DeepSeek V4 Flash 0731 vs Qwen3.8 27B
Qwen3.8 27B leads DeepSeek V4 Flash 0731 on coding, 74.2 to 51.8 on the Vibe Coding Index, and DeepSeek V4 Flash 0731 costs 6.4x less per output token ($0.396 vs $2.55 per million). Updated October 22, 2026; 33 benchmarks have a result for both.
DeepSeek V4 Flash 0731 or Qwen3.8 27B: which to pick
Pick Qwen3.8 27B for coding: it scores 74.2 on the Vibe Coding Index against 51.8 for DeepSeek V4 Flash 0731.
Pick DeepSeek V4 Flash 0731 for output tokens 6.4x cheaper ($0.396 vs $2.55 per million) and a lower cost per solved task ($0.01 against $1.26).
- Shared results
- 33
- DeepSeek V4 Flash 0731 only
- 22
- Qwen3.8 27B only
- 52
- 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
Qwen3.8 27B
Qwen3.8 27B scores 74.2 against 51.8.
4 benchmarks behind each score
Intelligence
AA Intelligence Index
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 leads, 34.3 against 33.7.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 27B
Qwen3.8 27B leads, 45.8 against 41.0.
Same board, both measured
Reasoning
GPQA Diamond
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 leads, 89.9% against 88.9%.
Same board, both measured
Knowledge
MMLU Pro
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 leads, 86.2% against 84.3%.
Same board, both measured
Web design
Design Arena: websites
No call
Only DeepSeek V4 Flash 0731 has a Design Arena: websites result, 1237.
Directional only
Long documents
Listed context window
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 $0.0003, Qwen3.8 27B $0.0017 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 $0.01, Qwen3.8 27B $1.26.
Published attempt cost and pass rate
At a glance
- Coding index
- DeepSeek V4 Flash 0731 51.8 from 4 benchmarks, Qwen3.8 27B 74.2 from 4 benchmarks.
- Widest category
- Math, 40.0 points apart on average over 1 shared benchmark.
- Widest single gap
- ProofBench: DeepSeek V4 Flash 0731 56.0% against Qwen3.8 27B 16.0%, 40.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Flash 0731 $0.396 against Qwen3.8 27B $2.55 per million output tokens, 6.4x apart.
- Context window
- DeepSeek V4 Flash 0731 takes 1.05M 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 V4 Flash 0731 | Bars | Qwen3.8 27B | Ties |
|---|---|---|---|---|
| Coding | 9 | 1 | 1 tie | |
| Intelligence | 2 | 0 | ||
| Agentic work | 2 | 4 | ||
| Reasoning | 5 | 0 | ||
| Math | 1 | 0 | ||
| Knowledge | 2 | 2 | ||
| General | 2 | 2 | ||
| All shared boards | 23 | 9 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index91
| Benchmark | DeepSeek V4 Flash 0731 | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 86.8% | Only Qwen3.8 27B measured |
| LMArena WebDev | Not measured | 1593 | Only Qwen3.8 27B measured |
| AA Coding Index | 69.1 | 68.1 | DeepSeek V4 Flash 0731 |
| LiveCodeBench (Vals AI) | 87.3% | 84.0% | DeepSeek V4 Flash 0731 |
| SWE-bench Verified (Vals AI) | 88.8% | 86.0% | DeepSeek V4 Flash 0731 |
| Terminal-Bench 4 (Vals AI) | 18.7% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Vibe Code Bench (Vals AI) | 74.7% | 64.8% | DeepSeek V4 Flash 0731 |
| Code migration (Vals AI) | 38.6% | 14.2% | DeepSeek V4 Flash 0731 |
| ProgramBench (Vals AI) | 0.0% | 0.0% | Tie |
| IOI (Vals AI) | 32.7% | 39.1% | Qwen3.8 27B |
| Terminal-Bench 2.1 (Vals AI) | 67.0% | 58.4% | DeepSeek V4 Flash 0731 |
| SkillsBench (Vals AI) | 50.7% | 38.1% | DeepSeek V4 Flash 0731 |
| CyberBench (Vals AI) | 72.1% | 67.8% | DeepSeek V4 Flash 0731 |
| SciCode | 50.3% | 46.6% | DeepSeek V4 Flash 0731 |
| FrontierCode | 18.8% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| WeirdML | 63.0% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| ALE-Bench | 1306.1 | Not measured | Only DeepSeek V4 Flash 0731 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 17 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Flash 0731 | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 34.3 | 33.7 | DeepSeek V4 Flash 0731 |
| Epoch Capabilities Index | 154.8 | 151.7 | DeepSeek V4 Flash 0731 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | DeepSeek V4 Flash 0731 | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Agentic Index | 41.0 | 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) | 49.5% | 48.5% | DeepSeek V4 Flash 0731 |
| Tax Agent Bench (Vals AI) | 28.1% | 30.9% | Qwen3.8 27B |
| Legal Research Bench (Vals AI) | 30.3% | 36.1% | Qwen3.8 27B |
| Excel Modeling Benchmark (Vals AI) | 57.0% | 59.7% | Qwen3.8 27B |
| BioMysteryBench (Vals AI) | 64.4% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Terminal-Bench-Science (Vals AI) | 4.3% | 0.0% | DeepSeek V4 Flash 0731 |
| APEX-Agents | Not measured | 47.5% | Only Qwen3.8 27B measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V4 Flash 0731 | Qwen3.8 27B | Leader |
|---|---|---|---|
| GPQA Diamond | 89.9% | 88.9% | DeepSeek V4 Flash 0731 |
