DeepSeek V3.2 Exp vs Qwen3.5-27B
Qwen3.5-27B leads DeepSeek V3.2 Exp on the Artificial Analysis Intelligence Index, 22.9 to 16.6, and DeepSeek V3.2 Exp costs 3.8x less per output token ($0.41 vs $1.56 per million). Updated October 22, 2026; 40 benchmarks have a result for both.
DeepSeek V3.2 Exp or Qwen3.5-27B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3.2 Exp for output tokens 3.8x cheaper ($0.41 vs $1.56 per million) and work measured by LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 40
- DeepSeek V3.2 Exp only
- 19
- Qwen3.5-27B only
- 13
- Like-for-like categories
- 7 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
Qwen3.5-27B
Qwen3.5-27B leads, 22.9 against 16.6.
Same board, both measured
Web design
Design Arena: websites
No call
Only DeepSeek V3.2 Exp has a Design Arena: websites result, 1178.
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 Exp
DeepSeek V3.2 Exp $0.0005, Qwen3.5-27B $0.0010 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.2 Exp not benchmarked, Qwen3.5-27B not benchmarked.
- Widest category
- Agentic work, 59.9 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: DeepSeek V3.2 Exp 33.9% against Qwen3.5-27B 93.9%, 59.9 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against Qwen3.5-27B $1.56 per million output tokens, 3.8x apart.
- Context window
- DeepSeek V3.2 Exp 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 Exp | Bars | Qwen3.5-27B | Ties |
|---|---|---|---|---|
| Coding | 2 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 2 | 3 | ||
| Math | 1 | 1 | ||
| Knowledge | 4 | 0 | ||
| General | 16 | 5 | ||
| All shared boards | 25 | 15 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index24
| Benchmark | DeepSeek V3.2 Exp | Qwen3.5-27B | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena WebDev | 1272 | 1358 | Qwen3.5-27B |
| LiveCodeBench (Artificial Analysis run) | 78.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | 32.6% | Qwen3.5-27B |
| WeirdML | 39.5% | 39.5% | Qwen3.5-27B |
| ALE-Bench | Not measured | 349.4 | Only Qwen3.5-27B measured |
| LMArena Text: coding | 1438 | 1427 | DeepSeek V3.2 Exp |
| LMArena Text: software and IT | 1438 | 1431 | DeepSeek V3.2 Exp |
| LMArena WebDev: React | Not measured | 1346 | Only Qwen3.5-27B measured |
| LMArena WebDev: HTML | 1281 | 1386 | Qwen3.5-27B |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 Exp | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | 22.9 | Qwen3.5-27B |
| Epoch Capabilities Index | 144.9 | Not measured | Only DeepSeek V3.2 Exp measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 Exp | Qwen3.5-27B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 33.9% | 93.9% | Qwen3.5-27B |
| TheAgentCompany | 42.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Vending-Bench 2 | Not measured | $202 | Only Qwen3.5-27B measured |
| Berkeley Function Calling Leaderboard (BFCL) | 56.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index23
| Benchmark | DeepSeek V3.2 Exp | Qwen3.5-27B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.7% | 85.8% | Qwen3.5-27B |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | 23.9% | Qwen3.5-27B |
| LMArena Text: hard prompts | 1429 | 1415 | DeepSeek V3.2 Exp |
| LMArena Text: expert | 1422 | 1427 | Qwen3.5-27B |
| LMArena Text: hard prompts in English | 1444 | 1429 | DeepSeek V3.2 Exp |
| LMArena Vision: diagrams | Not measured | 1249 | Only Qwen3.5-27B measured |
| LMArena Vision: homework | Not measured | 1256 | Only Qwen3.5-27B measured |
| Kagi LLM Benchmark | 52.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | DeepSeek V3.2 Exp | Qwen3.5-27B | Leader |
|---|---|---|---|
| AA Math Index | 87.7 | Not measured | Only DeepSeek V3.2 Exp measured |
| AIME 2025 (Artificial Analysis run) | 87.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: math | 1422 | 1429 | Qwen3.5-27B |
