1507 | Qwen3.8 Max (0902) |
| LMArena Text: hard prompts in English | 1442 | 1503 | Qwen3.8 Max (0902) |
| LMArena Vision: diagrams | 1288 | 1323 | Qwen3.8 Max (0902) |
| LMArena Vision: homework | Not measured | 1336 | Only Qwen3.8 Max (0902) measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Mistral Large 4 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.3% | 100.0% | Qwen3.8 Max (0902) |
| FrontierMath, tiers 1 to 3 | 58.6% | 74.7% | Qwen3.8 Max (0902) |
| FrontierMath, tier 4 | 26.8% | 46.3% | Qwen3.8 Max (0902) |
| ProofBench | 10.0% | 58.0% | Qwen3.8 Max (0902) |
| LMArena Text: math | 1469 | 1501 | Qwen3.8 Max (0902) |
| LMArena Text: mathematical work | 1462 | 1491 | Qwen3.8 Max (0902) |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | Mistral Large 4 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 88.6% | Only Qwen3.8 Max (0902) measured |
| 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) | Not measured | 75.5% | Only Qwen3.8 Max (0902) measured |
| MortgageTax (Vals AI) | Not measured | 64.0% | Only Qwen3.8 Max (0902) measured |
| MedCode (Vals AI) | 40.7% | 40.7% | Mistral Large 4 |
| SimpleQA Verified | 20.0% | 47.3% | Qwen3.8 Max (0902) |
| LMArena Text: science | 1448 | 1512 | Qwen3.8 Max (0902) |
| LMArena Text: medicine and healthcare | 1433 | 1503 | Qwen3.8 Max (0902) |
| LMArena Text: legal and government | 1425 | 1492 | Qwen3.8 Max (0902) |
| LMArena Text: business and finance | 1439 | 1480 | Qwen3.8 Max (0902) |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | Mistral Large 4 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| Design Arena: websites | 1258 | 1275 | Qwen3.8 Max (0902) |
| Design Arena: UI components | 1275 | 1340 | Qwen3.8 Max (0902) |
| Design Arena: game dev | 1226 | 1313 | Qwen3.8 Max (0902) |
| Design Arena: data viz | 1274 | 1281 | Qwen3.8 Max (0902) |
| Design Arena: 3D | 1253 | 1337 | Qwen3.8 Max (0902) |
| Design Arena: all code categories | 1259 | 1293 | Qwen3.8 Max (0902) |
| Design Arena: SVG | 1232 | Not measured | Only Mistral Large 4 measured |
| Design Arena: ASCII art | 1203 | 1201 | Mistral Large 4 |
| LiveBench design | 65.6% | 78.6% | Qwen3.8 Max (0902) |
GeneralLike-for-likePublic boards, shown as published, kept out of the index221
| Benchmark | Mistral Large 4 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| LiveBench data analysis | 76.5% | 78.4% | Qwen3.8 Max (0902) |
| 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) | 80.4% | 85.0% | Qwen3.8 Max (0902) |
| Vals Multimodal Index | Not measured | 65.4% | Only Qwen3.8 Max (0902) measured |
| Long context reasoning (AA-LCR) | 81.3% | 80.3% | Mistral Large 4 |
| DTBench | 88.2% | 92.0% | Qwen3.8 Max (0902) |
| LMCA | 35.8% | 46.2% | Qwen3.8 Max (0902) |
| Surface Evolver Bench | 28.1% | Not measured | Only Mistral Large 4 measured |
| Furniture assembly | 20.8% | 20.0% | Mistral Large 4 |
| LMArena Text | 1431 | 1483 | Qwen3.8 Max (0902) |
| LMArena Text: instruction following | 1422 | 1481 | Qwen3.8 Max (0902) |
| LMArena Text: creative writing | 1363 | 1477 | Qwen3.8 Max (0902) |
| LMArena Vision | 1272 | 1313 | Qwen3.8 Max (0902) |
| LMArena Text: English | 1433 | 1491 | Qwen3.8 Max (0902) |
| LMArena Text: non-English | 1422 | 1473 | Qwen3.8 Max (0902) |
| LMArena Text: Chinese | 1492 | 1539 | Qwen3.8 Max (0902) |
| LMArena Text: German | Not measured | 1478 | Only Qwen3.8 Max (0902) measured |
| LMArena Text: Japanese | 1415 | 1473 | Qwen3.8 Max (0902) |
| LMArena Text: Korean | Not measured | 1460 | Only Qwen3.8 Max (0902) measured |
| LMArena Text: French | 1492 | 1498 | Qwen3.8 Max (0902) |
| LMArena Text: Spanish | 1455 | 1489 | Qwen3.8 Max (0902) |
| LMArena Text: Russian | 1417 | 1482 | Qwen3.8 Max (0902) |
| LMArena Text: Polish | Not measured | 1471 | Only Qwen3.8 Max (0902) measured |
| LMArena Text: multi-turn | 1435 | 1489 | Qwen3.8 Max (0902) |
| LMArena Text: longer queries | 1428 | 1488 | Qwen3.8 Max (0902) |
| LMArena Text: writing and language | 1386 | 1477 | Qwen3.8 Max (0902) |
| LMArena Text: entertainment and media | 1378 | 1464 | Qwen3.8 Max (0902) |
| LMArena Vision: English | 1283 | 1311 | Qwen3.8 Max (0902) |
| LMArena Vision: Chinese | Not measured | 1358 | Only Qwen3.8 Max (0902) measured |
| LMArena Vision: reading text | 1284 | 1322 | Qwen3.8 Max (0902) |
| 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 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large 4 | 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 4 costs less
Repository review
50K input + 3K output tokens
Mistral Large 4 costs less
Agent loop
200K input + 10K output tokens
Mistral Large 4 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 4 | Qwen3.8 Max (0902) |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | October 6, 2026 | September 3, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $1.36 | $2.00 |
| Output $/Mtok | $4.18 | $6.00 |
| Coding index | 55.3 | 63.5 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | Not computable | $1.32 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Large 4 better than Qwen3.8 Max (0902) for coding?
Pick Qwen3.8 Max (0902) for coding: it scores 63.5 on the Vibe Coding Index against 55.3 for Mistral Large 4. Pick Mistral Large 4 when output tokens cost 1.4x less, or when the context window is larger, 1.05M against 1M tokens.
Which is cheaper, Mistral Large 4 or Qwen3.8 Max (0902)?
Mistral Large 4 costs $1.36 per million input tokens and $4.18 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 4 or Qwen3.8 Max (0902)?
Mistral Large 4, with 1.05M tokens against 1M.
How many benchmarks is the Mistral Large 4 vs Qwen3.8 Max (0902) comparison based on?
75 benchmarks have a result for both models as of October 21, 2026. Mistral Large 4's coding index rests on 2 independent benchmarks and Qwen3.8 Max (0902)'s on 2. Where only one model has a result, the page says so instead of naming a winner.