Qwen3.5-Flash vs Qwen3.6 27B
Qwen3.5-Flash leads Qwen3.6 27B on coding, 41.1 to 36.8 on the Vibe Coding Index, and Qwen3.5-Flash costs 10.4x less per output token ($0.26 vs $2.70 per million). Updated October 22, 2026; 13 benchmarks have a result for both.
Qwen3.5-Flash or Qwen3.6 27B: which to pick
Pick Qwen3.5-Flash for coding: it scores 41.1 on the Vibe Coding Index against 36.8 for Qwen3.6 27B.
Pick Qwen3.6 27B for work measured by SWE-bench Verified (Vals AI) or Epoch Capabilities Index, where it leads.
- Shared results
- 13
- Qwen3.5-Flash only
- 42
- Qwen3.6 27B only
- 12
- 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
Qwen3.5-Flash
Qwen3.5-Flash scores 41.1 against 36.8.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
No call
Only Qwen3.6 27B has a AA Intelligence Index result, 21.4.
Directional only
Agentic work
AA Agentic Index
No call
Only Qwen3.6 27B has a AA Agentic Index result, 18.5.
Directional only
Reasoning
GPQA Diamond
No call
Only Qwen3.5-Flash has a GPQA Diamond result, 82.8%.
Directional only
Math
AIME
No call
Only Qwen3.5-Flash has a AIME result, 92.5%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.5-Flash has a MMLU Pro result, 84.1%.
Directional only
Long documents
Listed context window
Qwen3.5-Flash
Qwen3.5-Flash takes 1M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-Flash
Qwen3.5-Flash $0.0002, Qwen3.6 27B $0.0018 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Qwen3.5-Flash
Qwen3.5-Flash $0.40, Qwen3.6 27B $1.30.
Published attempt cost and pass rate
At a glance
- Coding index
- Qwen3.5-Flash 41.1 from 2 benchmarks, Qwen3.6 27B 36.8 from 2 benchmarks.
- Widest category
- Math, 11.8 points apart on average over 2 shared benchmarks.
- Widest single gap
- FrontierMath, tiers 1 to 3: Qwen3.5-Flash 18.2% against Qwen3.6 27B 35.1%, 16.8 points apart on a 0 to 100 scale.
- Output price
- Qwen3.5-Flash $0.26 against Qwen3.6 27B $2.70 per million output tokens, 10.4x apart.
- Context window
- Qwen3.5-Flash takes 1M tokens, Qwen3.6 27B 262K.
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-Flash | Bars | Qwen3.6 27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 1 | 1 | ||
| Math | 0 | 2 | ||
| Knowledge | 3 | 1 | ||
| General | 1 | 2 | ||
| All shared boards | 5 | 8 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Qwen3.5-Flash | Qwen3.6 27B | Leader |
|---|---|---|---|
| LMArena WebDev | 1244 | Not measured | Only Qwen3.5-Flash measured |
| AA Coding Index | Not measured | 53.7 | Only Qwen3.6 27B measured |
| LiveCodeBench (Vals AI) | 83.3% | Not measured | Only Qwen3.5-Flash measured |
| SWE-bench Verified (Vals AI) | 64.4% | 70.0% | Qwen3.6 27B |
| Vibe Code Bench (Vals AI) | Not measured | 11.9% | Only Qwen3.6 27B measured |
| SciCode | Not measured | 42.8% | Only Qwen3.6 27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 34.8% | Only Qwen3.6 27B measured |
| ALE-Bench | 265.9 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: coding | 1412 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: software and IT | 1416 | Not measured | Only Qwen3.5-Flash measured |
| LMArena WebDev: React | 1229 | Not measured | Only Qwen3.5-Flash measured |
| LMArena WebDev: HTML | 1291 | Not measured | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen3.5-Flash | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 21.4 | Only Qwen3.6 27B measured |
| Epoch Capabilities Index | 143.5 | 146.2 | Qwen3.6 27B |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Qwen3.5-Flash | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 18.5 | Only Qwen3.6 27B measured |
| τ²-Bench Telecom | Not measured | 94.2% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | Qwen3.5-Flash | Qwen3.6 27B | Leader |
|---|---|---|---|
| GPQA Diamond | 82.8% | Not measured | Only Qwen3.5-Flash measured |
| MMMU Pro (Vals AI) | 81.9% | Not measured | Only Qwen3.5-Flash measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 84.2% | Only Qwen3.6 27B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 23.1% | Only Qwen3.6 27B measured |
| GPQA Diamond (Epoch AI run) | 82.3% | 85.9% | Qwen3.6 27B |
| Chess puzzles | 14.0% | Not measured | Only Qwen3.5-Flash measured |
| Mystery game puzzles | 20.0% | 7.0% | Qwen3.5-Flash |
| LMArena Text: hard prompts | 1403 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: expert | 1407 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: hard prompts in English | 1409 | Not measured | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Qwen3.5-Flash | Qwen3.6 27B | Leader |
|---|---|---|---|
| AIME | 92.5% | Not measured | Only Qwen3.5-Flash measured |
| OTIS Mock AIME | 84.4% | 91.1% | Qwen3.6 27B |
