DeepSeek V3.2 vs Qwen3.5-27B
Updated October 21, 2026. 41 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick DeepSeek V3.2 for coding: it scores 54.4 on the Vibe Coding Index against 22.6 for Qwen3.5-27B.
Pick Qwen3.5-27B when the context window is larger, 262K against 164K tokens, or when what matters is LMArena WebDev: HTML or AA Intelligence Index, where it leads.
- Shared results
- 41
- DeepSeek V3.2 only
- 34
- Qwen3.5-27B only
- 12
- Like-for-like categories
- 7 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
DeepSeek V3.2
DeepSeek V3.2 scores 54.4 against 22.6.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.5-27B
Qwen3.5-27B leads, 22.9 against 21.5.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only DeepSeek V3.2 has a GPQA Diamond result, 80.3%.
Directional only
Math
AIME
No call
Only DeepSeek V3.2 has a AIME result, 84.6%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V3.2 has a MMLU Pro result, 84.9%.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V3.2 has a Design Arena: websites result, 1175.
Directional only
Long documents
Listed context window
Qwen3.5-27B
Qwen3.5-27B takes 262K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2
DeepSeek V3.2 $0.0005, Qwen3.5-27B $0.0014 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only DeepSeek V3.2 has the number, $0.99.
Directional only
At a glance
- Coding index
- DeepSeek V3.2 54.4 from 2 benchmarks, Qwen3.5-27B 22.6 from 1 benchmark.
- Widest category
- General, 4.4 points apart on average over 21 shared benchmarks.
- Widest single gap
- IFBench: DeepSeek V3.2 60.7% against Qwen3.5-27B 75.6%, 14.9 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 $0.42 against Qwen3.5-27B $2.16 per million output tokens, 5.1x apart.
- Context window
- DeepSeek V3.2 takes 164K tokens, Qwen3.5-27B 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 | DeepSeek V3.2 | Bars | Qwen3.5-27B | Ties |
|---|---|---|---|---|
| Coding | 5 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 1 | ||
| Reasoning | 4 | 1 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 17 | 4 | ||
| All shared boards | 33 | 8 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index51
| Benchmark | DeepSeek V3.2 | Qwen3.5-27B | Leader |
|---|---|---|---|
| SWE-bench Verified | 70.0% | Not measured | Only DeepSeek V3.2 measured |
| SWE-bench Multilingual | 59.0% | Not measured | Only DeepSeek V3.2 measured |
| LMArena WebDev | 1362 | 1358 | DeepSeek V3.2 |
| AA Coding Index | 44.2 | Not measured | Only DeepSeek V3.2 measured |
| LiveCodeBench (Vals AI) | 80.7% | Not measured | Only DeepSeek V3.2 measured |
| SWE-bench Verified (Vals AI) | 67.6% | Not measured | Only DeepSeek V3.2 measured |
| Vibe Code Bench (Vals AI) | 5.1% | Not measured | Only DeepSeek V3.2 measured |
| LiveCodeBench (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 35.6% | 32.6% | DeepSeek V3.2 |
| Terminal-Bench | 39.6% | Not measured | Only DeepSeek V3.2 measured |
| WeirdML | Not measured | 39.5% | Only Qwen3.5-27B measured |
| ALE-Bench | Not measured | 349.4 | Only Qwen3.5-27B measured |
| LMArena Text: coding | 1453 | 1427 | DeepSeek V3.2 |
| LMArena Text: software and IT | 1449 | 1431 | DeepSeek V3.2 |
| LMArena WebDev: React | 1350 | 1346 | DeepSeek V3.2 |
| LMArena WebDev: HTML | 1371 | 1386 | Qwen3.5-27B |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 21.5 | 22.9 | Qwen3.5-27B |
| Epoch Capabilities Index | 146.1 | Not measured | Only DeepSeek V3.2 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | DeepSeek V3.2 | Qwen3.5-27B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 90.6% | 93.9% | Qwen3.5-27B |
| APEX-Agents | 21.3% | Not measured | Only DeepSeek V3.2 measured |
| Vending-Bench 2 | $1,034 | $202 | DeepSeek V3.2 |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | DeepSeek V3.2 | Qwen3.5-27B | Leader |
|---|---|---|---|
| GPQA Diamond | 80.3% | Not measured | Only DeepSeek V3.2 measured |
| GPQA Diamond (Artificial Analysis run) | 84.0% | 85.8% | Qwen3.5-27B |
| Humanity's Last Exam (Artificial Analysis run) | 24.6% | 23.9% | DeepSeek V3.2 |
| ARC-AGI | 57.0% | Not measured | Only DeepSeek V3.2 measured |
| ARC-AGI-2 | 4.0% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: hard prompts | 1434 | 1415 | DeepSeek V3.2 |
| LMArena Text: expert | 1436 | 1427 | DeepSeek V3.2 |
| LMArena Text: hard prompts in English | 1446 | 1429 | DeepSeek V3.2 |
| LMArena Vision: diagrams | Not measured | 1249 | Only Qwen3.5-27B measured |
| LMArena Vision: homework | Not measured | 1256 | Only Qwen3.5-27B measured |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V3.2 | Qwen3.5-27B | Leader |
|---|---|---|---|
| AIME | 84.6% | Not measured | Only DeepSeek V3.2 measured |
| MGSM (Vals AI) | 86.0% | Not measured | Only DeepSeek V3.2 measured |
| AA Math Index | 92.0 | Not measured | Only DeepSeek V3.2 measured |
| AIME 2025 (Artificial Analysis run) | 92.0% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: math | 1435 | 1429 | DeepSeek V3.2 |
| LMArena Text: mathematical work | 1440 | 1429 | DeepSeek V3.2 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V3.2 | Qwen3.5-27B | Leader |
