DeepSeek V3.2 vs Qwen3.8 27B
Updated October 21, 2026. 49 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 54.4 for DeepSeek V3.2.
Pick DeepSeek V3.2 when output tokens cost 3.2x less, or when a solved task costs $0.99 against $1.26.
- Shared results
- 49
- DeepSeek V3.2 only
- 26
- Qwen3.8 27B only
- 36
- 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
Qwen3.8 27B
Qwen3.8 27B scores 73.6 against 54.4.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
Qwen3.8 27B
Qwen3.8 27B leads, 33.7 against 21.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.8 27B has a AA Agentic Index result, 45.8.
Directional only
Reasoning
GPQA Diamond
Qwen3.8 27B
Qwen3.8 27B leads, 88.9% against 80.3%.
Same board, both measured
Math
AIME
No call
Only DeepSeek V3.2 has a AIME result, 84.6%.
Directional only
Knowledge
MMLU Pro
DeepSeek V3.2
DeepSeek V3.2 leads, 84.9% against 84.3%.
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
Qwen3.8 27B
Qwen3.8 27B takes 1M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2
DeepSeek V3.2 $0.0005, Qwen3.8 27B $0.0007 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V3.2
DeepSeek V3.2 $0.99, Qwen3.8 27B $1.26.
Published attempt cost and pass rate
At a glance
- Coding index
- DeepSeek V3.2 54.4 from 2 benchmarks, Qwen3.8 27B 73.6 from 2 benchmarks.
- Widest category
- Agentic work, 26.2 points apart on average over 1 shared benchmark.
- Widest single gap
- Vibe Code Bench (Vals AI): DeepSeek V3.2 5.1% against Qwen3.8 27B 64.8%, 59.7 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 $0.42 against Qwen3.8 27B $1.35 per million output tokens, 3.2x apart.
- Context window
- DeepSeek V3.2 takes 164K tokens, Qwen3.8 27B 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 | Qwen3.8 27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 9 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 8 | ||
| Math | 0 | 2 | ||
| Knowledge | 2 | 5 | ||
| General | 4 | 16 | ||
| All shared boards | 6 | 43 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index09
| Benchmark | DeepSeek V3.2 | Qwen3.8 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 |
| LiveBench coding | Not measured | 86.8% | Only Qwen3.8 27B measured |
| LMArena WebDev | 1362 | 1593 | Qwen3.8 27B |
| AA Coding Index | 44.2 | 68.1 | Qwen3.8 27B |
| LiveCodeBench (Vals AI) | 80.7% | 84.0% | Qwen3.8 27B |
| SWE-bench Verified (Vals AI) | 67.6% | 86.0% | Qwen3.8 27B |
| Vibe Code Bench (Vals AI) | 5.1% | 64.8% | Qwen3.8 27B |
| Code migration (Vals AI) | Not measured | 14.2% | Only Qwen3.8 27B measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Qwen3.8 27B measured |
| IOI (Vals AI) | Not measured | 39.1% | Only Qwen3.8 27B measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 58.4% | Only Qwen3.8 27B measured |
| SkillsBench (Vals AI) | Not measured | 38.1% | Only Qwen3.8 27B measured |
| CyberBench (Vals AI) | Not measured | 67.8% | Only Qwen3.8 27B measured |
| LiveCodeBench (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| SciCode | Not measured | 46.6% | Only Qwen3.8 27B 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 |
| LMArena Text: coding | 1453 | 1483 | Qwen3.8 27B |
| LMArena Text: software and IT | 1449 | 1478 | Qwen3.8 27B |
| LMArena WebDev: React | 1350 | 1602 | Qwen3.8 27B |
| LMArena WebDev: HTML | 1371 | 1570 | Qwen3.8 27B |
| LMArena WebDev: from an image | Not measured | 1577 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 21.5 | 33.7 | Qwen3.8 27B |
