DeepSeek V4 Pro 0423 vs Qwen3.5 397B A17B
DeepSeek V4 Pro 0423 leads Qwen3.5 397B A17B on the Artificial Analysis Intelligence Index, 30.4 to 21.4, at about the same output price ($2.716 vs $2.34 per million). Updated October 22, 2026; 47 benchmarks have a result for both.
DeepSeek V4 Pro 0423 or Qwen3.5 397B A17B: which to pick
Only DeepSeek V4 Pro 0423 has a coding score, 38.7 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 output tokens 1.2x cheaper ($2.34 vs $2.716 per million) and work measured by Mystery game puzzles or LMArena Text: math, where it leads.
- Shared results
- 47
- DeepSeek V4 Pro 0423 only
- 37
- Qwen3.5 397B A17B only
- 18
- 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 0423 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 30.4 against 21.4.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Pro 0423 has a AA Agentic Index result, 26.3.
Directional only
Reasoning
GPQA Diamond
No call
Only DeepSeek V4 Pro 0423 has a GPQA Diamond result, 89.4%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V4 Pro 0423 has a MMLU Pro result, 87.3%.
Directional only
Web design
Design Arena: websites
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 1234 against 1190.
Same board, both measured
Long documents
Listed context window
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5 397B A17B
DeepSeek V4 Pro 0423 $0.0026, 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 0423 has the number, $0.57.
Directional only
At a glance
- Coding index
- DeepSeek V4 Pro 0423 38.7 from 5 benchmarks, Qwen3.5 397B A17B not benchmarked.
- Widest category
- Web design, 6.6 points apart on average over 7 shared benchmarks.
- Widest single gap
- FrontierMath, tiers 1 to 3: DeepSeek V4 Pro 0423 45.3% against Qwen3.5 397B A17B 31.2%, 14.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0423 $2.716 against Qwen3.5 397B A17B $2.34 per million output tokens, 1.2x apart.
- Context window
- DeepSeek V4 Pro 0423 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 0423 | Bars | Qwen3.5 397B A17B | Ties |
|---|---|---|---|---|
| Coding | 6 | 0 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 4 | 1 | ||
| Math | 3 | 1 | ||
| Knowledge | 4 | 0 | ||
| Web design | 6 | 0 | 1 tie | |
| General | 18 | 1 | ||
| All shared boards | 43 | 3 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index60
| Benchmark | DeepSeek V4 Pro 0423 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| LMArena WebDev | 1447 | 1400 | DeepSeek V4 Pro 0423 |
| AA Coding Index | 59.4 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveCodeBench (Vals AI) | 87.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SWE-bench Verified (Vals AI) | 77.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Terminal-Bench 4 (Vals AI) | 11.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Vibe Code Bench (Vals AI) | 49.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Code migration (Vals AI) | 26.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Terminal-Bench 2.1 (Vals AI) | 50.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SkillsBench (Vals AI) | 51.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SciCode | Not measured | 44.8% | Only Qwen3.5 397B A17B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 40.9% | Only Qwen3.5 397B A17B measured |
| FrontierCode | 17.6% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| WeirdML | 48.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| ALE-Bench | 1006.1 | 621.9 | DeepSeek V4 Pro 0423 |
| LMArena Text: coding | 1470 | 1464 | DeepSeek V4 Pro 0423 |
| LMArena Text: software and IT | 1469 | 1463 | DeepSeek V4 Pro 0423 |
| LMArena WebDev: React | 1441 | 1388 | DeepSeek V4 Pro 0423 |
| LMArena WebDev: HTML | 1435 | 1410 | DeepSeek V4 Pro 0423 |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Pro 0423 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 21.4 | DeepSeek V4 Pro 0423 |
| Epoch Capabilities Index | 149.1 | 146.8 | DeepSeek V4 Pro 0423 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building web apps | 1000 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building full-stack apps | 948 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building Godot games | 1059 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Finance Agent (Vals AI) | 44.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Tax Agent Bench (Vals AI) | 25.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Legal Research Bench (Vals AI) | 23.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Excel Modeling Benchmark (Vals AI) | 51.6% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| τ²-Bench Telecom | Not measured | 95.6% | Only Qwen3.5 397B A17B measured |
| APEX-Agents | Not measured | 24.9% | Only Qwen3.5 397B A17B measured |
| Vending-Bench 2 | $3,285 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | DeepSeek V4 Pro 0423 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| GPQA Diamond | 89.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 89.3% | Only Qwen3.5 397B A17B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 29.0% | Only Qwen3.5 397B A17B measured |
| GPQA Diamond (Epoch AI run) | 90.9% | 86.4% | DeepSeek V4 Pro 0423 |
| Chess puzzles | 19.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Mystery game puzzles | 17.0% | 18.0% | Qwen3.5 397B A17B |
| LMArena Text: hard prompts | 1461 | 1448 | DeepSeek V4 Pro 0423 |
| LMArena Text: expert | 1463 | 1462 | DeepSeek V4 Pro 0423 |
| LMArena Text: hard prompts in English | 1466 | 1450 | DeepSeek V4 Pro 0423 |
