Mistral Large 4 vs Qwen3.5-122B-A10B
Mistral Large 4 leads Qwen3.5-122B-A10B on the Artificial Analysis Intelligence Index, 38.4 to 17.7, and Qwen3.5-122B-A10B costs 2.0x less per output token ($2.08 vs $4.18 per million). Updated October 22, 2026; 39 benchmarks have a result for both.
Mistral Large 4 or Qwen3.5-122B-A10B: which to pick
Only Mistral Large 4 has a coding score, 56.4 on the Vibe Coding Index; Qwen3.5-122B-A10B has none, so the two cannot be ranked on coding.
Pick Qwen3.5-122B-A10B for output tokens 2.0x cheaper ($2.08 vs $4.18 per million) and work measured by LMArena Text: creative writing or LMArena Text: writing and language, where it leads.
- Shared results
- 39
- Mistral Large 4 only
- 43
- Qwen3.5-122B-A10B only
- 12
- 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 Mistral Large 4 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Mistral Large 4
Mistral Large 4 leads, 38.4 against 17.7.
Same board, both measured
Web design
Design Arena: websites
No call
Only Mistral Large 4 has a Design Arena: websites result, 1258.
Directional only
Long documents
Listed context window
Mistral Large 4
Mistral Large 4 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-122B-A10B
Mistral Large 4 $0.0034, Qwen3.5-122B-A10B $0.0013 per turn.
List prices
At a glance
- Coding index
- Mistral Large 4 56.4 from 5 benchmarks, Qwen3.5-122B-A10B not benchmarked.
- Widest category
- Intelligence, 20.7 points apart on average over 1 shared benchmark.
- Widest single gap
- LMArena WebDev: React: Mistral Large 4 1559 against Qwen3.5-122B-A10B 1353, 34.3 points apart on a 0 to 100 scale.
- Output price
- Mistral Large 4 $4.18 against Qwen3.5-122B-A10B $2.08 per million output tokens, 2.0x apart.
- Context window
- Mistral Large 4 takes 1.05M tokens, Qwen3.5-122B-A10B 262K.
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 | Qwen3.5-122B-A10B | Ties |
|---|---|---|---|---|
| Coding | 6 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 5 | 0 | 1 tie | |
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 18 | 2 | ||
| All shared boards | 36 | 2 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index60
| Benchmark | Mistral Large 4 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| LiveBench coding | 60.0% | Not measured | Only Mistral Large 4 measured |
| LMArena WebDev | 1543 | 1361 | Mistral Large 4 |
| AA Coding Index | Not measured | 43.3 | Only Qwen3.5-122B-A10B measured |
| Terminal-Bench 4 (Vals AI) | 27.8% | Not measured | Only Mistral Large 4 measured |
| Vibe Code Bench (Vals AI) | 78.4% | Not measured | Only Mistral Large 4 measured |
| Code migration (Vals AI) | 30.6% | Not measured | Only Mistral Large 4 measured |
| IOI (Vals AI) | 45.3% | Not measured | Only Mistral Large 4 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 |
| SciCode | 54.2% | 39.7% | Mistral Large 4 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 31.1% | Only Qwen3.5-122B-A10B measured |
| FrontierCode | 39.4% | Not measured | Only Mistral Large 4 measured |
| ALE-Bench | 1213.2 | Not measured | Only Mistral Large 4 measured |
| LMArena Text: coding | 1478 | 1436 | Mistral Large 4 |
| LMArena Text: software and IT | 1467 | 1436 | Mistral Large 4 |
| LMArena WebDev: React | 1559 | 1353 | Mistral Large 4 |
| LMArena WebDev: HTML | 1487 | 1351 | Mistral Large 4 |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Mistral Large 4 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| AA Intelligence Index | 38.4 | 17.7 | Mistral Large 4 |
| Epoch Capabilities Index | 150.0 | Not measured | Only Mistral Large 4 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | Mistral Large 4 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| 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% | Not measured | Only Mistral Large 4 measured |
| Tax Agent Bench (Vals AI) | 30.4% | Not measured | Only Mistral Large 4 measured |
| Legal Research Bench (Vals AI) | 31.7% | Not measured | Only Mistral Large 4 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 17.5% | Not measured | Only Mistral Large 4 measured |
| Excel Modeling Benchmark (Vals AI) | 56.2% | Not measured | Only Mistral Large 4 measured |
| BioMysteryBench (Vals AI) | 67.0% | Not measured | Only Mistral Large 4 measured |
| τ²-Bench Telecom | Not measured | 93.6% | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Mistral Large 4 | Qwen3.5-122B-A10B | 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 | 85.7% | Only Qwen3.5-122B-A10B measured |
| Humanity's Last Exam (Artificial Analysis run) | 35.0% | 25.2% | Mistral Large 4 |
| GPQA Diamond (Epoch AI run) | 88.1% | Not measured | Only Mistral Large 4 measured |
| Chess puzzles | 33.0% | Not measured | Only Mistral Large 4 measured |
| Mystery game puzzles | 17.0% | 17.0% | Tie |
| LMArena Text: hard prompts | 1444 | 1421 | Mistral Large 4 |
| LMArena Text: expert | 1444 | 1431 | Mistral Large 4 |
| LMArena Text: hard prompts in English | 1442 | 1431 | Mistral Large 4 |
| LMArena Vision: diagrams | 1288 | 1249 | Mistral Large 4 |
| LMArena Vision: homework | Not measured | 1261 | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Mistral Large 4 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.3% | Not measured | Only Mistral Large 4 measured |
| FrontierMath, tiers 1 to 3 | 58.6% | Not measured | Only Mistral Large 4 measured |
