Qwen3.5-122B-A10B vs Qwen3 Next 80B A3B Thinking
Qwen3.5-122B-A10B leads Qwen3 Next 80B A3B Thinking on the Artificial Analysis Intelligence Index, 17.7 to 11.2, and Qwen3 Next 80B A3B Thinking costs 1.7x less per output token ($1.20 vs $2.08 per million). Updated October 22, 2026; 30 benchmarks have a result for both.
Qwen3.5-122B-A10B or Qwen3 Next 80B A3B Thinking: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 30
- Qwen3.5-122B-A10B only
- 21
- Qwen3 Next 80B A3B Thinking only
- 0
- 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 11.2.
Same board, both measured
Long documents
Listed context window
No call
Both take 262K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 Next 80B A3B Thinking
Qwen3.5-122B-A10B $0.0013, Qwen3 Next 80B A3B Thinking $0.0008 per turn.
List prices
At a glance
- Coding index
- Qwen3.5-122B-A10B not benchmarked, Qwen3 Next 80B A3B Thinking not benchmarked.
- Widest category
- Coding, 13.5 points apart on average over 3 shared benchmarks.
- Widest single gap
- AA Coding Index: Qwen3.5-122B-A10B 43.3 against Qwen3 Next 80B A3B Thinking 17.4, 25.9 points apart on a 0 to 100 scale.
- Output price
- Qwen3.5-122B-A10B $2.08 against Qwen3 Next 80B A3B Thinking $1.20 per million output tokens, 1.7x 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 Next 80B A3B Thinking | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 3 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 17 | 0 | ||
| All shared boards | 30 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | Qwen3.5-122B-A10B | Qwen3 Next 80B A3B Thinking | Leader |
|---|---|---|---|
| LMArena WebDev | 1361 | Not measured | Only Qwen3.5-122B-A10B measured |
| AA Coding Index | 43.3 | 17.4 | Qwen3.5-122B-A10B |
| SciCode | 39.7% | Not measured | Only Qwen3.5-122B-A10B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: coding | 1436 | 1392 | Qwen3.5-122B-A10B |
| LMArena Text: software and IT | 1436 | 1393 | Qwen3.5-122B-A10B |
| LMArena WebDev: React | 1353 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena WebDev: HTML | 1351 | Not measured | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Qwen3.5-122B-A10B | Qwen3 Next 80B A3B Thinking | Leader |
|---|---|---|---|
| AA Intelligence Index | 17.7 | 11.2 | Qwen3.5-122B-A10B |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Qwen3.5-122B-A10B | Qwen3 Next 80B A3B Thinking | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 93.6% | Not measured | Only Qwen3.5-122B-A10B measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Qwen3.5-122B-A10B | Qwen3 Next 80B A3B Thinking | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 85.7% | Not measured | Only Qwen3.5-122B-A10B measured |
| Humanity's Last Exam (Artificial Analysis run) | 25.2% | Not measured | Only Qwen3.5-122B-A10B measured |
| Mystery game puzzles | 17.0% | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: hard prompts | 1421 | 1371 | Qwen3.5-122B-A10B |
| LMArena Text: expert | 1431 | 1376 | Qwen3.5-122B-A10B |
| LMArena Text: hard prompts in English | 1431 | 1384 | Qwen3.5-122B-A10B |
| 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 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Qwen3.5-122B-A10B | Qwen3 Next 80B A3B Thinking | Leader |
|---|---|---|---|
| LMArena Text: math | 1431 | 1399 | Qwen3.5-122B-A10B |
| LMArena Text: mathematical work | 1431 | 1390 | Qwen3.5-122B-A10B |
| Neither model has a result on the other 12 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Qwen3.5-122B-A10B | Qwen3 Next 80B A3B Thinking | Leader |
