Qwen3.5-27B vs Qwen3.5-Flash
Updated October 21, 2026. 34 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Qwen3.5-27B for coding: it scores 22.6 on the Vibe Coding Index against 6.2 for Qwen3.5-Flash.
Pick Qwen3.5-Flash when output tokens cost 8.3x less, or when the context window is larger, 1M against 262K tokens.
- Shared results
- 34
- Qwen3.5-27B only
- 19
- Qwen3.5-Flash only
- 21
- 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
Qwen3.5-27B
Qwen3.5-27B scores 22.6 against 6.2.
One benchmark behind each score
Intelligence
AA Intelligence Index
No call
Only Qwen3.5-27B has a AA Intelligence Index result, 22.9.
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
Qwen3.5-27B $0.0014, Qwen3.5-Flash $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.5-Flash has the number, $0.40.
Directional only
At a glance
- Coding index
- Qwen3.5-27B 22.6 from 1 benchmark, Qwen3.5-Flash 6.2 from 1 benchmark.
- Widest category
- Coding, 9.9 points apart on average over 6 shared benchmarks.
- Widest single gap
- LMArena WebDev: React: Qwen3.5-27B 1346 against Qwen3.5-Flash 1229, 19.5 points apart on a 0 to 100 scale.
- Output price
- Qwen3.5-27B $2.16 against Qwen3.5-Flash $0.26 per million output tokens, 8.3x apart.
- Context window
- Qwen3.5-27B takes 262K tokens, Qwen3.5-Flash 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 | Qwen3.5-27B | Bars | Qwen3.5-Flash | Ties |
|---|---|---|---|---|
| Coding | 6 | 0 | ||
| Reasoning | 3 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 15 | 4 | ||
| All shared boards | 30 | 4 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index60
| Benchmark | Qwen3.5-27B | Qwen3.5-Flash | Leader |
|---|---|---|---|
| LMArena WebDev | 1358 | 1244 | Qwen3.5-27B |
| LiveCodeBench (Vals AI) | Not measured | 83.3% | Only Qwen3.5-Flash measured |
| SWE-bench Verified (Vals AI) | Not measured | 64.4% | Only Qwen3.5-Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | 32.6% | Not measured | Only Qwen3.5-27B measured |
| WeirdML | 39.5% | Not measured | Only Qwen3.5-27B measured |
| ALE-Bench | 349.4 | 265.9 | Qwen3.5-27B |
| LMArena Text: coding | 1427 | 1412 | Qwen3.5-27B |
| LMArena Text: software and IT | 1431 | 1416 | Qwen3.5-27B |
| LMArena WebDev: React | 1346 | 1229 | Qwen3.5-27B |
| LMArena WebDev: HTML | 1386 | 1291 | Qwen3.5-27B |
| Neither model has a result on the other 28 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Qwen3.5-27B | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 22.9 | Not measured | Only Qwen3.5-27B measured |
| Epoch Capabilities Index | Not measured | 143.5 | Only Qwen3.5-Flash measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Qwen3.5-27B | Qwen3.5-Flash | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 93.9% | Not measured | Only Qwen3.5-27B measured |
| Vending-Bench 2 | $202 | Not measured | Only Qwen3.5-27B measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Qwen3.5-27B | Qwen3.5-Flash | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 82.8% | Only Qwen3.5-Flash measured |
| MMMU Pro (Vals AI) | Not measured | 81.9% | Only Qwen3.5-Flash measured |
| GPQA Diamond (Artificial Analysis run) | 85.8% | Not measured | Only Qwen3.5-27B measured |
| Humanity's Last Exam (Artificial Analysis run) | 23.9% | Not measured | Only Qwen3.5-27B measured |
| GPQA Diamond (Epoch AI run) | Not measured | 82.3% | Only Qwen3.5-Flash measured |
| Chess puzzles | Not measured | 14.0% | Only Qwen3.5-Flash measured |
| Mystery game puzzles | Not measured | 20.0% | Only Qwen3.5-Flash measured |
| LMArena Text: hard prompts | 1415 | 1403 | Qwen3.5-27B |
| LMArena Text: expert | 1427 | 1407 | Qwen3.5-27B |
| LMArena Text: hard prompts in English | 1429 | 1409 | Qwen3.5-27B |
| LMArena Vision: diagrams | 1249 | Not measured | Only Qwen3.5-27B measured |
| LMArena Vision: homework | 1256 | Not measured | Only Qwen3.5-27B measured |
| Neither model has a result on the other 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Qwen3.5-27B | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AIME | Not measured | 92.5% | Only Qwen3.5-Flash measured |
| OTIS Mock AIME | Not measured | 84.4% | Only Qwen3.5-Flash measured |
| FrontierMath, tiers 1 to 3 | Not measured | 18.2% | Only Qwen3.5-Flash measured |
| LMArena Text: math | 1429 | 1407 | Qwen3.5-27B |
| LMArena Text: mathematical work | 1429 | 1413 | Qwen3.5-27B |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Qwen3.5-27B | Qwen3.5-Flash | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 84.1% | Only Qwen3.5-Flash measured |
