Qwen3 235B A22B Instruct 2507 vs Qwen3.5 397B A17B
Qwen3.5 397B A17B leads Qwen3 235B A22B Instruct 2507 on the Artificial Analysis Intelligence Index, 21.4 to 12.0, and Qwen3 235B A22B Instruct 2507 costs 3.0x less per output token ($0.775 vs $2.34 per million). Updated October 23, 2026; 16 benchmarks have a result for both.
Qwen3 235B A22B Instruct 2507 or Qwen3.5 397B A17B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Qwen3 235B A22B Instruct 2507 for output tokens 3.0x cheaper ($0.775 vs $2.34 per million) and work measured by SciCode or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 16
- Qwen3 235B A22B Instruct 2507 only
- 8
- Qwen3.5 397B A17B only
- 50
- 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 397B A17B
Qwen3.5 397B A17B leads, 21.4 against 12.0.
Same board, both measured
Web design
Design Arena: websites
Qwen3.5 397B A17B
Qwen3.5 397B A17B leads, 1190 against 1058.
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 235B A22B Instruct 2507
Qwen3 235B A22B Instruct 2507 $0.0005, Qwen3.5 397B A17B $0.0016 per turn.
List prices
At a glance
- Coding index
- Qwen3 235B A22B Instruct 2507 not benchmarked, Qwen3.5 397B A17B not benchmarked.
- Widest category
- Agentic work, 42.4 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Qwen3 235B A22B Instruct 2507 53.2% against Qwen3.5 397B A17B 95.6%, 42.4 points apart on a 0 to 100 scale.
- Output price
- Qwen3 235B A22B Instruct 2507 $0.775 against Qwen3.5 397B A17B $2.34 per million output tokens, 3.0x 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 235B A22B Instruct 2507 | Bars | Qwen3.5 397B A17B | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Web design | 0 | 6 | ||
| General | 0 | 4 | ||
| All shared boards | 0 | 16 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Qwen3 235B A22B Instruct 2507 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1400 | Only Qwen3.5 397B A17B measured |
| LiveCodeBench (Artificial Analysis run) | 78.8% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| SciCode | 41.4% | 44.8% | Qwen3.5 397B A17B |
| Terminal-Bench Hard (Artificial Analysis run) | 15.2% | 40.9% | Qwen3.5 397B A17B |
| WeirdML | 38.7% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| ALE-Bench | Not measured | 621.9 | Only Qwen3.5 397B A17B measured |
| LMArena Text: coding | Not measured | 1465 | Only Qwen3.5 397B A17B measured |
| LMArena Text: software and IT | Not measured | 1463 | Only Qwen3.5 397B A17B measured |
| LMArena WebDev: React | Not measured | 1388 | Only Qwen3.5 397B A17B measured |
| LMArena WebDev: HTML | Not measured | 1409 | Only Qwen3.5 397B A17B 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 235B A22B Instruct 2507 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.0 | 21.4 | Qwen3.5 397B A17B |
| Epoch Capabilities Index | Not measured | 146.8 | Only Qwen3.5 397B A17B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3 235B A22B Instruct 2507 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 53.2% | 95.6% | Qwen3.5 397B A17B |
| APEX-Agents | Not measured | 24.9% | Only Qwen3.5 397B A17B measured |
| Berkeley Function Calling Leaderboard (BFCL) | 52.1% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Qwen3 235B A22B Instruct 2507 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.0% | 89.3% | Qwen3.5 397B A17B |
| Humanity's Last Exam (Artificial Analysis run) | 15.9% | 29.0% | Qwen3.5 397B A17B |
| GPQA Diamond (Epoch AI run) | Not measured | 86.4% | Only Qwen3.5 397B A17B measured |
| ARC-AGI | 11.0% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| ARC-AGI-2 | 1.3% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Mystery game puzzles | Not measured | 18.0% | Only Qwen3.5 397B A17B measured |
| LMArena Text: hard prompts | Not measured | 1448 | Only Qwen3.5 397B A17B measured |
| LMArena Text: expert | Not measured | 1462 | Only Qwen3.5 397B A17B measured |
| LMArena Text: hard prompts in English | Not measured | 1450 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: diagrams | Not measured | 1277 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: homework | Not measured | 1292 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Qwen3 235B A22B Instruct 2507 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| AA Math Index | 91.0 | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| AIME 2025 (Artificial Analysis run) | 91.0% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| OTIS Mock AIME | Not measured | 88.9% | Only Qwen3.5 397B A17B measured |
| FrontierMath, tiers 1 to 3 | Not measured | 31.2% | Only Qwen3.5 397B A17B measured |
| LMArena Text: math | Not measured | 1454 | Only Qwen3.5 397B A17B measured |
| LMArena Text: mathematical work | Not measured | 1454 | Only Qwen3.5 397B A17B measured |
| MathArena | Not measured | 19.2% | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Qwen3 235B A22B Instruct 2507 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 84.3% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| LMArena Text: science | Not measured | 1457 | Only Qwen3.5 397B A17B measured |
