DeepSeek V4 Pro 0423 vs Qwen3.7 Max
Updated October 21, 2026. 45 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Qwen3.7 Max for coding: it scores 45.1 on the Vibe Coding Index against 35.3 for DeepSeek V4 Pro 0423.
Pick DeepSeek V4 Pro 0423 when output tokens cost 2.3x less, or when a solved task costs $0.57 against $4.69.
- Shared results
- 45
- DeepSeek V4 Pro 0423 only
- 38
- Qwen3.7 Max only
- 14
- Like-for-like categories
- 8 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
Qwen3.7 Max
Qwen3.7 Max scores 45.1 against 35.3.
One benchmark behind each score
Intelligence
AA Intelligence Index
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 30.4 against 29.5.
Same board, both measured
Agentic work
AA Agentic Index
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 26.3 against 22.5.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.7 Max
Qwen3.7 Max leads, 90.2% against 89.4%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.7 Max
Qwen3.7 Max leads, 89.3% against 87.3%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.7 Max
Qwen3.7 Max leads, 1274 against 1235.
Same board, both measured
Long documents
Listed context window
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 $0.0019, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 $0.57, Qwen3.7 Max $4.69.
Published attempt cost and pass rate
At a glance
- Coding index
- DeepSeek V4 Pro 0423 35.3 from 1 benchmark, Qwen3.7 Max 45.1 from 1 benchmark.
- Widest category
- Math, 15.5 points apart on average over 4 shared benchmarks.
- Widest single gap
- FrontierMath, tier 4: DeepSeek V4 Pro 0423 2.4% against Qwen3.7 Max 34.1%, 31.7 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0423 $1.9105 against Qwen3.7 Max $4.425 per million output tokens, 2.3x apart.
- Context window
- DeepSeek V4 Pro 0423 takes 1.05M tokens, Qwen3.7 Max 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 Pro 0423 | Bars | Qwen3.7 Max | Ties |
|---|---|---|---|---|
| Coding | 4 | 6 | ||
| Intelligence | 1 | 1 | ||
| Agentic work | 2 | 6 | ||
| Reasoning | 0 | 3 | 1 tie | |
| Math | 1 | 3 | ||
| Knowledge | 1 | 5 | ||
| Web design | 0 | 8 | ||
| General | 0 | 3 | ||
| All shared boards | 9 | 35 | 1 tie |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index46
| Benchmark | DeepSeek V4 Pro 0423 | Qwen3.7 Max | Leader |
|---|---|---|---|
| LMArena WebDev | 1447 | 1515 | Qwen3.7 Max |
| AA Coding Index | 59.4 | 66.0 | Qwen3.7 Max |
| LiveCodeBench (Vals AI) | 87.5% | 87.1% | DeepSeek V4 Pro 0423 |
| SWE-bench Verified (Vals AI) | 77.4% | 68.8% | DeepSeek V4 Pro 0423 |
| Terminal-Bench 4 (Vals AI) | 11.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Vibe Code Bench (Vals AI) | 49.9% | 47.7% | DeepSeek V4 Pro 0423 |
| Code migration (Vals AI) | 26.2% | 13.1% | DeepSeek V4 Pro 0423 |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Terminal-Bench 2.1 (Vals AI) | 50.2% | 61.0% | Qwen3.7 Max |
| SkillsBench (Vals AI) | 51.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SciCode | Not measured | 49.5% | Only Qwen3.7 Max measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 50.8% | Only Qwen3.7 Max 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 | 1189.4 | Qwen3.7 Max |
| LMArena Text: coding | 1470 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: software and IT | 1469 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena WebDev: React | 1441 | 1514 | Qwen3.7 Max |
| LMArena WebDev: HTML | 1435 | 1512 | Qwen3.7 Max |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | DeepSeek V4 Pro 0423 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 29.5 | DeepSeek V4 Pro 0423 |
| Epoch Capabilities Index | 149.1 | 154.0 | Qwen3.7 Max |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index26
| Benchmark | DeepSeek V4 Pro 0423 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | 22.5 | DeepSeek V4 Pro 0423 |
| Design Arena: agents building web apps | 1000 | 1208 | Qwen3.7 Max |
| Design Arena: agents building full-stack apps | 948 | 1191 | Qwen3.7 Max |
| Design Arena: agents building mobile apps | Not measured | 1153 | Only Qwen3.7 Max measured |
| Design Arena: agents building Android apps | Not measured | 1165 | Only Qwen3.7 Max measured |
| Design Arena: agents building Godot games | 1059 | 1187 | Qwen3.7 Max |
| Design Arena: agents building games | Not measured | 1161 | Only Qwen3.7 Max measured |
| Design Arena: HTML slides | Not measured | 1158 | Only Qwen3.7 Max measured |
| Design Arena: PowerPoint slides | Not measured | 1196 | Only Qwen3.7 Max measured |
| Finance Agent (Vals AI) | 44.1% | 47.8% | Qwen3.7 Max |
| Tax Agent Bench (Vals AI) | 25.2% | 15.8% | DeepSeek V4 Pro 0423 |
| Legal Research Bench (Vals AI) | 23.1% | 25.5% | Qwen3.7 Max |
| Excel Modeling Benchmark (Vals AI) | 51.6% | 57.0% | Qwen3.7 Max |
| τ²-Bench Telecom | Not measured | 94.7% | Only Qwen3.7 Max measured |
| Vending-Bench 2 | $3,285 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | DeepSeek V4 Pro 0423 | Qwen3.7 Max | Leader |
|---|---|---|---|
| GPQA Diamond | 89.4% | 90.2% | Qwen3.7 Max |
| GPQA Diamond (Artificial Analysis run) | Not measured | 92.3% | Only Qwen3.7 Max measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 40.5% | Only Qwen3.7 Max measured |
| GPQA Diamond (Epoch AI run) | 90.9% | 90.9% | Tie |
| SimpleBench | Not measured | 70.4% | Only Qwen3.7 Max measured |
| Chess puzzles | 19.0% | 26.0% | Qwen3.7 Max |
| Mystery game puzzles | 17.0% | 32.0% | Qwen3.7 Max |
