Qwen3 235B A22B Instruct 2507 vs Qwen3.8 27B
Qwen3.8 27B leads Qwen3 235B A22B Instruct 2507 on the Artificial Analysis Intelligence Index, 33.7 to 12.0, and Qwen3 235B A22B Instruct 2507 costs 3.3x less per output token ($0.775 vs $2.55 per million). Updated October 23, 2026; 9 benchmarks have a result for both.
Qwen3 235B A22B Instruct 2507 or Qwen3.8 27B: which to pick
Only Qwen3.8 27B has a coding score, 74.1 on the Vibe Coding Index; Qwen3 235B A22B Instruct 2507 has none, so the two cannot be ranked on coding.
Pick Qwen3 235B A22B Instruct 2507 for output tokens 3.3x cheaper ($0.775 vs $2.55 per million).
- Shared results
- 9
- Qwen3 235B A22B Instruct 2507 only
- 15
- Qwen3.8 27B only
- 76
- Like-for-like categories
- 4 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
Only Qwen3.8 27B has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.8 27B
Qwen3.8 27B leads, 33.7 against 12.0.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.8 27B has a AA Agentic Index result, 45.8.
Directional only
Reasoning
GPQA Diamond
No call
Only Qwen3.8 27B has a GPQA Diamond result, 88.9%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.8 27B has a MMLU Pro result, 84.3%.
Directional only
Web design
Design Arena: websites
No call
Only Qwen3 235B A22B Instruct 2507 has a Design Arena: websites result, 1058.
Directional only
Long documents
Listed context window
Qwen3.8 27B
Qwen3.8 27B takes 1M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 235B A22B Instruct 2507
Qwen3 235B A22B Instruct 2507 $0.0005, Qwen3.8 27B $0.0017 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.8 27B has the number, $1.26.
Directional only
At a glance
- Coding index
- Qwen3 235B A22B Instruct 2507 not benchmarked, Qwen3.8 27B 74.1 from 4 benchmarks.
- Widest category
- Reasoning, 36.8 points apart on average over 4 shared benchmarks.
- Widest single gap
- ARC-AGI: Qwen3 235B A22B Instruct 2507 11.0% against Qwen3.8 27B 87.5%, 76.5 points apart on a 0 to 100 scale.
- Output price
- Qwen3 235B A22B Instruct 2507 $0.775 against Qwen3.8 27B $2.55 per million output tokens, 3.3x apart.
- Context window
- Qwen3 235B A22B Instruct 2507 takes 262K tokens, Qwen3.8 27B 1M.
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 | Qwen3.8 27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 4 | ||
| General | 0 | 3 | ||
| All shared boards | 0 | 9 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Qwen3 235B A22B Instruct 2507 | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 86.8% | Only Qwen3.8 27B measured |
| LMArena WebDev | Not measured | 1591 | Only Qwen3.8 27B measured |
| AA Coding Index | Not measured | 68.1 | Only Qwen3.8 27B measured |
| LiveCodeBench (Vals AI) | Not measured | 84.0% | Only Qwen3.8 27B measured |
| SWE-bench Verified (Vals AI) | Not measured | 86.0% | Only Qwen3.8 27B measured |
| Vibe Code Bench (Vals AI) | Not measured | 64.8% | Only Qwen3.8 27B measured |
| Code migration (Vals AI) | Not measured | 14.2% | Only Qwen3.8 27B measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Qwen3.8 27B measured |
| IOI (Vals AI) | Not measured | 39.1% | Only Qwen3.8 27B measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 58.4% | Only Qwen3.8 27B measured |
| SkillsBench (Vals AI) | Not measured | 38.1% | Only Qwen3.8 27B measured |
| CyberBench (Vals AI) | Not measured | 67.8% | Only Qwen3.8 27B measured |
| LiveCodeBench (Artificial Analysis run) | 78.8% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| SciCode | 41.4% | 46.6% | Qwen3.8 27B |
| Terminal-Bench Hard (Artificial Analysis run) | 15.2% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| WeirdML | 38.7% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| LMArena Text: coding | Not measured | 1483 | Only Qwen3.8 27B measured |
| LMArena Text: software and IT | Not measured | 1477 | Only Qwen3.8 27B measured |
| LMArena WebDev: React | Not measured | 1600 | Only Qwen3.8 27B measured |
| LMArena WebDev: HTML | Not measured | 1569 | Only Qwen3.8 27B measured |
