DeepSeek V4 Pro 0423 vs Qwen3.8 Max (0902)

Qwen3.8 Max (0902) leads DeepSeek V4 Pro 0423 on coding, 62.4 to 38.7 on the Vibe Coding Index, and DeepSeek V4 Pro 0423 costs 2.0x less per output token ($2.976 vs $6.00 per million). Updated October 22, 2026; 75 benchmarks have a result for both.

Replace DeepSeek V4 Pro 0423 Replace Qwen3.8 Max (0902)

DeepSeek V4 Pro 0423DeepSeek

Coding: 38.7
Overall: 50.0
Output $/M: $2.976

Qwen3.8 Max (0902)Qwen

Coding: 62.4
Overall: 68.9
Output $/M: $6.00

vs boards won of 75 shared, 1 tied

DeepSeek V4 Pro 0423 or Qwen3.8 Max (0902): which to pick

Pick Qwen3.8 Max (0902) for coding: it scores 62.4 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423.

Pick DeepSeek V4 Pro 0423 for output tokens 2.0x cheaper ($2.976 vs $6.00 per million) and a lower cost per solved task ($0.57 against $1.32).

Model ADeepSeek V4 Pro 0423DeepSeek38.7/1005 independent benchmarks$0.57 per solved task Model BQwen3.8 Max (0902)Qwen62.4/1005 independent benchmarks$1.32 per solved task

75938

Shared results: 75
DeepSeek V4 Pro 0423 only: 9
Qwen3.8 Max (0902) only: 38
Like-for-like categories: 8 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 Max (0902)
Qwen3.8 Max (0902) scores 62.4 against 38.7.
5 benchmarks behind each score
Intelligence
AA Intelligence Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 45.4 against 30.4.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 56.0 against 26.3.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 93.7% against 89.4%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 88.6% against 87.3%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 1275 against 1234.
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 $0.0028, Qwen3.8 Max (0902) $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 $0.57, Qwen3.8 Max (0902) $1.32.
Published attempt cost and pass rate

At a glance

Coding index: DeepSeek V4 Pro 0423 38.7 from 5 benchmarks, Qwen3.8 Max (0902) 62.4 from 5 benchmarks.
Widest category: Agentic work, 22.5 points apart on average over 8 shared benchmarks.
Widest single gap: Design Arena: agents building full-stack apps: DeepSeek V4 Pro 0423 948 against Qwen3.8 Max (0902) 1305, 44.6 points apart on a 0 to 100 scale.
Output price: DeepSeek V4 Pro 0423 $2.976 against Qwen3.8 Max (0902) $6.00 per million output tokens, 2.0x apart.
Context window: DeepSeek V4 Pro 0423 takes 1.05M tokens, Qwen3.8 Max (0902) 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.

Boards won, where both have a result
Category	DeepSeek V4 Pro 0423	Qwen3.8 Max (0902)	Ties
Coding	2	11	1 tie
Intelligence	0	2
Agentic work	0	8
Reasoning	0	7
Math	0	6
Knowledge	0	10
Web design	0	7
General	0	21
All shared boards	2	72	1 tie

CodingLike-for-likeSix of these boards feed the Vibe Coding Index211

Coding benchmarks for DeepSeek V4 Pro 0423 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 Max (0902)	Leader
LiveBench coding	Not measured	60.3%	Only Qwen3.8 Max (0902) measured
LMArena WebDev	1447	1674	Qwen3.8 Max (0902)
AA Coding Index	59.4	76.2	Qwen3.8 Max (0902)
LiveCodeBench (Vals AI)	87.5%	87.8%	Qwen3.8 Max (0902)
SWE-bench Verified (Vals AI)	77.4%	85.6%	Qwen3.8 Max (0902)
Terminal-Bench 4 (Vals AI)	11.1%	34.3%	Qwen3.8 Max (0902)
Vibe Code Bench (Vals AI)	49.9%	64.7%	Qwen3.8 Max (0902)
Code migration (Vals AI)	26.2%	24.0%	DeepSeek V4 Pro 0423
ProgramBench (Vals AI)	0.0%	0.0%	Tie
IOI (Vals AI)	Not measured	68.9%	Only Qwen3.8 Max (0902) measured
Terminal-Bench 2.1 (Vals AI)	50.2%	67.4%	Qwen3.8 Max (0902)
SWE-bench Multimodal (Vals AI)	Not measured	42.5%	Only Qwen3.8 Max (0902) measured
Vibe Code Bench 1-100 (Vals AI)	Not measured	12.8%	Only Qwen3.8 Max (0902) measured
SkillsBench (Vals AI)	51.3%	42.0%	DeepSeek V4 Pro 0423
CyberBench (Vals AI)	Not measured	28.6%	Only Qwen3.8 Max (0902) measured
SciCode	Not measured	53.2%	Only Qwen3.8 Max (0902) measured
FrontierCode	17.6%	Not measured	Only DeepSeek V4 Pro 0423 measured
FrontierSWE	Not measured	17.8%	Only Qwen3.8 Max (0902) measured
WeirdML	48.9%	Not measured	Only DeepSeek V4 Pro 0423 measured
ALE-Bench	1006.1	Not measured	Only DeepSeek V4 Pro 0423 measured
LMArena Text: coding	1470	1502	Qwen3.8 Max (0902)
LMArena Text: software and IT	1469	1498	Qwen3.8 Max (0902)
LMArena WebDev: React	1441	1692	Qwen3.8 Max (0902)
LMArena WebDev: HTML	1435	1649	Qwen3.8 Max (0902)
LMArena WebDev: from an image	Not measured	1637	Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 14 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02

