DeepSeek V4 Pro 0813 vs Qwen3.5 397B A17B
DeepSeek V4 Pro 0813 leads Qwen3.5 397B A17B on the Artificial Analysis Intelligence Index, 36.0 to 21.4, at about the same output price ($1.98 vs $2.34 per million). Updated October 22, 2026; 42 benchmarks have a result for both.
DeepSeek V4 Pro 0813 or Qwen3.5 397B A17B: which to pick
Only DeepSeek V4 Pro 0813 has a coding score, 56.1 on the Vibe Coding Index; Qwen3.5 397B A17B has none, so the two cannot be ranked on coding.
Pick Qwen3.5 397B A17B for work measured by LMArena Text: software and IT or LMArena Text: expert, where it leads.
- Shared results
- 42
- DeepSeek V4 Pro 0813 only
- 36
- Qwen3.5 397B A17B only
- 24
- 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
No call
Only DeepSeek V4 Pro 0813 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 36.0 against 21.4.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Pro 0813 has a AA Agentic Index result, 41.3.
Directional only
Reasoning
GPQA Diamond
No call
Only DeepSeek V4 Pro 0813 has a GPQA Diamond result, 92.4%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V4 Pro 0813 has a MMLU Pro result, 87.0%.
Directional only
Web design
Design Arena: websites
No call
Only Qwen3.5 397B A17B has a Design Arena: websites result, 1190.
Directional only
General
Vals Index
No call
Only DeepSeek V4 Pro 0813 has a Vals Index result, 53.0%.
Directional only
Long documents
Listed context window
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5 397B A17B
DeepSeek V4 Pro 0813 $0.0016, Qwen3.5 397B A17B $0.0016 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only DeepSeek V4 Pro 0813 has the number, $0.10.
Directional only
At a glance
- Coding index
- DeepSeek V4 Pro 0813 56.1 from 5 benchmarks, Qwen3.5 397B A17B not benchmarked.
- Widest category
- Agentic work, 22.4 points apart on average over 1 shared benchmark.
- Widest single gap
- LMArena WebDev: React: DeepSeek V4 Pro 0813 1588 against Qwen3.5 397B A17B 1388, 33.4 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0813 $1.98 against Qwen3.5 397B A17B $2.34 per million output tokens, 1.2x apart.
- Context window
- DeepSeek V4 Pro 0813 takes 1.05M tokens, Qwen3.5 397B A17B 262K.
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 Pro 0813 | Bars | Qwen3.5 397B A17B | Ties |
|---|---|---|---|---|
| Coding | 6 | 1 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 6 | 1 | ||
| Math | 2 | 2 | ||
| Knowledge | 2 | 2 | ||
| General | 14 | 3 | ||
| All shared boards | 33 | 9 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index61
| Benchmark | DeepSeek V4 Pro 0813 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| LMArena WebDev | 1582 | 1400 | DeepSeek V4 Pro 0813 |
| AA Coding Index | 68.8 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LiveCodeBench (Vals AI) | 87.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SWE-bench Verified (Vals AI) | 96.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench 4 (Vals AI) | 14.1% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Vibe Code Bench (Vals AI) | 82.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Code migration (Vals AI) | 41.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| IOI (Vals AI) | 51.6% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench 2.1 (Vals AI) | 54.7% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Vibe Code Bench 1-100 (Vals AI) | 17.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SkillsBench (Vals AI) | 53.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| CyberBench (Vals AI) | 72.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SciCode | 51.0% | 44.8% | DeepSeek V4 Pro 0813 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 40.9% | Only Qwen3.5 397B A17B measured |
| FrontierCode | 28.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| WeirdML | 66.2% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ALE-Bench | 1403.2 | 621.9 | DeepSeek V4 Pro 0813 |
| LMArena Text: coding | 1469 | 1464 | DeepSeek V4 Pro 0813 |
| LMArena Text: software and IT | 1462 | 1463 | Qwen3.5 397B A17B |
| LMArena WebDev: React | 1588 | 1388 | DeepSeek V4 Pro 0813 |