| Epoch Capabilities Index | 146.1 | 151.7 | Qwen3.8 27B |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 45.8 | Only Qwen3.8 27B measured |
| LiveBench research | Not measured | 59.4% | Only Qwen3.8 27B measured |
| LiveBench browser | Not measured | 32.0% | Only Qwen3.8 27B measured |
| LiveBench long agentic | Not measured | 27.9% | Only Qwen3.8 27B measured |
| Finance Agent (Vals AI) | Not measured | 48.5% | Only Qwen3.8 27B measured |
| Tax Agent Bench (Vals AI) | Not measured | 30.9% | Only Qwen3.8 27B measured |
| Legal Research Bench (Vals AI) | Not measured | 36.1% | Only Qwen3.8 27B measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 59.7% | Only Qwen3.8 27B measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 0.0% | Only Qwen3.8 27B measured |
| τ²-Bench Telecom | 90.6% | Not measured | Only DeepSeek V3.2 measured |
| APEX-Agents | 21.3% | 47.5% | Qwen3.8 27B |
| Vending-Bench 2 | $1,034 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 27 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | DeepSeek V3.2 | Qwen3.8 27B | Leader |
|---|---|---|---|
| GPQA Diamond | 80.3% | 88.9% | Qwen3.8 27B |
| LiveBench reasoning | Not measured | 80.0% | Only Qwen3.8 27B measured |
| MMMU Pro (Vals AI) | Not measured | 83.9% | Only Qwen3.8 27B measured |
| GPQA Diamond (Artificial Analysis run) | 84.0% | 90.5% | Qwen3.8 27B |
| Humanity's Last Exam (Artificial Analysis run) | 24.6% | 33.9% | Qwen3.8 27B |
| ARC-AGI | 57.0% | 87.5% | Qwen3.8 27B |
| ARC-AGI-2 | 4.0% | 42.4% | Qwen3.8 27B |
| LMArena Text: hard prompts | 1434 | 1459 | Qwen3.8 27B |
| LMArena Text: expert | 1436 | 1479 | Qwen3.8 27B |
| LMArena Text: hard prompts in English | 1446 | 1461 | Qwen3.8 27B |
| LMArena Vision: diagrams | Not measured | 1285 | Only Qwen3.8 27B measured |
| LMArena Vision: homework | Not measured | 1295 | Only Qwen3.8 27B 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 | Qwen3.8 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 |
| ProofBench | Not measured | 16.0% | Only Qwen3.8 27B measured |
| LMArena Text: math | 1435 | 1455 | Qwen3.8 27B |
| LMArena Text: mathematical work | 1440 | 1458 | Qwen3.8 27B |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index25
| Benchmark | DeepSeek V3.2 | Qwen3.8 27B | Leader |
|---|---|---|---|
| MMLU Pro | 84.9% | 84.3% | DeepSeek V3.2 |
| LegalBench (Vals AI) | 76.1% | 82.4% | Qwen3.8 27B |
| 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% | 70.8% | Qwen3.8 27B |
| MortgageTax (Vals AI) | Not measured | 64.9% | Only Qwen3.8 27B measured |
| MedQA (Vals AI) | 93.9% | Not measured | Only DeepSeek V3.2 measured |
| MedCode (Vals AI) | Not measured | 28.7% | Only Qwen3.8 27B measured |
| MMLU Pro (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: science | 1446 | 1467 | Qwen3.8 27B |
| LMArena Text: medicine and healthcare | 1440 | 1437 | DeepSeek V3.2 |
| LMArena Text: legal and government | 1425 | 1447 | Qwen3.8 27B |
| LMArena Text: business and finance | 1410 | 1438 | Qwen3.8 27B |
| Neither model has a result on the other 4 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | DeepSeek V3.2 | Qwen3.8 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 |
| LiveBench design | Not measured | 75.9% | Only Qwen3.8 27B measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index416
| Benchmark | DeepSeek V3.2 | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 76.6% | Only Qwen3.8 27B measured |