Intelligence benchmarks for DeepSeek V4 Pro 0423 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 Max (0902)	Leader
AA Intelligence Index	30.4	45.4	Qwen3.8 Max (0902)
Epoch Capabilities Index	149.1	157.4	Qwen3.8 Max (0902)

Agentic workLike-for-likePublic boards, shown as published, kept out of the index08

Agentic work benchmarks for DeepSeek V4 Pro 0423 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 Max (0902)	Leader
AA Agentic Index	26.3	56.0	Qwen3.8 Max (0902)
Design Arena: agents building web apps	1000	1300	Qwen3.8 Max (0902)
Design Arena: agents building full-stack apps	948	1305	Qwen3.8 Max (0902)
Design Arena: agents building mobile apps	Not measured	1224	Only Qwen3.8 Max (0902) measured
Design Arena: agents building Android apps	Not measured	1196	Only Qwen3.8 Max (0902) measured
Design Arena: agents building Godot games	1059	1234	Qwen3.8 Max (0902)
Design Arena: PowerPoint slides	Not measured	1233	Only Qwen3.8 Max (0902) measured
Design Arena: spreadsheets	Not measured	967	Only Qwen3.8 Max (0902) measured
LiveBench research	Not measured	69.9%	Only Qwen3.8 Max (0902) measured
LiveBench browser	Not measured	32.0%	Only Qwen3.8 Max (0902) measured
LiveBench long agentic	Not measured	42.3%	Only Qwen3.8 Max (0902) measured
Finance Agent (Vals AI)	44.1%	50.6%	Qwen3.8 Max (0902)
Tax Agent Bench (Vals AI)	25.2%	32.1%	Qwen3.8 Max (0902)
Legal Research Bench (Vals AI)	23.1%	47.6%	Qwen3.8 Max (0902)
Excel Modeling Benchmark (Vals AI)	51.6%	60.1%	Qwen3.8 Max (0902)
Terminal-Bench-Science (Vals AI)	Not measured	12.9%	Only Qwen3.8 Max (0902) measured
APEX-Agents	Not measured	63.3%	Only Qwen3.8 Max (0902) measured
Vending-Bench 2	$3,285	Not measured	Only DeepSeek V4 Pro 0423 measured
LMArena Search	Not measured	1261	Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 22 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index07

Reasoning benchmarks for DeepSeek V4 Pro 0423 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 Max (0902)	Leader
GPQA Diamond	89.4%	93.7%	Qwen3.8 Max (0902)
LiveBench reasoning	Not measured	88.2%	Only Qwen3.8 Max (0902) measured
MysteryMechanism (Vals AI)	Not measured	23.9%	Only Qwen3.8 Max (0902) measured
MMMU Pro (Vals AI)	Not measured	88.0%	Only Qwen3.8 Max (0902) measured
GPQA Diamond (Artificial Analysis run)	Not measured	92.8%	Only Qwen3.8 Max (0902) measured
Humanity's Last Exam (Artificial Analysis run)	Not measured	43.1%	Only Qwen3.8 Max (0902) measured
GPQA Diamond (Epoch AI run)	90.9%	92.7%	Qwen3.8 Max (0902)
Chess puzzles	19.0%	46.0%	Qwen3.8 Max (0902)
Mystery game puzzles	17.0%	38.0%	Qwen3.8 Max (0902)
LMArena Text: hard prompts	1461	1496	Qwen3.8 Max (0902)
LMArena Text: expert	1463	1507	Qwen3.8 Max (0902)
LMArena Text: hard prompts in English	1466	1503	Qwen3.8 Max (0902)
LMArena Vision: diagrams	Not measured	1323	Only Qwen3.8 Max (0902) measured
LMArena Vision: homework	Not measured	1336	Only Qwen3.8 Max (0902) measured
Kagi LLM Benchmark	53.5%	Not measured	Only DeepSeek V4 Pro 0423 measured
Neither model has a result on the other 7 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index06