|---|---|---|---|
| LMArena Text: science | 1432 | 1384 | Qwen3.5-122B-A10B |
| LMArena Text: medicine and healthcare | 1423 | 1392 | Qwen3.5-122B-A10B |
| LMArena Text: legal and government | 1422 | 1364 | Qwen3.5-122B-A10B |
| LMArena Text: business and finance | 1414 | 1360 | Qwen3.5-122B-A10B |
| 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 Next 80B A3B Thinking | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index170
| Benchmark | Qwen3.5-122B-A10B | Qwen3 Next 80B A3B Thinking | Leader |
|---|---|---|---|
| IFBench | 75.7% | Not measured | Only Qwen3.5-122B-A10B measured |
| Long context reasoning (AA-LCR) | 76.3% | Not measured | Only Qwen3.5-122B-A10B measured |
| DTBench | 84.3% | Not measured | Only Qwen3.5-122B-A10B measured |
| LMCA | 32.2% | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text | 1417 | 1368 | Qwen3.5-122B-A10B |
| LMArena Text: instruction following | 1399 | 1344 | Qwen3.5-122B-A10B |
| LMArena Text: creative writing | 1368 | 1314 | Qwen3.5-122B-A10B |
| LMArena Vision | 1244 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: English | 1428 | 1392 | Qwen3.5-122B-A10B |
| LMArena Text: non-English | 1401 | 1342 | Qwen3.5-122B-A10B |
| LMArena Text: Chinese | 1463 | 1407 | Qwen3.5-122B-A10B |
| LMArena Text: German | 1425 | 1356 | Qwen3.5-122B-A10B |
| LMArena Text: Japanese | 1367 | 1280 | Qwen3.5-122B-A10B |
| LMArena Text: Korean | 1353 | 1310 | Qwen3.5-122B-A10B |
| LMArena Text: French | 1443 | 1351 | Qwen3.5-122B-A10B |
| LMArena Text: Spanish | 1425 | 1358 | Qwen3.5-122B-A10B |
| LMArena Text: Russian | 1400 | 1337 | Qwen3.5-122B-A10B |
| LMArena Text: Polish | 1398 | 1345 | Qwen3.5-122B-A10B |
| LMArena Text: multi-turn | 1416 | 1346 | Qwen3.5-122B-A10B |
| LMArena Text: longer queries | 1410 | 1353 | Qwen3.5-122B-A10B |
| LMArena Text: writing and language | 1387 | 1330 | Qwen3.5-122B-A10B |
| LMArena Text: entertainment and media | 1366 | 1312 | Qwen3.5-122B-A10B |
| 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 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3.5-122B-A10B | Qwen3 Next 80B A3B Thinking | 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 Next 80B A3B Thinking costs less
Repository review
50K input + 3K output tokens
Qwen3 Next 80B A3B Thinking costs less
Agent loop
200K input + 10K output tokens
Qwen3 Next 80B A3B Thinking 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 Next 80B A3B Thinking |
|---|---|---|
| Maker | Qwen | Qwen |
| Weights | Open weights | Open weights |
| Released | February 25, 2026 | September 11, 2025 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.26 | $0.15 |
| Output $/Mtok | $2.08 | $1.20 |
| 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 Next 80B A3B Thinking for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Qwen3.5-122B-A10B or Qwen3 Next 80B A3B Thinking?
Qwen3.5-122B-A10B costs $0.26 per million input tokens and $2.08 per million output tokens; Qwen3 Next 80B A3B Thinking costs $0.15 and $1.20. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen3.5-122B-A10B or Qwen3 Next 80B A3B Thinking?
Neither: both take 262K tokens.
How many benchmarks is the Qwen3.5-122B-A10B vs Qwen3 Next 80B A3B Thinking comparison based on?
30 benchmarks have a result for both models as of October 22, 2026. Qwen3.5-122B-A10B's coding index rests on 1 independent benchmark and Qwen3 Next 80B A3B Thinking's on 0. Where only one model has a result, the page says so instead of naming a winner.