Mistral Large 4 vs Qwen3.7 Plus
Mistral Large 4 leads Qwen3.7 Plus on coding, 56.4 to 33.2 on the Vibe Coding Index, and Qwen3.7 Plus costs 3.3x less per output token ($1.28 vs $4.18 per million). Updated October 22, 2026; 55 benchmarks have a result for both.
Mistral Large 4 or Qwen3.7 Plus: which to pick
Pick Mistral Large 4 for coding: it scores 56.4 on the Vibe Coding Index against 33.2 for Qwen3.7 Plus.
Pick Qwen3.7 Plus for output tokens 3.3x cheaper ($1.28 vs $4.18 per million) and work measured by LMArena Text: software and IT or Humanity's Last Exam (Artificial Analysis run), where it leads.
- Shared results
- 55
- Mistral Large 4 only
- 26
- Qwen3.7 Plus only
- 20
- Like-for-like categories
- 8 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
Mistral Large 4
Mistral Large 4 scores 56.4 against 33.2.
5 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
Mistral Large 4
Mistral Large 4 leads, 38.4 against 25.2.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.7 Plus has a AA Agentic Index result, 17.5.
Directional only
Web design
Design Arena: websites
Qwen3.7 Plus
Qwen3.7 Plus leads, 1269 against 1257.
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
Qwen3.7 Plus
Mistral Large 4 $0.0034, Qwen3.7 Plus $0.0010 per turn.
List prices
At a glance
- Coding index
- Mistral Large 4 56.4 from 5 benchmarks, Qwen3.7 Plus 33.2 from 2 benchmarks.
- Widest category
- Coding, 17.5 points apart on average over 6 shared benchmarks.
- Widest single gap
- FrontierCode: Mistral Large 4 39.4% against Qwen3.7 Plus 10.2%, 45.0 points apart on a 0 to 100 scale.
- Output price
- Mistral Large 4 $4.18 against Qwen3.7 Plus $1.28 per million output tokens, 3.3x apart.
- Context window
- Mistral Large 4 takes 1.05M tokens, Qwen3.7 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 4 | Bars | Qwen3.7 Plus | Ties |
|---|---|---|---|---|
| Coding | 5 | 1 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 4 | 0 | ||
| Reasoning | 1 | 5 | 1 tie | |
| Math | 3 | 1 | ||
| Knowledge | 1 | 3 | ||
| Web design | 4 | 4 | ||
| General | 5 | 15 | ||
| All shared boards | 25 | 29 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index51
| Benchmark | Mistral Large 4 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| LiveBench coding | 60.0% | Not measured | Only Mistral Large 4 measured |
| LMArena WebDev | 1543 | Not measured | Only Mistral Large 4 measured |
| AA Coding Index | Not measured | 55.9 | Only Qwen3.7 Plus measured |
| Terminal-Bench 4 (Vals AI) | 27.8% | Not measured | Only Mistral Large 4 measured |
| Vibe Code Bench (Vals AI) | 78.4% | 46.4% | Mistral Large 4 |
| Code migration (Vals AI) | 30.6% | 12.9% | Mistral Large 4 |
| IOI (Vals AI) | 45.3% | Not measured | Only Mistral Large 4 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 52.8% | Only Qwen3.7 Plus measured |
| SWE-bench Multimodal (Vals AI) | 33.1% | Not measured | Only Mistral Large 4 measured |
| Vibe Code Bench 1-100 (Vals AI) | 14.3% | Not measured | Only Mistral Large 4 measured |
| SkillsBench (Vals AI) | Not measured | 54.3% | Only Qwen3.7 Plus measured |
| SciCode | 54.2% | 46.1% | Mistral Large 4 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 47.0% | Only Qwen3.7 Plus measured |
| FrontierCode | 39.4% | 10.2% | Mistral Large 4 |
| ALE-Bench | 1213.2 | Not measured | Only Mistral Large 4 measured |
| LMArena Text: coding | 1478 | 1473 | Mistral Large 4 |
| LMArena Text: software and IT | 1467 | 1473 | Qwen3.7 Plus |
| LMArena WebDev: React | 1559 | Not measured | Only Mistral Large 4 measured |
| LMArena WebDev: HTML | 1487 | Not measured | Only Mistral Large 4 measured |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Mistral Large 4 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 38.4 | 25.2 | Mistral Large 4 |
