DeepSeek V3.2 vs Qwen3.5 Plus 2026-04-20
Qwen3.5 Plus 2026-04-20 leads DeepSeek V3.2 on coding, 39.5 to 30.0 on the Vibe Coding Index, and DeepSeek V3.2 costs 2.0x less per output token ($0.88 vs $1.80 per million). Updated October 22, 2026; 16 benchmarks have a result for both.
DeepSeek V3.2 or Qwen3.5 Plus 2026-04-20: which to pick
Pick Qwen3.5 Plus 2026-04-20 for coding: it scores 39.5 on the Vibe Coding Index against 30.0 for DeepSeek V3.2, though on 2 benchmarks against 3.
Pick DeepSeek V3.2 for output tokens 2.0x cheaper ($0.88 vs $1.80 per million) and a lower cost per solved task ($0.99 against $1.35).
- Shared results
- 16
- DeepSeek V3.2 only
- 59
- Qwen3.5 Plus 2026-04-20 only
- 9
- 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
Qwen3.5 Plus 2026-04-20
Qwen3.5 Plus 2026-04-20 scores 39.5 against 30.0.
3 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only DeepSeek V3.2 has a AA Intelligence Index result, 21.5.
Directional only
Reasoning
GPQA Diamond
Qwen3.5 Plus 2026-04-20
Qwen3.5 Plus 2026-04-20 leads, 87.4% against 80.3%.
Same board, both measured
Math
AIME
Qwen3.5 Plus 2026-04-20
Qwen3.5 Plus 2026-04-20 leads, 86.0% against 84.6%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.5 Plus 2026-04-20
Qwen3.5 Plus 2026-04-20 leads, 87.2% against 84.9%.
Same board, both measured
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 Plus 2026-04-20
Qwen3.5 Plus 2026-04-20 takes 1M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2
DeepSeek V3.2 $0.0007, Qwen3.5 Plus 2026-04-20 $0.0012 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V3.2
DeepSeek V3.2 $0.99, Qwen3.5 Plus 2026-04-20 $1.35.
Published attempt cost and pass rate
At a glance
- Coding index
- DeepSeek V3.2 30.0 from 3 benchmarks, Qwen3.5 Plus 2026-04-20 39.5 from 2 benchmarks.
- Widest category
- Reasoning, 7.1 points apart on average over 1 shared benchmark.
- Widest single gap
- CorpFin (Vals AI): DeepSeek V3.2 51.0% against Qwen3.5 Plus 2026-04-20 65.3%, 14.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 $0.88 against Qwen3.5 Plus 2026-04-20 $1.80 per million output tokens, 2.0x apart.
- Context window
- DeepSeek V3.2 takes 164K tokens, Qwen3.5 Plus 2026-04-20 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 | DeepSeek V3.2 | Bars | Qwen3.5 Plus 2026-04-20 | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 0 | 1 | ||
| Math | 0 | 1 | ||
| Knowledge | 0 | 5 | ||
| General | 1 | 3 | ||
| All shared boards | 2 | 14 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | DeepSeek V3.2 | Qwen3.5 Plus 2026-04-20 | 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 | Not measured | Only DeepSeek V3.2 measured |
| AA Coding Index | 44.2 | Not measured | Only DeepSeek V3.2 measured |
| LiveCodeBench (Vals AI) | 80.7% | 85.3% | Qwen3.5 Plus 2026-04-20 |
| SWE-bench Verified (Vals AI) | 67.6% | 71.2% | Qwen3.5 Plus 2026-04-20 |
| Vibe Code Bench (Vals AI) | 5.1% | 15.7% | Qwen3.5 Plus 2026-04-20 |
| LiveCodeBench (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 35.6% | Not measured | Only DeepSeek V3.2 measured |
| Terminal-Bench | 39.6% | Not measured | Only DeepSeek V3.2 measured |
| ALE-Bench | Not measured | 608.9 | Only Qwen3.5 Plus 2026-04-20 measured |
| LMArena Text: coding | 1453 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: software and IT | 1449 | Not measured | Only DeepSeek V3.2 measured |
| LMArena WebDev: React | 1350 | Not measured | Only DeepSeek V3.2 measured |
| LMArena WebDev: HTML | 1371 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 | Qwen3.5 Plus 2026-04-20 | Leader |
|---|---|---|---|
| AA Intelligence Index | 21.5 | Not measured | Only DeepSeek V3.2 measured |
| Epoch Capabilities Index | 146.1 | 146.2 | Qwen3.5 Plus 2026-04-20 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.2 | Qwen3.5 Plus 2026-04-20 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 90.6% | Not measured | Only DeepSeek V3.2 measured |
| APEX-Agents | 21.3% | Not measured | Only DeepSeek V3.2 measured |
| Vending-Bench 2 | $1,034 | $1 | DeepSeek V3.2 |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 | Qwen3.5 Plus 2026-04-20 | Leader |
|---|---|---|---|
| GPQA Diamond | 80.3% | 87.4% | Qwen3.5 Plus 2026-04-20 |
| MMMU Pro (Vals AI) | Not measured | 22.8% | Only Qwen3.5 Plus 2026-04-20 measured |
| GPQA Diamond (Artificial Analysis run) | 84.0% | Not measured | Only DeepSeek V3.2 measured |
| Humanity's Last Exam (Artificial Analysis run) | 24.6% | Not measured | Only DeepSeek V3.2 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 84.8% | Only Qwen3.5 Plus 2026-04-20 measured |
| ARC-AGI | 57.0% | Not measured | Only DeepSeek V3.2 measured |
| ARC-AGI-2 | 4.0% | Not measured | Only DeepSeek V3.2 measured |
| Chess puzzles | Not measured | 8.0% | Only Qwen3.5 Plus 2026-04-20 measured |
| Mystery game puzzles | Not measured | 17.0% | Only Qwen3.5 Plus 2026-04-20 measured |
| LMArena Text: hard prompts | 1434 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: expert | 1436 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: hard prompts in English | 1446 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 | Qwen3.5 Plus 2026-04-20 | Leader |
|---|---|---|---|
| AIME | 84.6% | 86.0% | Qwen3.5 Plus 2026-04-20 |
| 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 |
