DeepSeek V3 0324 vs Qwen3.8 27B
Updated October 21, 2026. 14 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 42.0 for DeepSeek V3 0324.
Pick DeepSeek V3 0324 when output tokens cost 1.2x less, or when the benchmark that matters is TaxEval (Vals AI), where it leads.
- Shared results
- 14
- DeepSeek V3 0324 only
- 21
- Qwen3.8 27B only
- 71
- Like-for-like categories
- 6 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 42.0.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.8 27B
Qwen3.8 27B leads, 33.7 against 9.7.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 27B
Qwen3.8 27B leads, 45.8 against 0.8.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.8 27B
Qwen3.8 27B leads, 88.9% against 61.6%.
Same board, both measured
Math
AIME
No call
Only DeepSeek V3 0324 has a AIME result, 52.2%.
Directional only
Knowledge
MMLU Pro
Qwen3.8 27B
Qwen3.8 27B leads, 84.3% against 79.5%.
Same board, both measured
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
Qwen3.8 27B
DeepSeek V3 0324 $0.0009, Qwen3.8 27B $0.0007 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.8 27B has the number, $1.26.
Directional only
At a glance
- Coding index
- DeepSeek V3 0324 42.0 from 1 benchmark, Qwen3.8 27B 73.6 from 2 benchmarks.
- Widest category
- Agentic work, 45.0 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Coding Index: DeepSeek V3 0324 21.2 against Qwen3.8 27B 68.1, 46.9 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3 0324 $1.14 against Qwen3.8 27B $1.35 per million output tokens, 1.2x apart.
- Context window
- DeepSeek V3 0324 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 0324 | Bars | Qwen3.8 27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| Knowledge | 1 | 2 | ||
| General | 0 | 3 | ||
| All shared boards | 1 | 13 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index03
| Benchmark | DeepSeek V3 0324 | Qwen3.8 27B | Leader |
|---|---|---|---|
| SWE-bench Verified | 42.0% | Not measured | Only DeepSeek V3 0324 measured |
| LiveBench coding | Not measured | 86.8% | Only Qwen3.8 27B measured |
| LMArena WebDev | Not measured | 1593 | Only Qwen3.8 27B measured |
| AA Coding Index | 21.2 | 68.1 | Qwen3.8 27B |
| LiveCodeBench (Vals AI) | 65.5% | 84.0% | Qwen3.8 27B |
| SWE-bench Verified (Vals AI) | Not measured | 86.0% | Only Qwen3.8 27B measured |
| Vibe Code Bench (Vals AI) | Not measured | 64.8% | Only Qwen3.8 27B measured |
| 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) | 40.5% | Not measured | Only DeepSeek V3 0324 measured |
| SciCode | 39.0% | 46.6% | Qwen3.8 27B |
| Terminal-Bench Hard (Artificial Analysis run) | 15.2% | Not measured | Only DeepSeek V3 0324 measured |
| WeirdML | 36.1% | Not measured | Only DeepSeek V3 0324 measured |
| LMArena Text: coding | Not measured | 1483 | Only Qwen3.8 27B measured |
| LMArena Text: software and IT | Not measured | 1478 | Only Qwen3.8 27B measured |
| LMArena WebDev: React | Not measured | 1602 | Only Qwen3.8 27B measured |
| LMArena WebDev: HTML | Not measured | 1570 | Only Qwen3.8 27B measured |
| LMArena WebDev: from an image | Not measured | 1577 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3 0324 | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 9.7 | 33.7 | Qwen3.8 27B |
| Epoch Capabilities Index | Not measured | 151.7 | Only Qwen3.8 27B measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3 0324 | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Agentic Index | 0.8 | 45.8 | Qwen3.8 27B |
| 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 | 47.1% | Not measured | Only DeepSeek V3 0324 measured |
| APEX-Agents | Not measured | 47.5% | Only Qwen3.8 27B measured |
| METR time horizons | 49.6% | Not measured | Only DeepSeek V3 0324 measured |
| Neither model has a result on the other 27 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | DeepSeek V3 0324 | Qwen3.8 27B | Leader |
|---|---|---|---|
| GPQA Diamond | 61.6% | 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) | 65.5% | 90.5% | Qwen3.8 27B |
| Humanity's Last Exam (Artificial Analysis run) | 4.7% | 33.9% | Qwen3.8 27B |
| GPQA Diamond (Epoch AI run) | 67.6% | Not measured | Only DeepSeek V3 0324 measured |
| ARC-AGI | Not measured | 87.5% | Only Qwen3.8 27B measured |
| ARC-AGI-2 | Not measured | 42.4% | Only Qwen3.8 27B measured |
| SimpleBench | 27.2% | Not measured | Only DeepSeek V3 0324 measured |
| LMArena Text: hard prompts | Not measured | 1459 | Only Qwen3.8 27B measured |
| LMArena Text: expert | Not measured | 1479 | Only Qwen3.8 27B measured |
| LMArena Text: hard prompts in English | Not measured | 1461 | Only Qwen3.8 27B measured |
| 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 7 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | DeepSeek V3 0324 | Qwen3.8 27B | Leader |
|---|---|---|---|
| AIME | 52.2% | Not measured | Only DeepSeek V3 0324 measured |
| MATH 500 (Vals AI) | 88.6% | Not measured | Only DeepSeek V3 0324 measured |
| MGSM (Vals AI) | 91.7% | Not measured | Only DeepSeek V3 0324 measured |
| AA Math Index | 41.0 | Not measured | Only DeepSeek V3 0324 measured |
