Qwen3.5-122B-A10B vs Qwen3 Coder 30B A3B Instruct
Updated October 21, 2026. 7 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Qwen3 Coder 30B A3B Instruct for coding: it scores 60.4 on the Vibe Coding Index against 22.9 for Qwen3.5-122B-A10B.
Pick Qwen3.5-122B-A10B when what matters is Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.
- Shared results
- 7
- Qwen3.5-122B-A10B only
- 44
- Qwen3 Coder 30B A3B Instruct only
- 9
- Like-for-like categories
- 5 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
Qwen3 Coder 30B A3B Instruct
Qwen3 Coder 30B A3B Instruct scores 60.4 against 22.9.
One benchmark behind each score
Intelligence
AA Intelligence Index
Qwen3.5-122B-A10B
Qwen3.5-122B-A10B leads, 17.7 against 9.6.
Same board, both measured
Web design
Design Arena: websites
No call
Only Qwen3 Coder 30B A3B Instruct has a Design Arena: websites result, 1087.
Directional only
Long documents
Listed context window
No call
Both take 262K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 Coder 30B A3B Instruct
Qwen3.5-122B-A10B $0.0013, Qwen3 Coder 30B A3B Instruct $0.0002 per turn.
List prices
At a glance
- Coding index
- Qwen3.5-122B-A10B 22.9 from 1 benchmark, Qwen3 Coder 30B A3B Instruct 60.4 from 1 benchmark.
- Widest category
- Agentic work, 59.1 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Qwen3.5-122B-A10B 93.6% against Qwen3 Coder 30B A3B Instruct 34.5%, 59.1 points apart on a 0 to 100 scale.
- Output price
- Qwen3.5-122B-A10B $2.08 against Qwen3 Coder 30B A3B Instruct $0.28 per million output tokens, 7.4x 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 Coder 30B A3B Instruct | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 7 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index10
| Benchmark | Qwen3.5-122B-A10B | Qwen3 Coder 30B A3B Instruct | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 60.4% | Only Qwen3 Coder 30B A3B Instruct measured |
| SWE-bench Lite | Not measured | 49.7% | Only Qwen3 Coder 30B A3B Instruct measured |
| LMArena WebDev | 1361 | Not measured | Only Qwen3.5-122B-A10B measured |
| AA Coding Index | 43.3 | Not measured | Only Qwen3.5-122B-A10B measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 40.3% | Only Qwen3 Coder 30B A3B Instruct measured |
| SciCode | 39.7% | Not measured | Only Qwen3.5-122B-A10B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | 15.2% | Qwen3.5-122B-A10B |
| 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 | 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 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Qwen3.5-122B-A10B | Qwen3 Coder 30B A3B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 17.7 | 9.6 | Qwen3.5-122B-A10B |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Qwen3.5-122B-A10B | Qwen3 Coder 30B A3B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 93.6% | 34.5% | Qwen3.5-122B-A10B |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Qwen3.5-122B-A10B | Qwen3 Coder 30B A3B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 85.7% | 51.6% | Qwen3.5-122B-A10B |
| Humanity's Last Exam (Artificial Analysis run) | 25.2% | 3.8% | Qwen3.5-122B-A10B |
| Mystery game puzzles | 17.0% | Not measured | Only Qwen3.5-122B-A10B measured |
| 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 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Qwen3.5-122B-A10B | Qwen3 Coder 30B A3B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 29.0 | Only Qwen3 Coder 30B A3B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 29.0% | Only Qwen3 Coder 30B A3B Instruct 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 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Qwen3.5-122B-A10B | Qwen3 Coder 30B A3B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 70.6% | Only Qwen3 Coder 30B A3B Instruct measured |
| LMArena Text: science | 1432 | 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 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Qwen3.5-122B-A10B | Qwen3 Coder 30B A3B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1087 | Only Qwen3 Coder 30B A3B Instruct measured |
| Design Arena: UI components | Not measured | 1046 | Only Qwen3 Coder 30B A3B Instruct measured |
| Design Arena: data viz | Not measured | 1085 | Only Qwen3 Coder 30B A3B Instruct measured |
| Neither model has a result on the other 6 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Qwen3.5-122B-A10B | Qwen3 Coder 30B A3B Instruct | Leader |
|---|---|---|---|
| IFBench | 75.7% | 32.7% | Qwen3.5-122B-A10B |
| Long context reasoning (AA-LCR) | 76.3% | 32.7% | Qwen3.5-122B-A10B |
| 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 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: instruction following | 1399 | 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 | 1398 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: multi-turn | 1416 | Not measured | Only Qwen3.5-122B-A10B measured |
| LMArena Text: longer queries | 1410 | 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 19 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3.5-122B-A10B | Qwen3 Coder 30B A3B Instruct | 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 Coder 30B A3B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen3 Coder 30B A3B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen3 Coder 30B A3B Instruct 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 Coder 30B A3B Instruct |
|---|---|---|
| Maker | Qwen | Qwen |
| Weights | Open weights | Open weights |
| Released | February 25, 2026 | July 31, 2025 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.26 | $0.07 |
| Output $/Mtok | $2.08 | $0.28 |
| Coding index | 22.9 | 60.4 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen3.5-122B-A10B better than Qwen3 Coder 30B A3B Instruct for coding?
Pick Qwen3 Coder 30B A3B Instruct for coding: it scores 60.4 on the Vibe Coding Index against 22.9 for Qwen3.5-122B-A10B. Pick Qwen3.5-122B-A10B when what matters is Terminal-Bench Hard (Artificial Analysis run) or AA Intelligence Index, where it leads.
Which is cheaper, Qwen3.5-122B-A10B or Qwen3 Coder 30B A3B Instruct?
Qwen3.5-122B-A10B costs $0.26 per million input tokens and $2.08 per million output tokens; Qwen3 Coder 30B A3B Instruct costs $0.07 and $0.28. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen3.5-122B-A10B or Qwen3 Coder 30B A3B Instruct?
Neither: both take 262K tokens.
How many benchmarks is the Qwen3.5-122B-A10B vs Qwen3 Coder 30B A3B Instruct comparison based on?
7 benchmarks have a result for both models as of October 21, 2026. Qwen3.5-122B-A10B's coding index rests on 1 independent benchmark and Qwen3 Coder 30B A3B Instruct's on 1. Where only one model has a result, the page says so instead of naming a winner.