| OTIS Mock AIME | Not measured | 86.7% | Only Qwen3.5 Plus 2026-04-20 measured |
| LMArena Text: math | 1435 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: mathematical work | 1440 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | DeepSeek V3.2 | Qwen3.5 Plus 2026-04-20 | Leader |
|---|---|---|---|
| MMLU Pro | 84.9% | 87.2% | Qwen3.5 Plus 2026-04-20 |
| LegalBench (Vals AI) | 76.1% | 85.1% | Qwen3.5 Plus 2026-04-20 |
| CaseLaw (Vals AI) | 55.4% | 59.7% | Qwen3.5 Plus 2026-04-20 |
| CorpFin (Vals AI) | 51.0% | 65.3% | Qwen3.5 Plus 2026-04-20 |
| TaxEval (Vals AI) | 68.2% | Not measured | Only DeepSeek V3.2 measured |
| MortgageTax (Vals AI) | Not measured | 60.8% | Only Qwen3.5 Plus 2026-04-20 measured |
| MedQA (Vals AI) | 93.9% | 95.2% | Qwen3.5 Plus 2026-04-20 |
| MMLU Pro (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| SimpleQA Verified | Not measured | 25.4% | Only Qwen3.5 Plus 2026-04-20 measured |
| LMArena Text: science | 1446 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: medicine and healthcare | 1440 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: legal and government | 1425 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: business and finance | 1410 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V3.2 | Qwen3.5 Plus 2026-04-20 | Leader |
|---|---|---|---|
| Design Arena: websites | 1175 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: UI components | 1146 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: game dev | 1131 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: data viz | 1158 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: 3D | 1132 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: all code categories | 1163 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: SVG | 1032 | Not measured | Only DeepSeek V3.2 measured |
| Design Arena: ASCII art | 1083 | 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 index13
| Benchmark | DeepSeek V3.2 | Qwen3.5 Plus 2026-04-20 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 30.4% | Only Qwen3.5 Plus 2026-04-20 measured |
| IFBench | 60.7% | Not measured | Only DeepSeek V3.2 measured |
| Long context reasoning (AA-LCR) | 73.3% | Not measured | Only DeepSeek V3.2 measured |
| DTBench | 85.6% | 80.5% | DeepSeek V3.2 |
| LMCA | 28.8% | 36.4% | Qwen3.5 Plus 2026-04-20 |
| CL-bench | 12.4% | 19.8% | Qwen3.5 Plus 2026-04-20 |
| CL-bench Life | 7.4% | 12.4% | Qwen3.5 Plus 2026-04-20 |
| LMArena Text | 1425 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: instruction following | 1413 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: creative writing | 1400 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: English | 1439 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: non-English | 1409 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Chinese | 1455 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: German | 1413 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Japanese | 1374 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Korean | 1370 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: French | 1429 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Spanish | 1424 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Russian | 1424 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Polish | 1419 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: multi-turn | 1427 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: longer queries | 1428 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: writing and language | 1407 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: entertainment and media | 1395 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 | Qwen3.5 Plus 2026-04-20 | 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 Plus 2026-04-20 |
|---|---|---|
| Maker | DeepSeek | Qwen |
| Weights | Open weights | No public weights listed |
| Released | December 1, 2025 | April 27, 2026 |
| Context window | 164K | 1M |
| Input $/Mtok | $0.29 | $0.30 |
| Output $/Mtok | $0.88 | $1.80 |
| Coding index | 30.0 | 39.5 |
| Independent measurements behind it | 3 | 2 |
| Cost per solved task | $0.99 | $1.35 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 better than Qwen3.5 Plus 2026-04-20 for coding?
Pick Qwen3.5 Plus 2026-04-20 for coding: it scores 39.5 on the Vibe Coding Index against 30.0 for DeepSeek V3.2, though on 2 benchmarks against 3. Pick DeepSeek V3.2 for output tokens 2.0x cheaper ($0.88 vs $1.80 per million) and a lower cost per solved task ($0.99 against $1.35).
Which is cheaper, DeepSeek V3.2 or Qwen3.5 Plus 2026-04-20?
DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; Qwen3.5 Plus 2026-04-20 costs $0.30 and $1.80. Per solved SWE-bench Verified task, DeepSeek V3.2 costs $0.99 and Qwen3.5 Plus 2026-04-20 $1.35.
Which has the bigger context window, DeepSeek V3.2 or Qwen3.5 Plus 2026-04-20?
Qwen3.5 Plus 2026-04-20, with 1M tokens against 164K.
How many benchmarks is the DeepSeek V3.2 vs Qwen3.5 Plus 2026-04-20 comparison based on?
16 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2's coding index rests on 3 independent benchmarks and Qwen3.5 Plus 2026-04-20's on 2. Where only one model has a result, the page says so instead of naming a winner.