Qwen3 30B A3B Instruct 2507 vs Qwen3.5-122B-A10B
Qwen3.5-122B-A10B leads Qwen3 30B A3B Instruct 2507 on the Artificial Analysis Intelligence Index, 17.7 to 7.5, and Qwen3 30B A3B Instruct 2507 costs 6.9x less per output token ($0.30 vs $2.08 per million). Updated October 22, 2026; 38 benchmarks have a result for both.
Qwen3 30B A3B Instruct 2507 or Qwen3.5-122B-A10B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Qwen3 30B A3B Instruct 2507 for output tokens 6.9x cheaper ($0.30 vs $2.08 per million) and work measured by SciCode or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 38
- Qwen3 30B A3B Instruct 2507 only
- 7
- Qwen3.5-122B-A10B only
- 13
- Like-for-like categories
- 7 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 7.5.
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 30B A3B Instruct 2507
Qwen3 30B A3B Instruct 2507 $0.0002, Qwen3.5-122B-A10B $0.0013 per turn.
List prices
At a glance
- Coding index
- Qwen3 30B A3B Instruct 2507 not benchmarked, Qwen3.5-122B-A10B not benchmarked.
- Widest category
- Agentic work, 65.5 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Qwen3 30B A3B Instruct 2507 28.1% against Qwen3.5-122B-A10B 93.6%, 65.5 points apart on a 0 to 100 scale.
- Output price
- Qwen3 30B A3B Instruct 2507 $0.30 against Qwen3.5-122B-A10B $2.08 per million output tokens, 6.9x 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 30B A3B Instruct 2507 | Bars | Qwen3.5-122B-A10B | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 21 | ||
| All shared boards | 0 | 38 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | Qwen3 30B A3B Instruct 2507 | 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 (Artificial Analysis run) | 70.7% | Not measured | Only Qwen3 30B A3B Instruct 2507 measured |
| SciCode | 33.0% | 39.7% | Qwen3.5-122B-A10B |
| Terminal-Bench Hard (Artificial Analysis run) | 6.1% | 31.1% | Qwen3.5-122B-A10B |
| LMArena Text: coding | 1416 | 1436 | Qwen3.5-122B-A10B |
| LMArena Text: software and IT | 1417 | 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 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3 30B A3B Instruct 2507 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.5 | 17.7 | 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 index01
| Benchmark | Qwen3 30B A3B Instruct 2507 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 28.1% | 93.6% | Qwen3.5-122B-A10B |
| Berkeley Function Calling Leaderboard (BFCL) | 41.4% | Not measured | Only Qwen3 30B A3B Instruct 2507 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 | Qwen3 30B A3B Instruct 2507 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 70.7% | 85.7% | Qwen3.5-122B-A10B |
| Humanity's Last Exam (Artificial Analysis run) | 10.3% | 25.2% | Qwen3.5-122B-A10B |
| GPQA Diamond (Epoch AI run) | 55.6% | Not measured | Only Qwen3 30B A3B Instruct 2507 measured |
| Mystery game puzzles | Not measured | 17.0% | Only Qwen3.5-122B-A10B measured |
| LMArena Text: hard prompts | 1398 | 1421 | Qwen3.5-122B-A10B |
| LMArena Text: expert | 1397 | 1431 | Qwen3.5-122B-A10B |
| LMArena Text: hard prompts in English | 1406 | 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 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Qwen3 30B A3B Instruct 2507 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| AA Math Index | 66.3 | Not measured | Only Qwen3 30B A3B Instruct 2507 measured |
| AIME 2025 (Artificial Analysis run) | 66.3% | Not measured | Only Qwen3 30B A3B Instruct 2507 measured |
| OTIS Mock AIME | 62.2% | Not measured | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: math | 1394 | 1431 | Qwen3.5-122B-A10B |
