DeepSeek V3.2 vs Qwen3.7 Plus
Updated October 21, 2026. 48 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Qwen3.7 Plus for coding: it scores 33.2 on the Vibe Coding Index against 30.0 for DeepSeek V3.2, though on 2 benchmarks against 3.
Pick DeepSeek V3.2 when output tokens cost 1.5x less, or when what matters is Long context reasoning (AA-LCR) or DTBench, where it leads.
- Shared results
- 48
- DeepSeek V3.2 only
- 27
- Qwen3.7 Plus only
- 27
- Like-for-like categories
- 8 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, six public coding benchmarks
Qwen3.7 Plus
Qwen3.7 Plus scores 33.2 against 30.0.
3 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.7 Plus
Qwen3.7 Plus leads, 25.2 against 21.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.7 Plus has a AA Agentic Index result, 17.5.
Directional only
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
Qwen3.7 Plus
Qwen3.7 Plus leads, 1269 against 1175.
Same board, both measured
Long documents
Listed context window
Qwen3.7 Plus
Qwen3.7 Plus takes 1M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2
DeepSeek V3.2 $0.0007, Qwen3.7 Plus $0.0010 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 30.0 from 3 benchmarks, Qwen3.7 Plus 33.2 from 2 benchmarks.
- Widest category
- Coding, 14.3 points apart on average over 5 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): DeepSeek V3.2 5.1% against Qwen3.7 Plus 46.4%, 41.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 $0.88 against Qwen3.7 Plus $1.28 per million output tokens, 1.5x apart.
- Context window
- DeepSeek V3.2 takes 164K tokens, Qwen3.7 Plus 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 | Qwen3.7 Plus | Ties |
|---|---|---|---|---|
| Coding | 0 | 5 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 8 | ||
| General | 2 | 19 | ||
| All shared boards | 2 | 46 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
| Benchmark | DeepSeek V3.2 | Qwen3.7 Plus | 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 | 55.9 | Qwen3.7 Plus |
| 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% | 46.4% | Qwen3.7 Plus |
| Code migration (Vals AI) | Not measured | 12.9% | Only Qwen3.7 Plus measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 52.8% | Only Qwen3.7 Plus measured |
| SkillsBench (Vals AI) | Not measured | 54.3% | Only Qwen3.7 Plus measured |
| LiveCodeBench (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| SciCode | Not measured | 46.1% | Only Qwen3.7 Plus measured |
| Terminal-Bench Hard (Artificial Analysis run) | 35.6% | 47.0% | Qwen3.7 Plus |
| Terminal-Bench | 39.6% | Not measured | Only DeepSeek V3.2 measured |
| FrontierCode | Not measured | 10.2% | Only Qwen3.7 Plus measured |
| LMArena Text: coding | 1453 | 1473 | Qwen3.7 Plus |
| LMArena Text: software and IT | 1449 | 1473 | Qwen3.7 Plus |
| 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 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 21.5 | 25.2 | Qwen3.7 Plus |
| Epoch Capabilities Index | 146.1 | 147.0 | Qwen3.7 Plus |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 17.5 | Only Qwen3.7 Plus measured |
| Finance Agent (Vals AI) | Not measured | 38.2% | Only Qwen3.7 Plus measured |
| Tax Agent Bench (Vals AI) | Not measured | 10.5% | Only Qwen3.7 Plus measured |
| Legal Research Bench (Vals AI) | Not measured | 16.4% | Only Qwen3.7 Plus measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 49.3% | Only Qwen3.7 Plus measured |
| τ²-Bench Telecom | 90.6% | 93.0% | Qwen3.7 Plus |
| APEX-Agents | 21.3% | Not measured | Only DeepSeek V3.2 measured |
| OSWorld 2.0 | Not measured | 2.8% | Only Qwen3.7 Plus measured |
| Vending-Bench 2 | $1,034 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | DeepSeek V3.2 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| GPQA Diamond | 80.3% | Not measured | Only DeepSeek V3.2 measured |
| GPQA Diamond (Artificial Analysis run) | 84.0% | 90.0% | Qwen3.7 Plus |
| Humanity's Last Exam (Artificial Analysis run) | 24.6% | 35.6% | Qwen3.7 Plus |
| GPQA Diamond (Epoch AI run) | Not measured | 87.9% | Only Qwen3.7 Plus measured |
| ARC-AGI | 57.0% | Not measured | Only DeepSeek V3.2 measured |
| ARC-AGI-2 | 4.0% | Not measured | Only DeepSeek V3.2 measured |
| Mystery game puzzles | Not measured | 17.0% | Only Qwen3.7 Plus measured |
| LMArena Text: hard prompts | 1434 | 1460 | Qwen3.7 Plus |
| LMArena Text: expert | 1436 | 1466 | Qwen3.7 Plus |
| LMArena Text: hard prompts in English | 1446 | 1460 | Qwen3.7 Plus |
| LMArena Vision: diagrams | Not measured | 1292 | Only Qwen3.7 Plus measured |
| LMArena Vision: homework | Not measured | 1295 | Only Qwen3.7 Plus measured |
| Neither model has a result on the other 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 | Qwen3.7 Plus | 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 |
