Mistral Large vs Qwen3.5-122B-A10B
Qwen3.5-122B-A10B leads Mistral Large on the Artificial Analysis Intelligence Index, 17.7 to 5.8, and Qwen3.5-122B-A10B costs 2.9x less per output token ($2.08 vs $6.00 per million). Updated October 22, 2026; 32 benchmarks have a result for both.
Mistral Large or Qwen3.5-122B-A10B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Mistral Large for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 32
- Mistral Large only
- 12
- Qwen3.5-122B-A10B only
- 19
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.5-122B-A10B
Qwen3.5-122B-A10B leads, 17.7 against 5.8.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Mistral Large has a GPQA Diamond result, 47.7%.
Directional only
Math
AIME
No call
Only Mistral Large has a AIME result, 9.2%.
Directional only
Knowledge
MMLU Pro
No call
Only Mistral Large has a MMLU Pro result, 69.7%.
Directional only
Long documents
Listed context window
Qwen3.5-122B-A10B
Qwen3.5-122B-A10B takes 262K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-122B-A10B
Mistral Large $0.0050, Qwen3.5-122B-A10B $0.0013 per turn.
List prices
At a glance
- Coding index
- Mistral Large not benchmarked, Qwen3.5-122B-A10B not benchmarked.
- Widest category
- Reasoning, 32.8 points apart on average over 5 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): Mistral Large 35.1% against Qwen3.5-122B-A10B 85.7%, 50.6 points apart on a 0 to 100 scale.
- Output price
- Mistral Large $6.00 against Qwen3.5-122B-A10B $2.08 per million output tokens, 2.9x apart.
- Context window
- Mistral Large takes 128K 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 | Bars | Qwen3.5-122B-A10B | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 18 | ||
| All shared boards | 0 | 32 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Mistral Large | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1361 | Only Qwen3.5-122B-A10B measured |
| AA Coding Index | Not measured | 43.3 | Only Qwen3.5-122B-A10B measured |
| LiveCodeBench (Vals AI) | 37.1% | Not measured | Only Mistral Large measured |
| LiveCodeBench (Artificial Analysis run) | 17.8% | Not measured | Only Mistral Large measured |
| SciCode | Not measured | 39.7% | Only Qwen3.5-122B-A10B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 31.1% | Only Qwen3.5-122B-A10B measured |
| LMArena Text: coding | 1275 | 1436 | Qwen3.5-122B-A10B |
| LMArena Text: software and IT | 1274 | 1436 | Qwen3.5-122B-A10B |
| LMArena WebDev: React | Not measured | 1353 | Only Qwen3.5-122B-A10B measured |
| LMArena WebDev: HTML | Not measured | 1351 | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Large | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.8 | 17.7 | Qwen3.5-122B-A10B |
| Epoch Capabilities Index | 122.1 | Not measured | Only Mistral Large measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Mistral Large | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 93.6% | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Mistral Large | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| GPQA Diamond | 47.7% | Not measured | Only Mistral Large measured |
| GPQA Diamond (Artificial Analysis run) | 35.1% | 85.7% | Qwen3.5-122B-A10B |
| Humanity's Last Exam (Artificial Analysis run) | 3.5% | 25.2% | Qwen3.5-122B-A10B |
| Mystery game puzzles | Not measured | 17.0% | Only Qwen3.5-122B-A10B measured |
| LMArena Text: hard prompts | 1257 | 1421 | Qwen3.5-122B-A10B |
| LMArena Text: expert | 1208 | 1431 | Qwen3.5-122B-A10B |
| LMArena Text: hard prompts in English | 1268 | 1431 | Qwen3.5-122B-A10B |
| LMArena Vision: diagrams | Not measured | 1249 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: homework | Not measured | 1261 | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Large | Qwen3.5-122B-A10B | 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 |