| LMArena WebDev: from an image | Not measured | 1577 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3 235B A22B Instruct 2507 | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.0 | 33.7 | Qwen3.8 27B |
| Epoch Capabilities Index | Not measured | 151.7 | Only Qwen3.8 27B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | Qwen3 235B A22B Instruct 2507 | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 45.8 | Only Qwen3.8 27B measured |
| LiveBench research | Not measured | 59.4% | Only Qwen3.8 27B measured |
| LiveBench browser | Not measured | 32.0% | Only Qwen3.8 27B measured |
| LiveBench long agentic | Not measured | 27.9% | Only Qwen3.8 27B measured |
| Finance Agent (Vals AI) | Not measured | 48.5% | Only Qwen3.8 27B measured |
| Tax Agent Bench (Vals AI) | Not measured | 30.9% | Only Qwen3.8 27B measured |
| Legal Research Bench (Vals AI) | Not measured | 36.1% | Only Qwen3.8 27B measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 59.7% | Only Qwen3.8 27B measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 0.0% | Only Qwen3.8 27B measured |
| τ²-Bench Telecom | 53.2% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| APEX-Agents | Not measured | 47.5% | Only Qwen3.8 27B 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 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Qwen3 235B A22B Instruct 2507 | Qwen3.8 27B | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 88.9% | Only Qwen3.8 27B measured |
| LiveBench reasoning | Not measured | 80.0% | Only Qwen3.8 27B measured |
| MMMU Pro (Vals AI) | Not measured | 83.9% | Only Qwen3.8 27B measured |
| GPQA Diamond (Artificial Analysis run) | 79.0% | 90.5% | Qwen3.8 27B |
| Humanity's Last Exam (Artificial Analysis run) | 15.9% | 33.9% | Qwen3.8 27B |
| ARC-AGI | 11.0% | 87.5% | Qwen3.8 27B |
| ARC-AGI-2 | 1.3% | 42.4% | Qwen3.8 27B |
| LMArena Text: hard prompts | Not measured | 1459 | Only Qwen3.8 27B measured |
| LMArena Text: expert | Not measured | 1480 | Only Qwen3.8 27B measured |
| LMArena Text: hard prompts in English | Not measured | 1462 | Only Qwen3.8 27B measured |
| LMArena Vision: diagrams | Not measured | 1285 | Only Qwen3.8 27B measured |
| LMArena Vision: homework | Not measured | 1295 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 11 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Qwen3 235B A22B Instruct 2507 | Qwen3.8 27B | 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 |
| ProofBench | Not measured | 16.0% | Only Qwen3.8 27B measured |
| LMArena Text: math | Not measured | 1455 | Only Qwen3.8 27B measured |
| LMArena Text: mathematical work | Not measured | 1458 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | Qwen3 235B A22B Instruct 2507 | Qwen3.8 27B | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 84.3% | Only Qwen3.8 27B measured |
| LegalBench (Vals AI) | Not measured | 82.4% | Only Qwen3.8 27B measured |
| TaxEval (Vals AI) | Not measured | 70.8% | Only Qwen3.8 27B measured |
| MortgageTax (Vals AI) | Not measured | 64.9% | Only Qwen3.8 27B measured |
| MedCode (Vals AI) | Not measured | 28.7% | Only Qwen3.8 27B measured |
| MMLU Pro (Artificial Analysis run) | 84.3% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| LMArena Text: science | Not measured | 1468 | Only Qwen3.8 27B measured |
| LMArena Text: medicine and healthcare | Not measured | 1438 | Only Qwen3.8 27B measured |
| LMArena Text: legal and government | Not measured | 1447 | Only Qwen3.8 27B measured |
| LMArena Text: business and finance | Not measured | 1438 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 10 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | Qwen3 235B A22B Instruct 2507 | Qwen3.8 27B | Leader |
|---|---|---|---|
| Design Arena: websites | 1058 | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: UI components | 963 | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: game dev | 951 | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: data viz | 1070 | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: 3D | 995 | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: all code categories | 1038 | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| LiveBench design | Not measured | 75.9% | Only Qwen3.8 27B measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Qwen3 235B A22B Instruct 2507 | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 76.6% | Only Qwen3.8 27B measured |