| EBR-bench | Not measured | 9.5% | Only Qwen3.7 Max measured |
| LMArena Text: hard prompts | 1461 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: expert | 1463 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: hard prompts in English | 1466 | 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 index13
| Benchmark | DeepSeek V4 Pro 0423 | Qwen3.7 Max | Leader |
|---|---|---|---|
| OTIS Mock AIME | 96.7% | 95.6% | DeepSeek V4 Pro 0423 |
| FrontierMath, tiers 1 to 3 | 45.3% | 64.6% | Qwen3.7 Max |
| FrontierMath, tier 4 | 2.4% | 34.1% | Qwen3.7 Max |
| ProofBench | 16.0% | 26.0% | Qwen3.7 Max |
| LMArena Text: math | 1443 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: mathematical work | 1455 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | DeepSeek V4 Pro 0423 | Qwen3.7 Max | Leader |
|---|---|---|---|
| MMLU Pro | 87.3% | 89.3% | Qwen3.7 Max |
| LegalBench (Vals AI) | 80.3% | 84.9% | Qwen3.7 Max |
| CaseLaw (Vals AI) | 59.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CorpFin (Vals AI) | 61.4% | 63.7% | Qwen3.7 Max |
| TaxEval (Vals AI) | 72.1% | 75.3% | Qwen3.7 Max |
| MedCode (Vals AI) | 40.5% | 38.8% | DeepSeek V4 Pro 0423 |
| SimpleQA Verified | 47.0% | 55.8% | Qwen3.7 Max |
| LMArena Text: science | 1470 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: medicine and healthcare | 1466 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: legal and government | 1464 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: business and finance | 1443 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | DeepSeek V4 Pro 0423 | Qwen3.7 Max | Leader |
|---|---|---|---|
| Design Arena: websites | 1235 | 1274 | Qwen3.7 Max |
| Design Arena: UI components | 1221 | 1269 | Qwen3.7 Max |
| Design Arena: game dev | 1229 | 1265 | Qwen3.7 Max |
| Design Arena: data viz | 1203 | 1275 | Qwen3.7 Max |
| Design Arena: 3D | 1254 | 1266 | Qwen3.7 Max |
| Design Arena: all code categories | 1240 | 1275 | Qwen3.7 Max |
| Design Arena: SVG | 1139 | 1216 | Qwen3.7 Max |
| Design Arena: ASCII art | 1148 | 1211 | Qwen3.7 Max |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | DeepSeek V4 Pro 0423 | Qwen3.7 Max | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 62.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| MedScribe (Vals AI) | 75.1% | 79.4% | Qwen3.7 Max |
| IFBench | Not measured | 80.5% | Only Qwen3.7 Max measured |
| Long context reasoning (AA-LCR) | Not measured | 79.0% | Only Qwen3.7 Max measured |
| DTBench | 90.7% | 92.3% | Qwen3.7 Max |
| LMCA | 41.2% | 44.0% | Qwen3.7 Max |
| 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 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: instruction following | 1445 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: creative writing | 1446 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: English | 1459 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: non-English | 1439 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Chinese | 1481 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: German | 1458 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Japanese | 1446 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Korean | 1409 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: French | 1473 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Spanish | 1457 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Russian | 1453 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Polish | 1461 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: multi-turn | 1468 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: longer queries | 1458 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: writing and language | 1447 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: entertainment and media | 1431 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | Qwen3.7 Max | 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 Pro 0423 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4 Pro 0423 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4 Pro 0423 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 | Qwen3.7 Max |
|---|---|---|
| Maker | DeepSeek | Qwen |
| Weights | Open weights | No public weights listed |
| Released | April 24, 2026 | May 21, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $0.9553 | $1.475 |
| Output $/Mtok | $1.9105 | $4.425 |
| Coding index | 35.3 | 45.1 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $0.57 | $4.69 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0423 better than Qwen3.7 Max for coding?
Pick Qwen3.7 Max for coding: it scores 45.1 on the Vibe Coding Index against 35.3 for DeepSeek V4 Pro 0423. Pick DeepSeek V4 Pro 0423 when output tokens cost 2.3x less, or when a solved task costs $0.57 against $4.69.
Which is cheaper, DeepSeek V4 Pro 0423 or Qwen3.7 Max?
DeepSeek V4 Pro 0423 costs $0.9553 per million input tokens and $1.9105 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0423 costs $0.57 and Qwen3.7 Max $4.69.
Which has the bigger context window, DeepSeek V4 Pro 0423 or Qwen3.7 Max?
DeepSeek V4 Pro 0423, with 1.05M tokens against 1M.
How many benchmarks is the DeepSeek V4 Pro 0423 vs Qwen3.7 Max comparison based on?
45 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Pro 0423's coding index rests on 1 independent benchmark and Qwen3.7 Max's on 1. Where only one model has a result, the page says so instead of naming a winner.