| LMArena Text: mathematical work | 1433 | 1429 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V3.2 Exp | Qwen3.5-27B | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 85.0% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: science | 1443 | 1437 | DeepSeek V3.2 Exp |
| LMArena Text: medicine and healthcare | 1468 | 1425 | DeepSeek V3.2 Exp |
| LMArena Text: legal and government | 1419 | 1405 | DeepSeek V3.2 Exp |
| LMArena Text: business and finance | 1412 | 1399 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Qwen3.5-27B | Leader |
|---|---|---|---|
| Design Arena: websites | 1178 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: UI components | 1164 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: game dev | 1144 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: data viz | 1152 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: 3D | 1148 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: all code categories | 1170 | Not measured | Only DeepSeek V3.2 Exp measured |
| Design Arena: SVG | 1031 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index165
| Benchmark | DeepSeek V3.2 Exp | Qwen3.5-27B | Leader |
|---|---|---|---|
| IFBench | 54.1% | 75.6% | Qwen3.5-27B |
| Long context reasoning (AA-LCR) | 72.3% | 77.7% | Qwen3.5-27B |
| DTBench | 87.7% | 82.4% | DeepSeek V3.2 Exp |
| LMCA | 29.1% | 34.0% | Qwen3.5-27B |
| Fiction.LiveBench | 83.3% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench | 13.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench Life | 9.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text | 1425 | 1409 | DeepSeek V3.2 Exp |
| LMArena Text: instruction following | 1401 | 1393 | DeepSeek V3.2 Exp |
| LMArena Text: creative writing | 1404 | 1362 | DeepSeek V3.2 Exp |
| LMArena Vision | Not measured | 1241 | Only Qwen3.5-27B measured |
| LMArena Text: English | 1441 | 1425 | DeepSeek V3.2 Exp |
| LMArena Text: non-English | 1404 | 1390 | DeepSeek V3.2 Exp |
| LMArena Text: Chinese | 1461 | 1478 | Qwen3.5-27B |
| LMArena Text: German | 1440 | 1392 | DeepSeek V3.2 Exp |
| LMArena Text: Japanese | 1331 | 1346 | Qwen3.5-27B |
| LMArena Text: Korean | 1372 | 1359 | DeepSeek V3.2 Exp |
| LMArena Text: French | 1434 | 1410 | DeepSeek V3.2 Exp |
| LMArena Text: Spanish | 1441 | 1407 | DeepSeek V3.2 Exp |
| LMArena Text: Russian | 1412 | 1390 | DeepSeek V3.2 Exp |
| LMArena Text: Polish | 1435 | 1375 | DeepSeek V3.2 Exp |
| LMArena Text: multi-turn | 1422 | 1410 | DeepSeek V3.2 Exp |
| LMArena Text: longer queries | 1418 | 1413 | DeepSeek V3.2 Exp |
| LMArena Text: writing and language | 1407 | 1380 | DeepSeek V3.2 Exp |
| LMArena Text: entertainment and media | 1407 | 1363 | DeepSeek V3.2 Exp |
| 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 14 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | 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 Exp costs less
Repository review
50K input + 3K output tokens
Qwen3.5-27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-27B 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 Exp | Qwen3.5-27B |
|---|---|---|
| Maker | DeepSeek | Qwen |
| Weights | Open weights | Open weights |
| Released | September 29, 2025 | February 25, 2026 |
| Context window | 164K | 262K |
| Input $/Mtok | $0.27 | $0.195 |
| Output $/Mtok | $0.41 | $1.56 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp better than Qwen3.5-27B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for output tokens 3.8x cheaper ($0.41 vs $1.56 per million) and work measured by LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, DeepSeek V3.2 Exp or Qwen3.5-27B?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; Qwen3.5-27B costs $0.195 and $1.56. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.2 Exp or Qwen3.5-27B?
Qwen3.5-27B, with 262K tokens against 164K.
How many benchmarks is the DeepSeek V3.2 Exp vs Qwen3.5-27B comparison based on?
40 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark and Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.