| LMArena Text: medicine and healthcare | Not measured | 1455 | Only Qwen3.5 397B A17B measured |
| LMArena Text: legal and government | Not measured | 1441 | Only Qwen3.5 397B A17B measured |
| LMArena Text: business and finance | Not measured | 1435 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 15 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | Qwen3 235B A22B Instruct 2507 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| Design Arena: websites | 1058 | 1190 | Qwen3.5 397B A17B |
| Design Arena: UI components | 963 | 1161 | Qwen3.5 397B A17B |
| Design Arena: game dev | 951 | 1140 | Qwen3.5 397B A17B |
| Design Arena: data viz | 1070 | 1175 | Qwen3.5 397B A17B |
| Design Arena: 3D | 995 | 1162 | Qwen3.5 397B A17B |
| Design Arena: all code categories | 1038 | 1179 | Qwen3.5 397B A17B |
| Design Arena: SVG | Not measured | 1139 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Qwen3 235B A22B Instruct 2507 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| IFBench | 51.2% | 78.8% | Qwen3.5 397B A17B |
| Long context reasoning (AA-LCR) | 72.0% | 77.3% | Qwen3.5 397B A17B |
| DTBench | 78.4% | 87.5% | Qwen3.5 397B A17B |
| LMCA | 23.5% | 37.9% | Qwen3.5 397B A17B |
| LMArena Text | Not measured | 1438 | Only Qwen3.5 397B A17B measured |
| LMArena Text: instruction following | Not measured | 1423 | Only Qwen3.5 397B A17B measured |
| LMArena Text: creative writing | Not measured | 1402 | Only Qwen3.5 397B A17B measured |
| LMArena Vision | Not measured | 1262 | Only Qwen3.5 397B A17B measured |
| LMArena Text: English | Not measured | 1443 | Only Qwen3.5 397B A17B measured |
| LMArena Text: non-English | Not measured | 1430 | Only Qwen3.5 397B A17B measured |
| LMArena Text: Chinese | Not measured | 1499 | Only Qwen3.5 397B A17B measured |
| LMArena Text: German | Not measured | 1444 | Only Qwen3.5 397B A17B measured |
| LMArena Text: Japanese | Not measured | 1430 | Only Qwen3.5 397B A17B measured |
| LMArena Text: Korean | Not measured | 1385 | Only Qwen3.5 397B A17B measured |
| LMArena Text: French | Not measured | 1462 | Only Qwen3.5 397B A17B measured |
| LMArena Text: Spanish | Not measured | 1441 | Only Qwen3.5 397B A17B measured |
| LMArena Text: Russian | Not measured | 1430 | Only Qwen3.5 397B A17B measured |
| LMArena Text: Polish | Not measured | 1448 | Only Qwen3.5 397B A17B measured |
| LMArena Text: multi-turn | Not measured | 1447 | Only Qwen3.5 397B A17B measured |
| LMArena Text: longer queries | Not measured | 1441 | Only Qwen3.5 397B A17B measured |
| LMArena Text: writing and language | Not measured | 1416 | Only Qwen3.5 397B A17B measured |
| LMArena Text: entertainment and media | Not measured | 1399 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: English | Not measured | 1268 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: Chinese | Not measured | 1314 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: reading text | Not measured | 1271 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: humor | Not measured | 1262 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: captioning | Not measured | 1230 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: recognizing things | Not measured | 1265 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: creative writing | Not measured | 1260 | Only Qwen3.5 397B A17B 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 235B A22B Instruct 2507 | Qwen3.5 397B A17B | 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 235B A22B Instruct 2507 costs less
Repository review
50K input + 3K output tokens
Qwen3 235B A22B Instruct 2507 costs less
Agent loop
200K input + 10K output tokens
Qwen3 235B A22B 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 235B A22B Instruct 2507 | Qwen3.5 397B A17B |
|---|---|---|
| Maker | Qwen | Qwen |
| Weights | Open weights | Open weights |
| Released | July 21, 2025 | February 16, 2026 |
| Context window | 262K | 262K |
| Input $/Mtok | $0.145 | $0.39 |
| Output $/Mtok | $0.775 | $2.34 |
| 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 235B A22B Instruct 2507 better than Qwen3.5 397B A17B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Qwen3 235B A22B Instruct 2507 for output tokens 3.0x cheaper ($0.775 vs $2.34 per million) and work measured by SciCode or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Qwen3 235B A22B Instruct 2507 or Qwen3.5 397B A17B?
Qwen3 235B A22B Instruct 2507 costs $0.145 per million input tokens and $0.775 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for neither of them.
Which has the bigger context window, Qwen3 235B A22B Instruct 2507 or Qwen3.5 397B A17B?
Neither: both take 262K tokens.
How many benchmarks is the Qwen3 235B A22B Instruct 2507 vs Qwen3.5 397B A17B comparison based on?
16 benchmarks have a result for both models as of October 23, 2026. Qwen3 235B A22B Instruct 2507's coding index rests on 0 independent benchmarks and Qwen3.5 397B A17B's on 1. Where only one model has a result, the page says so instead of naming a winner.