| Epoch Capabilities Index | 150.0 | 147.0 | Mistral Large 4 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Mistral Large 4 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 17.5 | Only Qwen3.7 Plus measured |
| LiveBench research | 28.1% | Not measured | Only Mistral Large 4 measured |
| LiveBench browser | 8.0% | Not measured | Only Mistral Large 4 measured |
| LiveBench long agentic | 11.8% | Not measured | Only Mistral Large 4 measured |
| Finance Agent (Vals AI) | 54.7% | 38.2% | Mistral Large 4 |
| Tax Agent Bench (Vals AI) | 30.4% | 10.5% | Mistral Large 4 |
| Legal Research Bench (Vals AI) | 31.7% | 16.4% | Mistral Large 4 |
| Harvey's Legal Agent Benchmark (Vals AI) | 17.5% | Not measured | Only Mistral Large 4 measured |
| Excel Modeling Benchmark (Vals AI) | 56.2% | 49.3% | Mistral Large 4 |
| BioMysteryBench (Vals AI) | 67.0% | Not measured | Only Mistral Large 4 measured |
| τ²-Bench Telecom | Not measured | 93.0% | Only Qwen3.7 Plus measured |
| OSWorld 2.0 | Not measured | 2.8% | Only Qwen3.7 Plus measured |
| Neither model has a result on the other 29 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | Mistral Large 4 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| LiveBench reasoning | 83.9% | Not measured | Only Mistral Large 4 measured |
| MysteryMechanism (Vals AI) | 12.2% | Not measured | Only Mistral Large 4 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 90.0% | Only Qwen3.7 Plus measured |
| Humanity's Last Exam (Artificial Analysis run) | 35.0% | 35.6% | Qwen3.7 Plus |
| GPQA Diamond (Epoch AI run) | 88.1% | 87.9% | Mistral Large 4 |
| Chess puzzles | 33.0% | Not measured | Only Mistral Large 4 measured |
| Mystery game puzzles | 17.0% | 17.0% | Tie |
| LMArena Text: hard prompts | 1444 | 1460 | Qwen3.7 Plus |
| LMArena Text: expert | 1444 | 1466 | Qwen3.7 Plus |
| LMArena Text: hard prompts in English | 1442 | 1460 | Qwen3.7 Plus |
| LMArena Vision: diagrams | 1288 | 1292 | Qwen3.7 Plus |
| LMArena Vision: homework | Not measured | 1295 | Only Qwen3.7 Plus measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | Mistral Large 4 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.3% | 93.3% | Mistral Large 4 |
| FrontierMath, tiers 1 to 3 | 58.6% | 34.4% | Mistral Large 4 |
| FrontierMath, tier 4 | 26.8% | Not measured | Only Mistral Large 4 measured |
| ProofBench | 10.0% | Not measured | Only Mistral Large 4 measured |
| LMArena Text: math | 1469 | 1467 | Mistral Large 4 |
| LMArena Text: mathematical work | 1462 | 1470 | Qwen3.7 Plus |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
| Benchmark | Mistral Large 4 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| MortgageTax (Vals AI) | Not measured | 66.2% | Only Qwen3.7 Plus measured |
| MedCode (Vals AI) | 40.7% | Not measured | Only Mistral Large 4 measured |
| SimpleQA Verified | 20.0% | Not measured | Only Mistral Large 4 measured |
| LMArena Text: science | 1448 | 1464 | Qwen3.7 Plus |
| LMArena Text: medicine and healthcare | 1433 | 1464 | Qwen3.7 Plus |
| LMArena Text: legal and government | 1425 | 1451 | Qwen3.7 Plus |
| LMArena Text: business and finance | 1439 | 1435 | Mistral Large 4 |
| Neither model has a result on the other 11 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index44
| Benchmark | Mistral Large 4 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| Design Arena: websites | 1257 | 1269 | Qwen3.7 Plus |
| Design Arena: UI components | 1272 | 1258 | Mistral Large 4 |
| Design Arena: game dev | 1225 | 1261 | Qwen3.7 Plus |
| Design Arena: data viz | 1275 | 1276 | Qwen3.7 Plus |
| Design Arena: 3D | 1252 | 1238 | Mistral Large 4 |
| Design Arena: all code categories | 1259 | 1269 | Qwen3.7 Plus |
| Design Arena: SVG | 1232 | 1208 | Mistral Large 4 |
| Design Arena: ASCII art | 1202 | 1143 | Mistral Large 4 |
| LiveBench design | 65.6% | Not measured | Only Mistral Large 4 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index515