| AIME 2025 (Artificial Analysis run) | 41.0% | Not measured | Only DeepSeek V3 0324 measured |
| OTIS Mock AIME | 37.8% | Not measured | Only DeepSeek V3 0324 measured |
| ProofBench | Not measured | 16.0% | Only Qwen3.8 27B measured |
| MATH level 5 | 75.5% | Not measured | Only DeepSeek V3 0324 measured |
| LMArena Text: math | Not measured | 1455 | Only Qwen3.8 27B measured |
| LMArena Text: mathematical work | Not measured | 1458 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | DeepSeek V3 0324 | Qwen3.8 27B | Leader |
|---|---|---|---|
| MMLU Pro | 79.5% | 84.3% | Qwen3.8 27B |
| LegalBench (Vals AI) | 77.7% | 82.4% | Qwen3.8 27B |
| CorpFin (Vals AI) | 54.7% | Not measured | Only DeepSeek V3 0324 measured |
| TaxEval (Vals AI) | 71.1% | 70.8% | DeepSeek V3 0324 |
| MortgageTax (Vals AI) | Not measured | 64.9% | Only Qwen3.8 27B measured |
| MedQA (Vals AI) | 82.0% | Not measured | Only DeepSeek V3 0324 measured |
| MedCode (Vals AI) | Not measured | 28.7% | Only Qwen3.8 27B measured |
| MMLU Pro (Artificial Analysis run) | 81.9% | Not measured | Only DeepSeek V3 0324 measured |
| LMArena Text: science | Not measured | 1467 | Only Qwen3.8 27B measured |
| LMArena Text: medicine and healthcare | Not measured | 1437 | Only Qwen3.8 27B measured |
| LMArena Text: legal and government | Not measured | 1447 | Only Qwen3.8 27B measured |
| LMArena Text: business and finance | Not measured | 1438 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3 0324 | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench design | Not measured | 75.9% | Only Qwen3.8 27B measured |
| Neither model has a result on the other 8 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | DeepSeek V3 0324 | 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 | 41.0% | Not measured | Only DeepSeek V3 0324 measured |
| Long context reasoning (AA-LCR) | 40.7% | 82.0% | Qwen3.8 27B |
| DTBench | 64.8% | 88.0% | Qwen3.8 27B |
| LMCA | 15.5% | 41.4% | Qwen3.8 27B |
| Fiction.LiveBench | 50.0% | Not measured | Only DeepSeek V3 0324 measured |
| Creative writing (Lech Mazur) | 77.0% | Not measured | Only DeepSeek V3 0324 measured |
| Surface Evolver Bench | Not measured | 45.0% | Only Qwen3.8 27B measured |
| LMArena Text | Not measured | 1440 | Only Qwen3.8 27B measured |
| LMArena Text: instruction following | Not measured | 1436 | Only Qwen3.8 27B measured |
| LMArena Text: creative writing | Not measured | 1383 | Only Qwen3.8 27B measured |
| LMArena Vision | Not measured | 1269 | Only Qwen3.8 27B measured |
| LMArena Text: English | Not measured | 1448 | Only Qwen3.8 27B measured |
| LMArena Text: non-English | Not measured | 1428 | Only Qwen3.8 27B measured |
| LMArena Text: Chinese | Not measured | 1501 | Only Qwen3.8 27B measured |
| LMArena Text: German | Not measured | 1443 | Only Qwen3.8 27B measured |
| LMArena Text: Japanese | Not measured | 1387 | Only Qwen3.8 27B measured |
| LMArena Text: Korean | Not measured | 1384 | Only Qwen3.8 27B measured |
| LMArena Text: French | Not measured | 1465 | Only Qwen3.8 27B measured |
| LMArena Text: Spanish | Not measured | 1449 | Only Qwen3.8 27B measured |
| LMArena Text: Russian | Not measured | 1415 | Only Qwen3.8 27B measured |
| LMArena Text: Polish | Not measured | 1439 | Only Qwen3.8 27B measured |
| LMArena Text: multi-turn | Not measured | 1440 | Only Qwen3.8 27B measured |
| LMArena Text: longer queries | Not measured | 1448 | Only Qwen3.8 27B measured |
| LMArena Text: writing and language | Not measured | 1405 | Only Qwen3.8 27B measured |
| LMArena Text: entertainment and media | Not measured | 1393 | Only Qwen3.8 27B measured |
| 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 0324 | 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
Qwen3.8 27B 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 0324 | Qwen3.8 27B |
|---|---|---|
| Maker | DeepSeek | Qwen |
| Weights | Open weights | Open weights |
| Released | March 24, 2025 | August 14, 2026 |
| Context window | 164K | 1M |
| Input $/Mtok | $0.29 | $0.04 |
| Output $/Mtok | $1.14 | $1.35 |
| Coding index | 42.0 | 73.6 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | Not computable | $1.26 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3 0324 better than Qwen3.8 27B for coding?
Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 42.0 for DeepSeek V3 0324. Pick DeepSeek V3 0324 when output tokens cost 1.2x less, or when the benchmark that matters is TaxEval (Vals AI), where it leads.
Which is cheaper, DeepSeek V3 0324 or Qwen3.8 27B?
DeepSeek V3 0324 costs $0.29 per million input tokens and $1.14 per million output tokens; Qwen3.8 27B costs $0.04 and $1.35. Cost per solved task is known for Qwen3.8 27B only.
Which has the bigger context window, DeepSeek V3 0324 or Qwen3.8 27B?
Qwen3.8 27B, with 1M tokens against 164K.
How many benchmarks is the DeepSeek V3 0324 vs Qwen3.8 27B comparison based on?
14 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3 0324's coding index rests on 1 independent benchmark and Qwen3.8 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.