| LMArena WebDev: HTML | 1566 | 1410 | DeepSeek V4 Pro 0813 |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Pro 0813 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| AA Intelligence Index | 36.0 | 21.4 | DeepSeek V4 Pro 0813 |
| Epoch Capabilities Index | 155.6 | 146.8 | DeepSeek V4 Pro 0813 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4 Pro 0813 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| AA Agentic Index | 41.3 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SREBench (Vals AI) | 1.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Finance Agent (Vals AI) | 50.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Tax Agent Bench (Vals AI) | 26.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Legal Research Bench (Vals AI) | 40.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 10.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Excel Modeling Benchmark (Vals AI) | 52.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench-Science (Vals AI) | 4.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| τ²-Bench Telecom | Not measured | 95.6% | Only Qwen3.5 397B A17B measured |
| APEX-Agents | 47.3% | 24.9% | DeepSeek V4 Pro 0813 |
| Neither model has a result on the other 32 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index61
| Benchmark | DeepSeek V4 Pro 0813 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| GPQA Diamond | 92.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| GPQA Diamond (Artificial Analysis run) | 92.8% | 89.3% | DeepSeek V4 Pro 0813 |
| Humanity's Last Exam (Artificial Analysis run) | 41.0% | 29.0% | DeepSeek V4 Pro 0813 |
| GPQA Diamond (Epoch AI run) | 91.7% | 86.4% | DeepSeek V4 Pro 0813 |
| ARC-AGI | 90.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ARC-AGI-2 | 61.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Chess puzzles | 46.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Mystery game puzzles | 43.0% | 18.0% | DeepSeek V4 Pro 0813 |
| LMArena Text: hard prompts | 1458 | 1448 | DeepSeek V4 Pro 0813 |
| LMArena Text: expert | 1459 | 1462 | Qwen3.5 397B A17B |
| LMArena Text: hard prompts in English | 1463 | 1450 | DeepSeek V4 Pro 0813 |
| LMArena Vision: diagrams | Not measured | 1277 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: homework | Not measured | 1292 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | DeepSeek V4 Pro 0813 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.6% | 88.9% | DeepSeek V4 Pro 0813 |
| FrontierMath, tiers 1 to 3 | 64.6% | 31.2% | DeepSeek V4 Pro 0813 |
| FrontierMath, tier 4 | 26.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ProofBench | 50.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: math | 1441 | 1453 | Qwen3.5 397B A17B |
| LMArena Text: mathematical work | 1443 | 1454 | Qwen3.5 397B A17B |
| MathArena | Not measured | 19.2% | Only Qwen3.5 397B A17B 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 Pro 0813 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| MMLU Pro | 87.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LegalBench (Vals AI) | 82.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| CorpFin (Vals AI) | 65.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| TaxEval (Vals AI) | 73.1% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| MedCode (Vals AI) | 42.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SimpleQA Verified | 52.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: science | 1457 | 1457 | Qwen3.5 397B A17B |
| LMArena Text: medicine and healthcare | 1446 | 1455 | Qwen3.5 397B A17B |
| LMArena Text: legal and government | 1459 | 1441 | DeepSeek V4 Pro 0813 |
| LMArena Text: business and finance | 1442 | 1435 | DeepSeek V4 Pro 0813 |
| Neither model has a result on the other 10 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1190 | Only Qwen3.5 397B A17B measured |
| Design Arena: UI components | Not measured | 1161 | Only Qwen3.5 397B A17B measured |
| Design Arena: game dev | Not measured | 1140 | Only Qwen3.5 397B A17B measured |
| Design Arena: data viz | Not measured | 1175 | Only Qwen3.5 397B A17B measured |
| Design Arena: 3D | Not measured | 1160 | Only Qwen3.5 397B A17B measured |
| Design Arena: all code categories | Not measured | 1179 | Only Qwen3.5 397B A17B measured |