Math benchmarks for DeepSeek V4 Pro 0423 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 Max (0902)	Leader
OTIS Mock AIME	96.7%	100.0%	Qwen3.8 Max (0902)
FrontierMath, tiers 1 to 3	45.3%	74.7%	Qwen3.8 Max (0902)
FrontierMath, tier 4	2.4%	46.3%	Qwen3.8 Max (0902)
ProofBench	16.0%	58.0%	Qwen3.8 Max (0902)
LMArena Text: math	1443	1501	Qwen3.8 Max (0902)
LMArena Text: mathematical work	1455	1491	Qwen3.8 Max (0902)
Neither model has a result on the other 7 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index010

Knowledge benchmarks for DeepSeek V4 Pro 0423 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 Max (0902)	Leader
MMLU Pro	87.3%	88.6%	Qwen3.8 Max (0902)
LegalBench (Vals AI)	80.3%	83.6%	Qwen3.8 Max (0902)
CaseLaw (Vals AI)	59.4%	Not measured	Only DeepSeek V4 Pro 0423 measured
CorpFin (Vals AI)	61.4%	65.8%	Qwen3.8 Max (0902)
TaxEval (Vals AI)	72.1%	75.5%	Qwen3.8 Max (0902)
MortgageTax (Vals AI)	Not measured	64.0%	Only Qwen3.8 Max (0902) measured
MedCode (Vals AI)	40.5%	40.7%	Qwen3.8 Max (0902)
SimpleQA Verified	47.0%	47.3%	Qwen3.8 Max (0902)
LMArena Text: science	1470	1512	Qwen3.8 Max (0902)
LMArena Text: medicine and healthcare	1466	1503	Qwen3.8 Max (0902)
LMArena Text: legal and government	1464	1492	Qwen3.8 Max (0902)
LMArena Text: business and finance	1443	1480	Qwen3.8 Max (0902)
Neither model has a result on the other 6 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index07

Web design benchmarks for DeepSeek V4 Pro 0423 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 Max (0902)	Leader
Design Arena: websites	1234	1275	Qwen3.8 Max (0902)
Design Arena: UI components	1219	1338	Qwen3.8 Max (0902)
Design Arena: game dev	1228	1312	Qwen3.8 Max (0902)
Design Arena: data viz	1203	1281	Qwen3.8 Max (0902)
Design Arena: 3D	1253	1336	Qwen3.8 Max (0902)
Design Arena: all code categories	1240	1293	Qwen3.8 Max (0902)
Design Arena: SVG	1139	Not measured	Only DeepSeek V4 Pro 0423 measured
Design Arena: ASCII art	1149	1200	Qwen3.8 Max (0902)
LiveBench design	Not measured	78.6%	Only Qwen3.8 Max (0902) measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index021