| SAGE (Vals AI) | Not measured | 52.4% | Only Qwen3.8 27B measured |
| MedScribe (Vals AI) | Not measured | 83.8% | Only Qwen3.8 27B measured |
| IFBench | 60.7% | Not measured | Only DeepSeek V3.2 measured |
| Long context reasoning (AA-LCR) | 73.3% | 82.0% | Qwen3.8 27B |
| DTBench | 85.6% | 88.0% | Qwen3.8 27B |
| LMCA | 28.8% | 41.4% | Qwen3.8 27B |
| CL-bench | 12.4% | Not measured | Only DeepSeek V3.2 measured |
| Surface Evolver Bench | Not measured | 45.0% | Only Qwen3.8 27B measured |
| CL-bench Life | 7.4% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text | 1425 | 1440 | Qwen3.8 27B |
| LMArena Text: instruction following | 1413 | 1436 | Qwen3.8 27B |
| LMArena Text: creative writing | 1400 | 1383 | DeepSeek V3.2 |
| LMArena Vision | Not measured | 1269 | Only Qwen3.8 27B measured |
| LMArena Text: English | 1439 | 1448 | Qwen3.8 27B |
| LMArena Text: non-English | 1409 | 1428 | Qwen3.8 27B |
| LMArena Text: Chinese | 1455 | 1501 | Qwen3.8 27B |
| LMArena Text: German | 1413 | 1443 | Qwen3.8 27B |
| LMArena Text: Japanese | 1374 | 1387 | Qwen3.8 27B |
| LMArena Text: Korean | 1370 | 1384 | Qwen3.8 27B |
| LMArena Text: French | 1429 | 1465 | Qwen3.8 27B |
| LMArena Text: Spanish | 1424 | 1449 | Qwen3.8 27B |
| LMArena Text: Russian | 1424 | 1415 | DeepSeek V3.2 |
| LMArena Text: Polish | 1419 | 1439 | Qwen3.8 27B |
| LMArena Text: multi-turn | 1427 | 1440 | Qwen3.8 27B |
| LMArena Text: longer queries | 1428 | 1448 | Qwen3.8 27B |
| LMArena Text: writing and language | 1407 | 1405 | DeepSeek V3.2 |
| LMArena Text: entertainment and media | 1395 | 1393 | DeepSeek V3.2 |
| LMArena Vision: English | Not measured | 1267 | Only Qwen3.8 27B measured |
| LMArena Vision: Chinese | Not measured | 1326 | Only Qwen3.8 27B measured |
| LMArena Vision: reading text | Not measured | 1281 | Only Qwen3.8 27B measured |
| LMArena Vision: humor | Not measured | 1235 | Only Qwen3.8 27B measured |
| LMArena Vision: creative writing | Not measured | 1237 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 | Qwen3.8 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
Qwen3.8 27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.8 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 | Qwen3.8 27B |
|---|---|---|
| Maker | DeepSeek | Qwen |
| Weights | Open weights | Open weights |
| Released | December 1, 2025 | August 14, 2026 |
| Context window | 164K | 1M |
| Input $/Mtok | $0.28 | $0.04 |
| Output $/Mtok | $0.42 | $1.35 |
| Coding index | 54.4 | 73.6 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $0.99 | $1.26 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 better than Qwen3.8 27B for coding?
Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 54.4 for DeepSeek V3.2. Pick DeepSeek V3.2 when output tokens cost 3.2x less, or when a solved task costs $0.99 against $1.26.
Which is cheaper, DeepSeek V3.2 or Qwen3.8 27B?
DeepSeek V3.2 costs $0.28 per million input tokens and $0.42 per million output tokens; Qwen3.8 27B costs $0.04 and $1.35. Per solved SWE-bench Verified task, DeepSeek V3.2 costs $0.99 and Qwen3.8 27B $1.26.
Which has the bigger context window, DeepSeek V3.2 or Qwen3.8 27B?
Qwen3.8 27B, with 1M tokens against 164K.
How many benchmarks is the DeepSeek V3.2 vs Qwen3.8 27B comparison based on?
49 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2's coding index rests on 2 independent benchmarks and Qwen3.8 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.