| Benchmark | Mistral Large 4 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| LiveBench data analysis | 76.5% | Not measured | Only Mistral Large 4 measured |
| SAGE (Vals AI) | Not measured | 39.3% | Only Qwen3.7 Plus measured |
| MedScribe (Vals AI) | 80.4% | Not measured | Only Mistral Large 4 measured |
| Vals Multimodal Index | Not measured | 53.9% | Only Qwen3.7 Plus measured |
| IFBench | Not measured | 78.0% | Only Qwen3.7 Plus measured |
| Long context reasoning (AA-LCR) | 81.3% | 73.0% | Mistral Large 4 |
| DTBench | 88.2% | 84.0% | Mistral Large 4 |
| LMCA | 35.8% | 37.6% | Qwen3.7 Plus |
| Surface Evolver Bench | 28.1% | Not measured | Only Mistral Large 4 measured |
| Furniture assembly | 20.8% | Not measured | Only Mistral Large 4 measured |
| LMArena Text | 1431 | 1454 | Qwen3.7 Plus |
| LMArena Text: instruction following | 1422 | 1439 | Qwen3.7 Plus |
| LMArena Text: creative writing | 1363 | 1438 | Qwen3.7 Plus |
| LMArena Vision | 1272 | 1278 | Qwen3.7 Plus |
| LMArena Text: English | 1433 | 1458 | Qwen3.7 Plus |
| LMArena Text: non-English | 1422 | 1445 | Qwen3.7 Plus |
| LMArena Text: Chinese | 1492 | 1509 | Qwen3.7 Plus |
| LMArena Text: German | Not measured | 1471 | Only Qwen3.7 Plus measured |
| LMArena Text: Japanese | 1415 | 1412 | Mistral Large 4 |
| LMArena Text: Korean | Not measured | 1413 | Only Qwen3.7 Plus measured |
| LMArena Text: French | 1492 | 1472 | Mistral Large 4 |
| LMArena Text: Spanish | 1455 | 1457 | Qwen3.7 Plus |
| LMArena Text: Russian | 1417 | 1457 | Qwen3.7 Plus |
| LMArena Text: Polish | Not measured | 1449 | Only Qwen3.7 Plus measured |
| LMArena Text: multi-turn | 1435 | 1459 | Qwen3.7 Plus |
| LMArena Text: longer queries | 1428 | 1454 | Qwen3.7 Plus |
| LMArena Text: writing and language | 1386 | 1437 | Qwen3.7 Plus |
| LMArena Text: entertainment and media | 1378 | 1422 | Qwen3.7 Plus |
| LMArena Vision: English | 1283 | 1280 | Mistral Large 4 |
| LMArena Vision: Chinese | Not measured | 1332 | Only Qwen3.7 Plus measured |
| LMArena Vision: reading text | 1284 | 1288 | Qwen3.7 Plus |
| LMArena Vision: humor | Not measured | 1261 | Only Qwen3.7 Plus measured |
| LMArena Vision: creative writing | Not measured | 1258 | Only Qwen3.7 Plus measured |
| LMArena Document | Not measured | 1444 | Only Qwen3.7 Plus measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large 4 | Qwen3.7 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
Qwen3.7 Plus costs less
Repository review
50K input + 3K output tokens
Qwen3.7 Plus costs less
Agent loop
200K input + 10K output tokens
Qwen3.7 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 4 | Qwen3.7 Plus |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | October 6, 2026 | June 3, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $1.36 | $0.32 |
| Output $/Mtok | $4.18 | $1.28 |
| Coding index | 56.4 | 33.2 |
| Independent measurements behind it | 5 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Large 4 better than Qwen3.7 Plus for coding?
Pick Mistral Large 4 for coding: it scores 56.4 on the Vibe Coding Index against 33.2 for Qwen3.7 Plus. Pick Qwen3.7 Plus for output tokens 3.3x cheaper ($1.28 vs $4.18 per million) and work measured by LMArena Text: software and IT or Humanity's Last Exam (Artificial Analysis run), where it leads.
Which is cheaper, Mistral Large 4 or Qwen3.7 Plus?
Mistral Large 4 costs $1.36 per million input tokens and $4.18 per million output tokens; Qwen3.7 Plus costs $0.32 and $1.28. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Large 4 or Qwen3.7 Plus?
Mistral Large 4, with 1.05M tokens against 1M.
How many benchmarks is the Mistral Large 4 vs Qwen3.7 Plus comparison based on?
55 benchmarks have a result for both models as of October 22, 2026. Mistral Large 4's coding index rests on 5 independent benchmarks and Qwen3.7 Plus's on 2. Where only one model has a result, the page says so instead of naming a winner.