General benchmarks for DeepSeek V4 Pro 0423 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 Max (0902)	Leader
LiveBench data analysis	Not measured	78.4%	Only Qwen3.8 Max (0902) measured
Public Benefits Bench (Vals AI)	62.9%	67.1%	Qwen3.8 Max (0902)
SAGE (Vals AI)	Not measured	51.2%	Only Qwen3.8 Max (0902) measured
MedScribe (Vals AI)	75.1%	85.0%	Qwen3.8 Max (0902)
Vals Multimodal Index	Not measured	65.4%	Only Qwen3.8 Max (0902) measured
Long context reasoning (AA-LCR)	Not measured	80.3%	Only Qwen3.8 Max (0902) measured
DTBench	90.7%	92.0%	Qwen3.8 Max (0902)
LMCA	41.2%	46.2%	Qwen3.8 Max (0902)
Surface Evolver Bench	40.0%	Not measured	Only DeepSeek V4 Pro 0423 measured
Furniture assembly	Not measured	20.0%	Only Qwen3.8 Max (0902) measured
CL-bench Life	13.5%	Not measured	Only DeepSeek V4 Pro 0423 measured
LMArena Text	1451	1483	Qwen3.8 Max (0902)
LMArena Text: instruction following	1445	1481	Qwen3.8 Max (0902)
LMArena Text: creative writing	1446	1477	Qwen3.8 Max (0902)
LMArena Vision	Not measured	1313	Only Qwen3.8 Max (0902) measured
LMArena Text: English	1459	1491	Qwen3.8 Max (0902)
LMArena Text: non-English	1439	1473	Qwen3.8 Max (0902)
LMArena Text: Chinese	1481	1539	Qwen3.8 Max (0902)
LMArena Text: German	1458	1478	Qwen3.8 Max (0902)
LMArena Text: Japanese	1446	1473	Qwen3.8 Max (0902)
LMArena Text: Korean	1409	1460	Qwen3.8 Max (0902)
LMArena Text: French	1473	1498	Qwen3.8 Max (0902)
LMArena Text: Spanish	1457	1489	Qwen3.8 Max (0902)
LMArena Text: Russian	1453	1482	Qwen3.8 Max (0902)
LMArena Text: Polish	1461	1471	Qwen3.8 Max (0902)
LMArena Text: multi-turn	1468	1489	Qwen3.8 Max (0902)
LMArena Text: longer queries	1458	1488	Qwen3.8 Max (0902)
LMArena Text: writing and language	1447	1477	Qwen3.8 Max (0902)
LMArena Text: entertainment and media	1431	1464	Qwen3.8 Max (0902)
LMArena Vision: English	Not measured	1311	Only Qwen3.8 Max (0902) measured
LMArena Vision: Chinese	Not measured	1358	Only Qwen3.8 Max (0902) measured
LMArena Vision: reading text	Not measured	1322	Only Qwen3.8 Max (0902) measured
LMArena Vision: humor	Not measured	1304	Only Qwen3.8 Max (0902) measured
LMArena Vision: creative writing	Not measured	1312	Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 12 boards here

Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared

Image generation benchmarks for DeepSeek V4 Pro 0423 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 Max (0902)	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$0.0028Fits in one request

Qwen3.8 Max (0902)$0.0050Fits in one request

DeepSeek V4 Pro 0423 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Pro 0423$0.077Fits in one request

Qwen3.8 Max (0902)$0.118Fits in one request

DeepSeek V4 Pro 0423 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Pro 0423$0.301Fits in one request

Qwen3.8 Max (0902)$0.460Fits in one request

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.

Specifications of DeepSeek V4 Pro 0423 and Qwen3.8 Max (0902)
Specification	DeepSeek V4 Pro 0423	Qwen3.8 Max (0902)
Maker	DeepSeek	Qwen
Weights	Open weights	No public weights listed
Released	April 24, 2026	September 3, 2026
Context window	1.05M	1M
Input $/Mtok	$1.358	$2.00
Output $/Mtok	$2.976	$6.00
Coding index	38.7	62.4
Independent measurements behind it	5	5
Cost per solved task	$0.57	$1.32
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V4 Pro 0423 better than Qwen3.8 Max (0902) for coding?

Pick Qwen3.8 Max (0902) for coding: it scores 62.4 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423. Pick DeepSeek V4 Pro 0423 for output tokens 2.0x cheaper ($2.976 vs $6.00 per million) and a lower cost per solved task ($0.57 against $1.32).

Which is cheaper, DeepSeek V4 Pro 0423 or Qwen3.8 Max (0902)?

DeepSeek V4 Pro 0423 costs $1.358 per million input tokens and $2.976 per million output tokens; Qwen3.8 Max (0902) costs $2.00 and $6.00. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0423 costs $0.57 and Qwen3.8 Max (0902) $1.32.

Which has the bigger context window, DeepSeek V4 Pro 0423 or Qwen3.8 Max (0902)?

DeepSeek V4 Pro 0423, with 1.05M tokens against 1M.

How many benchmarks is the DeepSeek V4 Pro 0423 vs Qwen3.8 Max (0902) comparison based on?

75 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 Qwen3.8 Max (0902)'s on 5. Where only one model has a result, the page says so instead of naming a winner.

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DeepSeek V4 Pro 0423 or Qwen3.8 Max (0902): which to pick

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

Knowledge

Web design

Long documents

Everyday cost

Cost per solved task

At a glance

Benchmark by benchmark

What each workload costs

Chat turn

Repository review

Agent loop

Specification differences

Questions

Compare next