| LMArena Vision: diagrams | Not measured | 1277 | Only Qwen3.5 397B A17B measured |
| LMArena Vision: homework | Not measured | 1292 | Only Qwen3.5 397B A17B measured |
| Kagi LLM Benchmark | 53.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | DeepSeek V4 Pro 0423 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 96.7% | 88.9% | DeepSeek V4 Pro 0423 |
| FrontierMath, tiers 1 to 3 | 45.3% | 31.2% | DeepSeek V4 Pro 0423 |
| FrontierMath, tier 4 | 2.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| ProofBench | 16.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: math | 1443 | 1453 | Qwen3.5 397B A17B |
| LMArena Text: mathematical work | 1455 | 1454 | DeepSeek V4 Pro 0423 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V4 Pro 0423 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| MMLU Pro | 87.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LegalBench (Vals AI) | 80.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CaseLaw (Vals AI) | 59.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CorpFin (Vals AI) | 61.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| TaxEval (Vals AI) | 72.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| MedCode (Vals AI) | 40.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SimpleQA Verified | 47.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: science | 1470 | 1457 | DeepSeek V4 Pro 0423 |
| LMArena Text: medicine and healthcare | 1466 | 1455 | DeepSeek V4 Pro 0423 |
| LMArena Text: legal and government | 1464 | 1441 | DeepSeek V4 Pro 0423 |
| LMArena Text: business and finance | 1443 | 1435 | DeepSeek V4 Pro 0423 |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | DeepSeek V4 Pro 0423 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| Design Arena: websites | 1234 | 1190 | DeepSeek V4 Pro 0423 |
| Design Arena: UI components | 1219 | 1161 | DeepSeek V4 Pro 0423 |
| Design Arena: game dev | 1228 | 1140 | DeepSeek V4 Pro 0423 |
| Design Arena: data viz | 1203 | 1175 | DeepSeek V4 Pro 0423 |
| Design Arena: 3D | 1253 | 1160 | DeepSeek V4 Pro 0423 |
| Design Arena: all code categories | 1240 | 1179 | DeepSeek V4 Pro 0423 |
| Design Arena: SVG | 1139 | 1139 | Tie |
| Design Arena: ASCII art | 1149 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index181
| Benchmark | DeepSeek V4 Pro 0423 | Qwen3.5 397B A17B | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 62.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| MedScribe (Vals AI) | 75.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| IFBench | Not measured | 78.8% | Only Qwen3.5 397B A17B measured |
| Long context reasoning (AA-LCR) | Not measured | 77.3% | Only Qwen3.5 397B A17B measured |
| DTBench | 90.7% | 87.5% | DeepSeek V4 Pro 0423 |
| LMCA | 41.2% | 37.9% | DeepSeek V4 Pro 0423 |
| Surface Evolver Bench | 40.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CL-bench Life | 13.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text | 1451 | 1437 | DeepSeek V4 Pro 0423 |
| LMArena Text: instruction following | 1445 | 1424 | DeepSeek V4 Pro 0423 |
| LMArena Text: creative writing | 1446 | 1402 | DeepSeek V4 Pro 0423 |
| LMArena Vision | Not measured | 1262 | Only Qwen3.5 397B A17B measured |
| LMArena Text: English | 1459 | 1443 | DeepSeek V4 Pro 0423 |
| LMArena Text: non-English | 1439 | 1430 | DeepSeek V4 Pro 0423 |
| LMArena Text: Chinese | 1481 | 1498 | Qwen3.5 397B A17B |
| LMArena Text: German | 1458 | 1445 | DeepSeek V4 Pro 0423 |
| LMArena Text: Japanese | 1446 | 1429 | DeepSeek V4 Pro 0423 |
| LMArena Text: Korean | 1409 | 1384 | DeepSeek V4 Pro 0423 |
| LMArena Text: French | 1473 | 1462 | DeepSeek V4 Pro 0423 |
| LMArena Text: Spanish | 1457 | 1441 | DeepSeek V4 Pro 0423 |
| LMArena Text: Russian | 1453 | 1430 | DeepSeek V4 Pro 0423 |
| LMArena Text: Polish | 1461 | 1447 | DeepSeek V4 Pro 0423 |
| LMArena Text: multi-turn | 1468 | 1446 | DeepSeek V4 Pro 0423 |
| LMArena Text: longer queries | 1458 | 1441 | DeepSeek V4 Pro 0423 |
| LMArena Text: writing and language | 1447 | 1415 | DeepSeek V4 Pro 0423 |
| LMArena Text: entertainment and media | 1431 | 1399 | DeepSeek V4 Pro 0423 |
| 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 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | 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 0423 | Qwen3.5 397B A17B |
|---|---|---|
| Maker | DeepSeek | Qwen |
| Weights | Open weights | Open weights |
| Released | April 24, 2026 | February 16, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $1.225 | $0.39 |
| Output $/Mtok | $2.716 | $2.34 |
| Coding index | 38.7 | Not benchmarked |
| Independent measurements behind it | 5 | 1 |
| Cost per solved task | $0.57 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0423 better than Qwen3.5 397B A17B for coding?
Only DeepSeek V4 Pro 0423 has a coding score, 38.7 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 output tokens 1.2x cheaper ($2.34 vs $2.716 per million) and work measured by Mystery game puzzles or LMArena Text: math, where it leads.
Which is cheaper, DeepSeek V4 Pro 0423 or Qwen3.5 397B A17B?
DeepSeek V4 Pro 0423 costs $1.225 per million input tokens and $2.716 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for DeepSeek V4 Pro 0423 only.
Which has the bigger context window, DeepSeek V4 Pro 0423 or Qwen3.5 397B A17B?
DeepSeek V4 Pro 0423, with 1.05M tokens against 262K.
How many benchmarks is the DeepSeek V4 Pro 0423 vs Qwen3.5 397B A17B comparison based on?
47 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Pro 0423'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.