| SAGE (Vals AI) | Not measured | 52.4% | Only Qwen3.8 27B measured |
| MedScribe (Vals AI) | Not measured | 83.8% | Only Qwen3.8 27B measured |
| IFBench | 51.2% | Not measured | Only Qwen3 235B A22B Instruct 2507 measured |
| Long context reasoning (AA-LCR) | 72.0% | 82.0% | Qwen3.8 27B |
| DTBench | 78.4% | 88.0% | Qwen3.8 27B |
| LMCA | 23.5% | 41.4% | Qwen3.8 27B |
| Surface Evolver Bench | Not measured | 45.0% | Only Qwen3.8 27B measured |
| LMArena Text | Not measured | 1440 | Only Qwen3.8 27B measured |
| LMArena Text: instruction following | Not measured | 1436 | Only Qwen3.8 27B measured |
| LMArena Text: creative writing | Not measured | 1383 | Only Qwen3.8 27B measured |
| LMArena Vision | Not measured | 1269 | Only Qwen3.8 27B measured |
| LMArena Text: English | Not measured | 1448 | Only Qwen3.8 27B measured |
| LMArena Text: non-English | Not measured | 1428 | Only Qwen3.8 27B measured |
| LMArena Text: Chinese | Not measured | 1501 | Only Qwen3.8 27B measured |
| LMArena Text: German | Not measured | 1442 | Only Qwen3.8 27B measured |
| LMArena Text: Japanese | Not measured | 1388 | Only Qwen3.8 27B measured |
| LMArena Text: Korean | Not measured | 1380 | Only Qwen3.8 27B measured |
| LMArena Text: French | Not measured | 1464 | Only Qwen3.8 27B measured |
| LMArena Text: Spanish | Not measured | 1448 | Only Qwen3.8 27B measured |
| LMArena Text: Russian | Not measured | 1414 | Only Qwen3.8 27B measured |
| LMArena Text: Polish | Not measured | 1447 | Only Qwen3.8 27B measured |
| LMArena Text: multi-turn | Not measured | 1440 | Only Qwen3.8 27B measured |
| LMArena Text: longer queries | Not measured | 1449 | Only Qwen3.8 27B measured |
| LMArena Text: writing and language | Not measured | 1405 | Only Qwen3.8 27B measured |
| LMArena Text: entertainment and media | Not measured | 1393 | Only Qwen3.8 27B measured |
| LMArena Vision: English | Not measured | 1267 | Only Qwen3.8 27B measured |
| LMArena Vision: Chinese | Not measured | 1326 | Only Qwen3.8 27B measured |
| LMArena Vision: reading text | Not measured | 1281 | Only Qwen3.8 27B measured |
| LMArena Vision: humor | Not measured | 1235 | Only Qwen3.8 27B measured |
| LMArena Vision: creative writing | Not measured | 1237 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3 235B A22B Instruct 2507 | Qwen3.8 27B | 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 | Qwen3.8 27B |
|---|---|---|
| Maker | Qwen | Qwen |
| Weights | Open weights | Open weights |
| Released | July 21, 2025 | August 14, 2026 |
| Context window | 262K | 1M |
| Input $/Mtok | $0.145 | $0.425 |
| Output $/Mtok | $0.775 | $2.55 |
| Coding index | Not benchmarked | 74.1 |
| Independent measurements behind it | 0 | 4 |
| Cost per solved task | Not computable | $1.26 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen3 235B A22B Instruct 2507 better than Qwen3.8 27B for coding?
Only Qwen3.8 27B has a coding score, 74.1 on the Vibe Coding Index; Qwen3 235B A22B Instruct 2507 has none, so the two cannot be ranked on coding. Pick Qwen3 235B A22B Instruct 2507 for output tokens 3.3x cheaper ($0.775 vs $2.55 per million).
Which is cheaper, Qwen3 235B A22B Instruct 2507 or Qwen3.8 27B?
Qwen3 235B A22B Instruct 2507 costs $0.145 per million input tokens and $0.775 per million output tokens; Qwen3.8 27B costs $0.425 and $2.55. Cost per solved task is known for Qwen3.8 27B only.
Which has the bigger context window, Qwen3 235B A22B Instruct 2507 or Qwen3.8 27B?
Qwen3.8 27B, with 1M tokens against 262K.
How many benchmarks is the Qwen3 235B A22B Instruct 2507 vs Qwen3.8 27B comparison based on?
9 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 Qwen3.8 27B's on 4. Where only one model has a result, the page says so instead of naming a winner.