| 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) | 90.8% | 90.5% | DeepSeek V4 Flash 0731 |
| Humanity's Last Exam (Artificial Analysis run) | 38.6% | 33.9% | DeepSeek V4 Flash 0731 |
| GPQA Diamond (Epoch AI run) | 91.0% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| ARC-AGI | 89.0% | 87.5% | DeepSeek V4 Flash 0731 |
| ARC-AGI-2 | 61.4% | 42.4% | DeepSeek V4 Flash 0731 |
| SimpleBench | 61.1% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Chess puzzles | 32.0% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Mystery game puzzles | 34.0% | Not measured | Only DeepSeek V4 Flash 0731 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 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4 Flash 0731 | Qwen3.8 27B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 94.4% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| FrontierMath, tiers 1 to 3 | 57.5% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| FrontierMath, tier 4 | 24.4% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| ProofBench | 56.0% | 16.0% | DeepSeek V4 Flash 0731 |
| 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 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | DeepSeek V4 Flash 0731 | Qwen3.8 27B | Leader |
|---|---|---|---|
| MMLU Pro | 86.2% | 84.3% | DeepSeek V4 Flash 0731 |
| LegalBench (Vals AI) | 77.7% | 82.4% | Qwen3.8 27B |
| CorpFin (Vals AI) | 61.9% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| TaxEval (Vals AI) | 70.7% | 70.8% | Qwen3.8 27B |
| MortgageTax (Vals AI) | Not measured | 64.9% | Only Qwen3.8 27B measured |
| MedCode (Vals AI) | 41.4% | 28.7% | DeepSeek V4 Flash 0731 |
| SimpleQA Verified | 33.6% | Not measured | Only DeepSeek V4 Flash 0731 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 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | DeepSeek V4 Flash 0731 | Qwen3.8 27B | Leader |
|---|---|---|---|
| Design Arena: websites | 1237 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Design Arena: UI components | 1233 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Design Arena: game dev | 1207 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Design Arena: data viz | 1180 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Design Arena: 3D | 1208 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Design Arena: all code categories | 1229 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Design Arena: SVG | 1184 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Design Arena: ASCII art | 1090 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| LiveBench design | Not measured | 75.9% | Only Qwen3.8 27B measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | DeepSeek V4 Flash 0731 | 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) | 80.4% | 83.8% | Qwen3.8 27B |
| Long context reasoning (AA-LCR) | 79.7% | 82.0% | Qwen3.8 27B |
| DTBench | 90.9% | 88.0% | DeepSeek V4 Flash 0731 |
| LMCA | 41.7% | 41.4% | DeepSeek V4 Flash 0731 |
| 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 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Flash 0731 | 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 V4 Flash 0731 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4 Flash 0731 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4 Flash 0731 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 V4 Flash 0731 | Qwen3.8 27B |
|---|---|---|
| Maker | DeepSeek | Qwen |
| Weights | Open weights | Open weights |
| Released | July 31, 2026 | August 14, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $0.14 | $0.425 |
| Output $/Mtok | $0.396 | $2.55 |
| Coding index | 51.8 | 74.2 |
| Independent measurements behind it | 4 | 4 |
| Cost per solved task | $0.01 | $1.26 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Flash 0731 better than Qwen3.8 27B for coding?
Pick Qwen3.8 27B for coding: it scores 74.2 on the Vibe Coding Index against 51.8 for DeepSeek V4 Flash 0731. Pick DeepSeek V4 Flash 0731 for output tokens 6.4x cheaper ($0.396 vs $2.55 per million) and a lower cost per solved task ($0.01 against $1.26).
Which is cheaper, DeepSeek V4 Flash 0731 or Qwen3.8 27B?
DeepSeek V4 Flash 0731 costs $0.14 per million input tokens and $0.396 per million output tokens; Qwen3.8 27B costs $0.425 and $2.55. Per solved SWE-bench Verified task, DeepSeek V4 Flash 0731 costs $0.01 and Qwen3.8 27B $1.26.
Which has the bigger context window, DeepSeek V4 Flash 0731 or Qwen3.8 27B?
DeepSeek V4 Flash 0731, with 1.05M tokens against 1M.
How many benchmarks is the DeepSeek V4 Flash 0731 vs Qwen3.8 27B comparison based on?
33 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Flash 0731's coding index rests on 4 independent benchmarks and Qwen3.8 27B's on 4. Where only one model has a result, the page says so instead of naming a winner.