|---|---|---|---|
| MMLU Pro | 84.9% | Not measured | Only DeepSeek V3.2 measured |
| LegalBench (Vals AI) | 76.1% | Not measured | Only DeepSeek V3.2 measured |
| CaseLaw (Vals AI) | 55.4% | Not measured | Only DeepSeek V3.2 measured |
| CorpFin (Vals AI) | 51.0% | Not measured | Only DeepSeek V3.2 measured |
| TaxEval (Vals AI) | 68.2% | Not measured | Only DeepSeek V3.2 measured |
| MedQA (Vals AI) | 93.9% | Not measured | Only DeepSeek V3.2 measured |
| MMLU Pro (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: science | 1446 | 1437 | DeepSeek V3.2 |
| LMArena Text: medicine and healthcare | 1440 | 1425 | DeepSeek V3.2 |
| LMArena Text: legal and government | 1425 | 1405 | DeepSeek V3.2 |
| LMArena Text: business and finance | 1410 | 1399 | DeepSeek V3.2 |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V3.2 | Qwen3.5-27B | Leader |
|---|---|---|---|
| Design Arena: websites | 1175 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: UI components | 1149 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: game dev | 1132 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: data viz | 1158 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: 3D | 1133 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: all code categories | 1164 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: SVG | 1032 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: ASCII art | 1082 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index174
| Benchmark | DeepSeek V3.2 | Qwen3.5-27B | Leader |
|---|---|---|---|
| IFBench | 60.7% | 75.6% | Qwen3.5-27B |
| Long context reasoning (AA-LCR) | 73.3% | 77.7% | Qwen3.5-27B |
| DTBench | 85.6% | 82.4% | DeepSeek V3.2 |
| LMCA | 28.8% | 34.0% | Qwen3.5-27B |
| CL-bench | 12.4% | Not measured | Only DeepSeek V3.2 measured |
| CL-bench Life | 7.4% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text | 1425 | 1409 | DeepSeek V3.2 |
| LMArena Text: instruction following | 1413 | 1393 | DeepSeek V3.2 |
| LMArena Text: creative writing | 1400 | 1362 | DeepSeek V3.2 |
| LMArena Vision | Not measured | 1241 | Only Qwen3.5-27B measured |
| LMArena Text: English | 1439 | 1425 | DeepSeek V3.2 |
| LMArena Text: non-English | 1409 | 1390 | DeepSeek V3.2 |
| LMArena Text: Chinese | 1455 | 1478 | Qwen3.5-27B |
| LMArena Text: German | 1413 | 1392 | DeepSeek V3.2 |
| LMArena Text: Japanese | 1374 | 1346 | DeepSeek V3.2 |
| LMArena Text: Korean | 1370 | 1359 | DeepSeek V3.2 |
| LMArena Text: French | 1429 | 1410 | DeepSeek V3.2 |
| LMArena Text: Spanish | 1424 | 1407 | DeepSeek V3.2 |
| LMArena Text: Russian | 1424 | 1390 | DeepSeek V3.2 |
| LMArena Text: Polish | 1419 | 1375 | DeepSeek V3.2 |
| LMArena Text: multi-turn | 1427 | 1410 | DeepSeek V3.2 |
| LMArena Text: longer queries | 1428 | 1413 | DeepSeek V3.2 |
| LMArena Text: writing and language | 1407 | 1380 | DeepSeek V3.2 |
| LMArena Text: entertainment and media | 1395 | 1363 | DeepSeek V3.2 |
| LMArena Vision: English | Not measured | 1246 | Only Qwen3.5-27B measured |
| LMArena Vision: Chinese | Not measured | 1301 | Only Qwen3.5-27B measured |
| LMArena Vision: reading text | Not measured | 1246 | Only Qwen3.5-27B measured |
| LMArena Vision: humor | Not measured | 1227 | Only Qwen3.5-27B measured |
| LMArena Vision: captioning | Not measured | 1170 | Only Qwen3.5-27B measured |
| LMArena Vision: recognizing things | Not measured | 1210 | Only Qwen3.5-27B measured |
| LMArena Vision: creative writing | Not measured | 1224 | 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 | DeepSeek V3.2 | Qwen3.5-27B | 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
DeepSeek V3.2 costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.2 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.2 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 | DeepSeek V3.2 | Qwen3.5-27B |
|---|---|---|
| Maker | DeepSeek | Qwen |
| Weights | Open weights | Open weights |
| Released | December 1, 2025 | February 25, 2026 |
| Context window | 164K | 262K |
| Input $/Mtok | $0.28 | $0.27 |
| Output $/Mtok | $0.42 | $2.16 |
| Coding index | 54.4 | 22.6 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $0.99 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 better than Qwen3.5-27B for coding?
Pick DeepSeek V3.2 for coding: it scores 54.4 on the Vibe Coding Index against 22.6 for Qwen3.5-27B. Pick Qwen3.5-27B when the context window is larger, 262K against 164K tokens, or when what matters is LMArena WebDev: HTML or AA Intelligence Index, where it leads.
Which is cheaper, DeepSeek V3.2 or Qwen3.5-27B?
DeepSeek V3.2 costs $0.28 per million input tokens and $0.42 per million output tokens; Qwen3.5-27B costs $0.27 and $2.16. Cost per solved task is known for DeepSeek V3.2 only.
Which has the bigger context window, DeepSeek V3.2 or Qwen3.5-27B?
Qwen3.5-27B, with 262K tokens against 164K.
How many benchmarks is the DeepSeek V3.2 vs Qwen3.5-27B comparison based on?
41 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2's coding index rests on 2 independent benchmarks and Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.