| Design Arena: SVG | Not measured | 1139 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index143
| Benchmark | DeepSeek V4 Pro 0813 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| Vals Index | 53.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| MedScribe (Vals AI) | 80.2% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| IFBench | Not measured | 78.8% | Only Qwen3.5 397B A17B measured |
| Long context reasoning (AA-LCR) | 80.3% | 77.3% | DeepSeek V4 Pro 0813 |
| DTBench | 93.9% | 87.5% | DeepSeek V4 Pro 0813 |
| LMCA | 45.5% | 37.9% | DeepSeek V4 Pro 0813 |
| LMArena Text | 1446 | 1437 | DeepSeek V4 Pro 0813 |
| LMArena Text: instruction following | 1448 | 1424 | DeepSeek V4 Pro 0813 |
| LMArena Text: creative writing | 1439 | 1402 | DeepSeek V4 Pro 0813 |
| LMArena Vision | Not measured | 1262 | Only Qwen3.5 397B A17B measured |
| LMArena Text: English | 1457 | 1443 | DeepSeek V4 Pro 0813 |
| LMArena Text: non-English | 1431 | 1430 | DeepSeek V4 Pro 0813 |
| LMArena Text: Chinese | 1481 | 1498 | Qwen3.5 397B A17B |
| LMArena Text: German | Not measured | 1445 | Only Qwen3.5 397B A17B measured |
| LMArena Text: Japanese | Not measured | 1429 | Only Qwen3.5 397B A17B measured |
| LMArena Text: Korean | 1449 | 1384 | DeepSeek V4 Pro 0813 |
| LMArena Text: French | 1446 | 1462 | Qwen3.5 397B A17B |
| LMArena Text: Spanish | 1435 | 1441 | Qwen3.5 397B A17B |
| LMArena Text: Russian | 1448 | 1430 | DeepSeek V4 Pro 0813 |
| LMArena Text: Polish | Not measured | 1447 | Only Qwen3.5 397B A17B measured |
| LMArena Text: multi-turn | 1454 | 1446 | DeepSeek V4 Pro 0813 |
| LMArena Text: longer queries | 1458 | 1441 | DeepSeek V4 Pro 0813 |
| LMArena Text: writing and language | 1443 | 1415 | DeepSeek V4 Pro 0813 |
| LMArena Text: entertainment and media | 1424 | 1399 | DeepSeek V4 Pro 0813 |
| LMArena Vision: English | Not measured | 1268 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: Chinese | Not measured | 1314 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: reading text | Not measured | 1271 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: humor | Not measured | 1262 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: captioning | Not measured | 1230 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: recognizing things | Not measured | 1265 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: creative writing | Not measured | 1260 | Only Qwen3.5 397B A17B measured |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | Qwen3.5 397B A17B | 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
Qwen3.5 397B A17B costs less
Repository review
50K input + 3K output tokens
Qwen3.5 397B A17B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5 397B A17B 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 Pro 0813 | Qwen3.5 397B A17B |
|---|---|---|
| Maker | DeepSeek | Qwen |
| Weights | Open weights | Open weights |
| Released | August 12, 2026 | February 16, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.66 | $0.39 |
| Output $/Mtok | $1.98 | $2.34 |
| Coding index | 56.1 | Not benchmarked |
| Independent measurements behind it | 5 | 1 |
| Cost per solved task | $0.10 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0813 better than Qwen3.5 397B A17B for coding?
Only DeepSeek V4 Pro 0813 has a coding score, 56.1 on the Vibe Coding Index; Qwen3.5 397B A17B has none, so the two cannot be ranked on coding. Pick Qwen3.5 397B A17B for work measured by LMArena Text: software and IT or LMArena Text: expert, where it leads.
Which is cheaper, DeepSeek V4 Pro 0813 or Qwen3.5 397B A17B?
DeepSeek V4 Pro 0813 costs $0.66 per million input tokens and $1.98 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for DeepSeek V4 Pro 0813 only.
Which has the bigger context window, DeepSeek V4 Pro 0813 or Qwen3.5 397B A17B?
DeepSeek V4 Pro 0813, with 1.05M tokens against 262K.
How many benchmarks is the DeepSeek V4 Pro 0813 vs Qwen3.5 397B A17B comparison based on?
42 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Pro 0813's coding index rests on 5 independent benchmarks and Qwen3.5 397B A17B's on 1. Where only one model has a result, the page says so instead of naming a winner.