| FrontierMath, tiers 1 to 3 | 18.2% | 35.1% | Qwen3.6 27B |
| LMArena Text: math | 1407 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: mathematical work | 1413 | Not measured | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | Qwen3.5-Flash | Qwen3.6 27B | Leader |
|---|---|---|---|
| MMLU Pro | 84.1% | Not measured | Only Qwen3.5-Flash measured |
| LegalBench (Vals AI) | 84.3% | Not measured | Only Qwen3.5-Flash measured |
| CaseLaw (Vals AI) | 56.0% | 53.2% | Qwen3.5-Flash |
| CorpFin (Vals AI) | 63.6% | 62.3% | Qwen3.5-Flash |
| TaxEval (Vals AI) | 72.2% | 71.3% | Qwen3.5-Flash |
| MortgageTax (Vals AI) | 67.4% | 68.3% | Qwen3.6 27B |
| MedCode (Vals AI) | 33.0% | Not measured | Only Qwen3.5-Flash measured |
| SimpleQA Verified | 20.3% | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: science | 1415 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: medicine and healthcare | 1407 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: legal and government | 1398 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: business and finance | 1394 | Not measured | Only Qwen3.5-Flash measured |
| Neither model has a result on the other 8 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3.5-Flash | Qwen3.6 27B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | Qwen3.5-Flash | Qwen3.6 27B | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 42.5% | 45.6% | Qwen3.6 27B |
| MedScribe (Vals AI) | 70.6% | Not measured | Only Qwen3.5-Flash measured |
| IFBench | Not measured | 67.6% | Only Qwen3.6 27B measured |
| Long context reasoning (AA-LCR) | Not measured | 77.3% | Only Qwen3.6 27B measured |
| DTBench | 82.9% | 78.1% | Qwen3.5-Flash |
| LMCA | 29.1% | 34.5% | Qwen3.6 27B |
| LMArena Text | 1397 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: instruction following | 1374 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: creative writing | 1343 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: English | 1404 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: non-English | 1385 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: Chinese | 1445 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: German | 1390 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: Japanese | 1369 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: Korean | 1344 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: French | 1413 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: Spanish | 1400 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: Russian | 1380 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: Polish | 1376 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: multi-turn | 1393 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: longer queries | 1392 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: writing and language | 1369 | Not measured | Only Qwen3.5-Flash measured |
| LMArena Text: entertainment and media | 1347 | Not measured | Only Qwen3.5-Flash measured |
| EQ-Bench 4 | Not measured | 1026 | Only Qwen3.6 27B measured |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3.5-Flash | Qwen3.6 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
Qwen3.5-Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.5-Flash costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-Flash 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-Flash | Qwen3.6 27B |
|---|---|---|
| Maker | Qwen | Qwen |
| Weights | No public weights listed | Open weights |
| Released | February 25, 2026 | April 27, 2026 |
| Context window | 1M | 262K |
| Input $/Mtok | $0.065 | $0.45 |
| Output $/Mtok | $0.26 | $2.70 |
| Coding index | 41.1 | 36.8 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $0.40 | $1.30 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen3.5-Flash better than Qwen3.6 27B for coding?
Pick Qwen3.5-Flash for coding: it scores 41.1 on the Vibe Coding Index against 36.8 for Qwen3.6 27B. Pick Qwen3.6 27B for work measured by SWE-bench Verified (Vals AI) or Epoch Capabilities Index, where it leads.
Which is cheaper, Qwen3.5-Flash or Qwen3.6 27B?
Qwen3.5-Flash costs $0.065 per million input tokens and $0.26 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Per solved SWE-bench Verified task, Qwen3.5-Flash costs $0.40 and Qwen3.6 27B $1.30.
Which has the bigger context window, Qwen3.5-Flash or Qwen3.6 27B?
Qwen3.5-Flash, with 1M tokens against 262K.
How many benchmarks is the Qwen3.5-Flash vs Qwen3.6 27B comparison based on?
13 benchmarks have a result for both models as of October 22, 2026. Qwen3.5-Flash's coding index rests on 2 independent benchmarks and Qwen3.6 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.