| 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 | 1431 | Mistral Large 4 |
| LMArena Text: mathematical work | 1462 | 1431 | Mistral Large 4 |
| MathArena | 26.7% | Not measured | Only Mistral Large 4 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Mistral Large 4 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| 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 | 1432 | Mistral Large 4 |
| LMArena Text: medicine and healthcare | 1433 | 1423 | Mistral Large 4 |
| LMArena Text: legal and government | 1425 | 1422 | Mistral Large 4 |
| LMArena Text: business and finance | 1439 | 1414 | Mistral Large 4 |
| Neither model has a result on the other 14 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | Mistral Large 4 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| Design Arena: websites | 1258 | Not measured | Only Mistral Large 4 measured |
| Design Arena: UI components | 1273 | Not measured | Only Mistral Large 4 measured |
| Design Arena: game dev | 1225 | Not measured | Only Mistral Large 4 measured |
| Design Arena: data viz | 1275 | Not measured | Only Mistral Large 4 measured |
| Design Arena: 3D | 1252 | Not measured | Only Mistral Large 4 measured |
| Design Arena: all code categories | 1259 | Not measured | Only Mistral Large 4 measured |
| Design Arena: SVG | 1232 | Not measured | Only Mistral Large 4 measured |
| Design Arena: ASCII art | 1202 | Not measured | Only Mistral Large 4 measured |
| LiveBench design | 65.6% | Not measured | Only Mistral Large 4 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index182
| Benchmark | Mistral Large 4 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| LiveBench data analysis | 76.5% | Not measured | Only Mistral Large 4 measured |
| MedScribe (Vals AI) | 80.4% | Not measured | Only Mistral Large 4 measured |
| IFBench | Not measured | 75.7% | Only Qwen3.5-122B-A10B measured |
| Long context reasoning (AA-LCR) | 81.3% | 76.3% | Mistral Large 4 |
| DTBench | 88.2% | 84.3% | Mistral Large 4 |
| LMCA | 35.8% | 32.2% | Mistral Large 4 |
| 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 | 1417 | Mistral Large 4 |
| LMArena Text: instruction following | 1422 | 1399 | Mistral Large 4 |
| LMArena Text: creative writing | 1363 | 1368 | Qwen3.5-122B-A10B |
| LMArena Vision | 1272 | 1244 | Mistral Large 4 |
| LMArena Text: English | 1433 | 1428 | Mistral Large 4 |
| LMArena Text: non-English | 1422 | 1401 | Mistral Large 4 |
| LMArena Text: Chinese | 1492 | 1463 | Mistral Large 4 |
| LMArena Text: German | Not measured | 1425 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Japanese | 1415 | 1367 | Mistral Large 4 |
| LMArena Text: Korean | Not measured | 1353 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: French | 1492 | 1443 | Mistral Large 4 |
| LMArena Text: Spanish | 1455 | 1425 | Mistral Large 4 |
| LMArena Text: Russian | 1417 | 1400 | Mistral Large 4 |
| LMArena Text: Polish | Not measured | 1398 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: multi-turn | 1435 | 1416 | Mistral Large 4 |
| LMArena Text: longer queries | 1428 | 1410 | Mistral Large 4 |
| LMArena Text: writing and language | 1386 | 1387 | Qwen3.5-122B-A10B |
| LMArena Text: entertainment and media | 1378 | 1366 | Mistral Large 4 |
| LMArena Vision: English | 1283 | 1249 | Mistral Large 4 |
| LMArena Vision: Chinese | Not measured | 1301 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: reading text | 1284 | 1251 | Mistral Large 4 |
| LMArena Vision: humor | Not measured | 1243 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: creative writing | Not measured | 1220 | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large 4 | Qwen3.5-122B-A10B | 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
Qwen3.5-122B-A10B costs less
Repository review
50K input + 3K output tokens
Qwen3.5-122B-A10B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-122B-A10B 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 | Qwen3.5-122B-A10B |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | October 6, 2026 | February 25, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $1.36 | $0.26 |
| Output $/Mtok | $4.18 | $2.08 |
| Coding index | 56.4 | Not benchmarked |
| Independent measurements behind it | 5 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Large 4 better than Qwen3.5-122B-A10B for coding?
Only Mistral Large 4 has a coding score, 56.4 on the Vibe Coding Index; Qwen3.5-122B-A10B has none, so the two cannot be ranked on coding. Pick Qwen3.5-122B-A10B for output tokens 2.0x cheaper ($2.08 vs $4.18 per million) and work measured by LMArena Text: creative writing or LMArena Text: writing and language, where it leads.
Which is cheaper, Mistral Large 4 or Qwen3.5-122B-A10B?
Mistral Large 4 costs $1.36 per million input tokens and $4.18 per million output tokens; Qwen3.5-122B-A10B costs $0.26 and $2.08. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Large 4 or Qwen3.5-122B-A10B?
Mistral Large 4, with 1.05M tokens against 262K.
How many benchmarks is the Mistral Large 4 vs Qwen3.5-122B-A10B comparison based on?
39 benchmarks have a result for both models as of October 22, 2026. Mistral Large 4's coding index rests on 5 independent benchmarks and Qwen3.5-122B-A10B's on 1. Where only one model has a result, the page says so instead of naming a winner.