| LegalBench (Vals AI) | Not measured | 84.3% | Only Qwen3.5-Flash measured |
| CaseLaw (Vals AI) | Not measured | 56.0% | Only Qwen3.5-Flash measured |
| CorpFin (Vals AI) | Not measured | 63.6% | Only Qwen3.5-Flash measured |
| TaxEval (Vals AI) | Not measured | 72.2% | Only Qwen3.5-Flash measured |
| MortgageTax (Vals AI) | Not measured | 67.4% | Only Qwen3.5-Flash measured |
| MedCode (Vals AI) | Not measured | 33.0% | Only Qwen3.5-Flash measured |
| SimpleQA Verified | Not measured | 20.3% | Only Qwen3.5-Flash measured |
| LMArena Text: science | 1437 | 1415 | Qwen3.5-27B |
| LMArena Text: medicine and healthcare | 1425 | 1407 | Qwen3.5-27B |
| LMArena Text: legal and government | 1405 | 1398 | Qwen3.5-27B |
| LMArena Text: business and finance | 1399 | 1394 | Qwen3.5-27B |
| Neither model has a result on the other 5 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3.5-27B | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index154
| Benchmark | Qwen3.5-27B | Qwen3.5-Flash | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 42.5% | Only Qwen3.5-Flash measured |
| MedScribe (Vals AI) | Not measured | 70.6% | Only Qwen3.5-Flash measured |
| IFBench | 75.6% | Not measured | Only Qwen3.5-27B measured |
| Long context reasoning (AA-LCR) | 77.7% | Not measured | Only Qwen3.5-27B measured |
| DTBench | 82.4% | 82.9% | Qwen3.5-Flash |
| LMCA | 34.0% | 29.1% | Qwen3.5-27B |
| LMArena Text | 1409 | 1397 | Qwen3.5-27B |
| LMArena Text: instruction following | 1393 | 1374 | Qwen3.5-27B |
| LMArena Text: creative writing | 1362 | 1343 | Qwen3.5-27B |
| LMArena Vision | 1241 | Not measured | Only Qwen3.5-27B measured |
| LMArena Text: English | 1425 | 1404 | Qwen3.5-27B |
| LMArena Text: non-English | 1390 | 1385 | Qwen3.5-27B |
| LMArena Text: Chinese | 1478 | 1445 | Qwen3.5-27B |
| LMArena Text: German | 1392 | 1390 | Qwen3.5-27B |
| LMArena Text: Japanese | 1346 | 1369 | Qwen3.5-Flash |
| LMArena Text: Korean | 1359 | 1344 | Qwen3.5-27B |
| LMArena Text: French | 1410 | 1413 | Qwen3.5-Flash |
| LMArena Text: Spanish | 1407 | 1400 | Qwen3.5-27B |
| LMArena Text: Russian | 1390 | 1380 | Qwen3.5-27B |
| LMArena Text: Polish | 1375 | 1376 | Qwen3.5-Flash |
| LMArena Text: multi-turn | 1410 | 1393 | Qwen3.5-27B |
| LMArena Text: longer queries | 1413 | 1392 | Qwen3.5-27B |
| LMArena Text: writing and language | 1380 | 1369 | Qwen3.5-27B |
| LMArena Text: entertainment and media | 1363 | 1347 | Qwen3.5-27B |
| LMArena Vision: English | 1246 | Not measured | Only Qwen3.5-27B measured |
| LMArena Vision: Chinese | 1301 | Not measured | Only Qwen3.5-27B measured |
| LMArena Vision: reading text | 1246 | Not measured | Only Qwen3.5-27B measured |
| LMArena Vision: humor | 1227 | Not measured | Only Qwen3.5-27B measured |
| LMArena Vision: captioning | 1170 | Not measured | Only Qwen3.5-27B measured |
| LMArena Vision: recognizing things | 1210 | Not measured | Only Qwen3.5-27B measured |
| LMArena Vision: creative writing | 1224 | Not measured | Only Qwen3.5-27B measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Qwen3.5-27B | Qwen3.5-Flash | 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 | Qwen3.5-27B | Qwen3.5-Flash |
|---|---|---|
| Maker | Qwen | Qwen |
| Weights | Open weights | No public weights listed |
| Released | February 25, 2026 | February 25, 2026 |
| Context window | 262K | 1M |
| Input $/Mtok | $0.27 | $0.065 |
| Output $/Mtok | $2.16 | $0.26 |
| Coding index | 22.6 | 6.2 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | $0.40 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen3.5-27B better than Qwen3.5-Flash for coding?
Pick Qwen3.5-27B for coding: it scores 22.6 on the Vibe Coding Index against 6.2 for Qwen3.5-Flash. Pick Qwen3.5-Flash when output tokens cost 8.3x less, or when the context window is larger, 1M against 262K tokens.
Which is cheaper, Qwen3.5-27B or Qwen3.5-Flash?
Qwen3.5-27B costs $0.27 per million input tokens and $2.16 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Cost per solved task is known for Qwen3.5-Flash only.
Which has the bigger context window, Qwen3.5-27B or Qwen3.5-Flash?
Qwen3.5-Flash, with 1M tokens against 262K.
How many benchmarks is the Qwen3.5-27B vs Qwen3.5-Flash comparison based on?
34 benchmarks have a result for both models as of October 21, 2026. Qwen3.5-27B's coding index rests on 1 independent benchmark and Qwen3.5-Flash's on 1. Where only one model has a result, the page says so instead of naming a winner.