| OTIS Mock AIME | Not measured | 93.3% | Only Qwen3.7 Plus measured |
| FrontierMath, tiers 1 to 3 | Not measured | 34.4% | Only Qwen3.7 Plus measured |
| LMArena Text: math | 1435 | 1467 | Qwen3.7 Plus |
| LMArena Text: mathematical work | 1440 | 1470 | Qwen3.7 Plus |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | DeepSeek V3.2 | Qwen3.7 Plus | 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 |
| MortgageTax (Vals AI) | Not measured | 66.2% | Only Qwen3.7 Plus 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 | 1464 | Qwen3.7 Plus |
| LMArena Text: medicine and healthcare | 1440 | 1464 | Qwen3.7 Plus |
| LMArena Text: legal and government | 1425 | 1451 | Qwen3.7 Plus |
| LMArena Text: business and finance | 1410 | 1435 | Qwen3.7 Plus |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | DeepSeek V3.2 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| Design Arena: websites | 1175 | 1269 | Qwen3.7 Plus |
| Design Arena: UI components | 1149 | 1261 | Qwen3.7 Plus |
| Design Arena: game dev | 1131 | 1262 | Qwen3.7 Plus |
| Design Arena: data viz | 1158 | 1275 | Qwen3.7 Plus |
| Design Arena: 3D | 1133 | 1239 | Qwen3.7 Plus |
| Design Arena: all code categories | 1164 | 1269 | Qwen3.7 Plus |
| Design Arena: SVG | 1032 | 1208 | Qwen3.7 Plus |
| Design Arena: ASCII art | 1082 | 1144 | Qwen3.7 Plus |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index219
| Benchmark | DeepSeek V3.2 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 39.3% | Only Qwen3.7 Plus measured |
| Vals Multimodal Index | Not measured | 53.9% | Only Qwen3.7 Plus measured |
| IFBench | 60.7% | 78.0% | Qwen3.7 Plus |
| Long context reasoning (AA-LCR) | 73.3% | 73.0% | DeepSeek V3.2 |
| DTBench | 85.6% | 84.0% | DeepSeek V3.2 |
| LMCA | 28.8% | 37.6% | Qwen3.7 Plus |
| 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 | 1454 | Qwen3.7 Plus |
| LMArena Text: instruction following | 1413 | 1439 | Qwen3.7 Plus |
| LMArena Text: creative writing | 1400 | 1438 | Qwen3.7 Plus |
| LMArena Vision | Not measured | 1278 | Only Qwen3.7 Plus measured |
| LMArena Text: English | 1439 | 1458 | Qwen3.7 Plus |
| LMArena Text: non-English | 1409 | 1445 | Qwen3.7 Plus |
| LMArena Text: Chinese | 1455 | 1509 | Qwen3.7 Plus |
| LMArena Text: German | 1413 | 1471 | Qwen3.7 Plus |
| LMArena Text: Japanese | 1374 | 1412 | Qwen3.7 Plus |
| LMArena Text: Korean | 1370 | 1413 | Qwen3.7 Plus |
| LMArena Text: French | 1429 | 1472 | Qwen3.7 Plus |
| LMArena Text: Spanish | 1424 | 1457 | Qwen3.7 Plus |
| LMArena Text: Russian | 1424 | 1457 | Qwen3.7 Plus |
| LMArena Text: Polish | 1419 | 1449 | Qwen3.7 Plus |
| LMArena Text: multi-turn | 1427 | 1459 | Qwen3.7 Plus |
| LMArena Text: longer queries | 1428 | 1454 | Qwen3.7 Plus |
| LMArena Text: writing and language | 1407 | 1437 | Qwen3.7 Plus |
| LMArena Text: entertainment and media | 1395 | 1422 | Qwen3.7 Plus |
| LMArena Vision: English | Not measured | 1280 | Only Qwen3.7 Plus measured |
| LMArena Vision: Chinese | Not measured | 1332 | Only Qwen3.7 Plus measured |
| LMArena Vision: reading text | Not measured | 1288 | Only Qwen3.7 Plus measured |
| LMArena Vision: humor | Not measured | 1261 | Only Qwen3.7 Plus measured |
| LMArena Vision: creative writing | Not measured | 1258 | Only Qwen3.7 Plus measured |
| LMArena Document | Not measured | 1444 | Only Qwen3.7 Plus 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 | Qwen3.7 Plus | 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 | Qwen3.7 Plus |
|---|---|---|
| Maker | DeepSeek | Qwen |
| Weights | Open weights | No public weights listed |
| Released | December 1, 2025 | June 3, 2026 |
| Context window | 164K | 1M |
| Input $/Mtok | $0.29 | $0.32 |
| Output $/Mtok | $0.88 | $1.28 |
| Coding index | 30.0 | 33.2 |
| Independent measurements behind it | 3 | 2 |
| Cost per solved task | $0.99 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 better than Qwen3.7 Plus for coding?
Pick Qwen3.7 Plus for coding: it scores 33.2 on the Vibe Coding Index against 30.0 for DeepSeek V3.2, though on 2 benchmarks against 3. Pick DeepSeek V3.2 when output tokens cost 1.5x less, or when what matters is Long context reasoning (AA-LCR) or DTBench, where it leads.
Which is cheaper, DeepSeek V3.2 or Qwen3.7 Plus?
DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; Qwen3.7 Plus costs $0.32 and $1.28. Cost per solved task is known for DeepSeek V3.2 only.
Which has the bigger context window, DeepSeek V3.2 or Qwen3.7 Plus?
Qwen3.7 Plus, with 1M tokens against 164K.
How many benchmarks is the DeepSeek V3.2 vs Qwen3.7 Plus comparison based on?
48 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2's coding index rests on 3 independent benchmarks and Qwen3.7 Plus's on 2. Where only one model has a result, the page says so instead of naming a winner.