| LMArena Text: mathematical work | 1411 | 1431 | Qwen3.5-122B-A10B |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Qwen3 30B A3B Instruct 2507 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 80.5% | Not measured | Only Qwen3 30B A3B Instruct 2507 measured |
| LMArena Text: science | 1393 | 1432 | Qwen3.5-122B-A10B |
| LMArena Text: medicine and healthcare | 1392 | 1423 | Qwen3.5-122B-A10B |
| LMArena Text: legal and government | 1387 | 1422 | Qwen3.5-122B-A10B |
| LMArena Text: business and finance | 1389 | 1414 | Qwen3.5-122B-A10B |
| Neither model has a result on the other 15 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3 30B A3B Instruct 2507 | 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 index021
| Benchmark | Qwen3 30B A3B Instruct 2507 | Qwen3.5-122B-A10B | Leader |
|---|---|---|---|
| IFBench | 50.7% | 75.7% | Qwen3.5-122B-A10B |
| Long context reasoning (AA-LCR) | 61.3% | 76.3% | Qwen3.5-122B-A10B |
| DTBench | 67.2% | 84.3% | Qwen3.5-122B-A10B |
| LMCA | 22.4% | 32.2% | Qwen3.5-122B-A10B |
| LMArena Text | 1384 | 1417 | Qwen3.5-122B-A10B |
| LMArena Text: instruction following | 1363 | 1399 | Qwen3.5-122B-A10B |
| LMArena Text: creative writing | 1317 | 1368 | Qwen3.5-122B-A10B |
| LMArena Vision | Not measured | 1244 | Only Qwen3.5-122B-A10B measured |
| LMArena Text: English | 1391 | 1428 | Qwen3.5-122B-A10B |
| LMArena Text: non-English | 1372 | 1401 | Qwen3.5-122B-A10B |
| LMArena Text: Chinese | 1432 | 1463 | Qwen3.5-122B-A10B |
| LMArena Text: German | 1379 | 1425 | Qwen3.5-122B-A10B |
| LMArena Text: Japanese | 1337 | 1367 | Qwen3.5-122B-A10B |
| LMArena Text: Korean | 1330 | 1353 | Qwen3.5-122B-A10B |
| LMArena Text: French | 1419 | 1443 | Qwen3.5-122B-A10B |
| LMArena Text: Spanish | 1404 | 1425 | Qwen3.5-122B-A10B |
| LMArena Text: Russian | 1369 | 1400 | Qwen3.5-122B-A10B |
| LMArena Text: Polish | 1353 | 1398 | Qwen3.5-122B-A10B |
| LMArena Text: multi-turn | 1378 | 1416 | Qwen3.5-122B-A10B |
| LMArena Text: longer queries | 1378 | 1410 | Qwen3.5-122B-A10B |
| LMArena Text: writing and language | 1336 | 1387 | Qwen3.5-122B-A10B |
| LMArena Text: entertainment and media | 1334 | 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 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3 30B A3B Instruct 2507 | 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 30B A3B Instruct 2507 costs less
Repository review
50K input + 3K output tokens
Qwen3 30B A3B Instruct 2507 costs less
Agent loop
200K input + 10K output tokens
Qwen3 30B A3B Instruct 2507 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 30B A3B Instruct 2507 | Qwen3.5-122B-A10B |
|---|---|---|
| Maker | Qwen | Qwen |
| Weights | Open weights | Open weights |
| Released | July 29, 2025 | February 25, 2026 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.09 | $0.26 |
| Output $/Mtok | $0.30 | $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 Qwen3 30B A3B Instruct 2507 better than Qwen3.5-122B-A10B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Qwen3 30B A3B Instruct 2507 for output tokens 6.9x cheaper ($0.30 vs $2.08 per million) and work measured by SciCode or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Qwen3 30B A3B Instruct 2507 or Qwen3.5-122B-A10B?
Qwen3 30B A3B Instruct 2507 costs $0.09 per million input tokens and $0.30 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, Qwen3 30B A3B Instruct 2507 or Qwen3.5-122B-A10B?
Neither: both take 262K tokens.
How many benchmarks is the Qwen3 30B A3B Instruct 2507 vs Qwen3.5-122B-A10B comparison based on?
38 benchmarks have a result for both models as of October 22, 2026. Qwen3 30B A3B Instruct 2507'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.