Qwen3.5-122B-A10B vs Qwen3.6 35B A3B
Qwen3.6 35B A3B leads Qwen3.5-122B-A10B on the Artificial Analysis Intelligence Index, 18.2 to 17.7, and Qwen3.6 35B A3B costs 2.1x less per output token ($1.00 vs $2.08 per million). Updated October 23, 2026; 12 benchmarks have a result for both.
Qwen3.5-122B-A10B or Qwen3.6 35B A3B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Qwen3.5-122B-A10B for work measured by Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.
- Shared results
- 12
- Qwen3.5-122B-A10B only
- 39
- Qwen3.6 35B A3B only
- 8
- Like-for-like categories
- 5 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
Qwen3.6 35B A3B
Qwen3.6 35B A3B leads, 18.2 against 17.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.6 35B A3B has a AA Agentic Index result, 13.1.
Directional only
Long documents
Listed context window
No call
Both take 262K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.6 35B A3B
Qwen3.5-122B-A10B $0.0013, Qwen3.6 35B A3B $0.0006 per turn.
List prices
At a glance
- Coding index
- Qwen3.5-122B-A10B not benchmarked, Qwen3.6 35B A3B not benchmarked.
- Widest category
- General, 7.2 points apart on average over 4 shared benchmarks.
- Widest single gap
- IFBench: Qwen3.5-122B-A10B 75.7% against Qwen3.6 35B A3B 64.4%, 11.4 points apart on a 0 to 100 scale.
- Output price
- Qwen3.5-122B-A10B $2.08 against Qwen3.6 35B A3B $1.00 per million output tokens, 2.1x apart.
- Context window
- Both take 262K tokens.
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 | Qwen3.5-122B-A10B | Bars | Qwen3.6 35B A3B | Ties |
|---|---|---|---|---|
| Coding | 2 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 2 | 1 | ||
| General | 4 | 0 | ||
| All shared boards | 8 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index21
| Benchmark | Qwen3.5-122B-A10B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| LMArena WebDev | 1361 | Not measured | Only Qwen3.5-122B-A10B measured |
| AA Coding Index | 43.3 | 41.9 | Qwen3.5-122B-A10B |
| SciCode | 39.7% | 36.6% | Qwen3.5-122B-A10B |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | 34.8% | Qwen3.6 35B A3B |
| Terminal-Bench | Not measured | 24.6% | Only Qwen3.6 35B A3B measured |
| WeirdML | Not measured | 34.5% | Only Qwen3.6 35B A3B measured |
| LMArena Text: coding | 1436 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: software and IT | 1436 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena WebDev: React | 1352 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena WebDev: HTML | 1352 | Not measured | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3.5-122B-A10B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| AA Intelligence Index | 17.7 | 18.2 | Qwen3.6 35B A3B |
| Epoch Capabilities Index | Not measured | 143.7 | Only Qwen3.6 35B A3B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3.5-122B-A10B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 13.1 | Only Qwen3.6 35B A3B measured |
| τ²-Bench Telecom | 93.6% | 95.3% | Qwen3.6 35B A3B |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | Qwen3.5-122B-A10B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 85.7% | 84.1% | Qwen3.5-122B-A10B |
| Humanity's Last Exam (Artificial Analysis run) | 25.2% | 22.2% | Qwen3.5-122B-A10B |
| GPQA Diamond (Epoch AI run) | Not measured | 84.8% | Only Qwen3.6 35B A3B measured |
| Mystery game puzzles | 17.0% | 22.0% | Qwen3.6 35B A3B |
| LMArena Text: hard prompts | 1421 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: expert | 1431 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: hard prompts in English | 1431 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: diagrams | 1249 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: homework | 1261 | Not measured | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Qwen3.5-122B-A10B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 86.7% | Only Qwen3.6 35B A3B measured |
| FrontierMath, tiers 1 to 3 | Not measured | 20.4% | Only Qwen3.6 35B A3B measured |
| LMArena Text: math | 1431 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: mathematical work | 1431 | Not measured | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Qwen3.5-122B-A10B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| LMArena Text: science | 1433 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: medicine and healthcare | 1423 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: legal and government | 1422 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: business and finance | 1414 | Not measured | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 16 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3.5-122B-A10B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Qwen3.5-122B-A10B | Qwen3.6 35B A3B | Leader |
|---|---|---|---|
| IFBench | 75.7% | 64.4% | Qwen3.5-122B-A10B |
| Long context reasoning (AA-LCR) | 76.3% | 71.7% | Qwen3.5-122B-A10B |
| DTBench | 84.3% | 73.9% | Qwen3.5-122B-A10B |
| LMCA | 32.2% | 29.7% | Qwen3.5-122B-A10B |
| Surface Evolver Bench | Not measured | 44.4% | Only Qwen3.6 35B A3B measured |
| LMArena Text | 1418 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: instruction following | 1400 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: creative writing | 1368 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Vision | 1244 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: English | 1428 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: non-English | 1401 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Chinese | 1463 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: German | 1425 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Japanese | 1367 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Korean | 1353 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: French | 1443 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Spanish | 1425 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Russian | 1400 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: Polish | 1399 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: multi-turn | 1417 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: longer queries | 1411 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: writing and language | 1387 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: entertainment and media | 1366 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: English | 1249 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: Chinese | 1301 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: reading text | 1251 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: humor | 1243 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Vision: creative writing | 1220 | Not measured | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 20 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3.5-122B-A10B | Qwen3.6 35B A3B | 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
Qwen3.6 35B A3B costs less
Repository review
50K input + 3K output tokens
Qwen3.6 35B A3B costs less
Agent loop
200K input + 10K output tokens
Qwen3.6 35B A3B 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 | Qwen3.5-122B-A10B | Qwen3.6 35B A3B |
|---|---|---|
| Maker | Qwen | Qwen |
| Weights | Open weights | Open weights |
| Released | February 25, 2026 | April 27, 2026 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.26 | $0.10 |
| Output $/Mtok | $2.08 | $1.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen3.5-122B-A10B better than Qwen3.6 35B A3B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Qwen3.5-122B-A10B for work measured by Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.
Which is cheaper, Qwen3.5-122B-A10B or Qwen3.6 35B A3B?
Qwen3.5-122B-A10B costs $0.26 per million input tokens and $2.08 per million output tokens; Qwen3.6 35B A3B costs $0.10 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen3.5-122B-A10B or Qwen3.6 35B A3B?
Neither: both take 262K tokens.
How many benchmarks is the Qwen3.5-122B-A10B vs Qwen3.6 35B A3B comparison based on?
12 benchmarks have a result for both models as of October 23, 2026. Qwen3.5-122B-A10B's coding index rests on 1 independent benchmark and Qwen3.6 35B A3B's on 0. Where only one model has a result, the page says so instead of naming a winner.