| LMArena Text: math | 1261 | 1431 | Qwen3.5-122B-A10B |
| LMArena Text: mathematical work | 1257 | 1431 | Qwen3.5-122B-A10B |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Mistral Large | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| MMLU Pro | 69.7% | Not measured | Only Mistral Large measured |
| TaxEval (Vals AI) | 63.8% | Not measured | Only Mistral Large measured |
| MedQA (Vals AI) | 76.2% | Not measured | Only Mistral Large measured |
| MMLU Pro (Artificial Analysis run) | 51.5% | Not measured | Only Mistral Large measured |
| MMLU | 68.8% | Not measured | Only Mistral Large measured |
| LMArena Text: science | 1260 | 1432 | Qwen3.5-122B-A10B |
| LMArena Text: medicine and healthcare | 1255 | 1423 | Qwen3.5-122B-A10B |
| LMArena Text: legal and government | 1275 | 1422 | Qwen3.5-122B-A10B |
| LMArena Text: business and finance | 1238 | 1414 | Qwen3.5-122B-A10B |
| Neither model has a result on the other 9 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
| Benchmark | Mistral Large | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| IFBench | Not measured | 75.7% | Only Qwen3.5-122B-A10B measured |
| Long context reasoning (AA-LCR) | Not measured | 76.3% | Only Qwen3.5-122B-A10B measured |
| DTBench | 60.8% | 84.3% | Qwen3.5-122B-A10B |
| LMCA | Not measured | 32.2% | Only Qwen3.5-122B-A10B measured |
| LMArena Text | 1265 | 1417 | Qwen3.5-122B-A10B |
| LMArena Text: instruction following | 1249 | 1399 | Qwen3.5-122B-A10B |
| LMArena Text: creative writing | 1242 | 1368 | Qwen3.5-122B-A10B |
| LMArena Vision | Not measured | 1244 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: English | 1284 | 1428 | Qwen3.5-122B-A10B |
| LMArena Text: non-English | 1235 | 1401 | Qwen3.5-122B-A10B |
| LMArena Text: Chinese | 1239 | 1463 | Qwen3.5-122B-A10B |
| LMArena Text: German | 1242 | 1425 | Qwen3.5-122B-A10B |
| LMArena Text: Japanese | 1170 | 1367 | Qwen3.5-122B-A10B |
| LMArena Text: Korean | 1202 | 1353 | Qwen3.5-122B-A10B |
| LMArena Text: French | 1325 | 1443 | Qwen3.5-122B-A10B |
| LMArena Text: Spanish | 1268 | 1425 | Qwen3.5-122B-A10B |
| LMArena Text: Russian | 1252 | 1400 | Qwen3.5-122B-A10B |
| LMArena Text: Polish | 1157 | 1398 | Qwen3.5-122B-A10B |
| LMArena Text: multi-turn | 1260 | 1416 | Qwen3.5-122B-A10B |
| LMArena Text: longer queries | 1261 | 1410 | Qwen3.5-122B-A10B |
| LMArena Text: writing and language | 1261 | 1387 | Qwen3.5-122B-A10B |
| LMArena Text: entertainment and media | 1230 | 1366 | Qwen3.5-122B-A10B |
| LMArena Vision: English | Not measured | 1249 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: Chinese | Not measured | 1301 | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: reading text | Not measured | 1251 | Only Qwen3.5-122B-A10B measured |
| 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 19 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large | 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 | Qwen3.5-122B-A10B |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | February 26, 2024 | February 25, 2026 |
| Context window | 128K | 262K |
| Input $/Mtok | $2.00 | $0.26 |
| Output $/Mtok | $6.00 | $2.08 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Large better than Qwen3.5-122B-A10B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mistral Large for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, Mistral Large or Qwen3.5-122B-A10B?
Mistral Large costs $2.00 per million input tokens and $6.00 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 or Qwen3.5-122B-A10B?
Qwen3.5-122B-A10B, with 262K tokens against 128K.
How many benchmarks is the Mistral Large vs Qwen3.5-122B-A10B comparison based on?
32 benchmarks have a result for both models as of October 22, 2026. Mistral Large's coding index rests on 0 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.