DeepSeek V4 Flash 0731 vs Qwen3.8 Max (0902)

Qwen3.8 Max (0902) leads DeepSeek V4 Flash 0731 on coding, 62.4 to 51.8 on the Vibe Coding Index, and DeepSeek V4 Flash 0731 costs 15.2x less per output token ($0.396 vs $6.00 per million). Updated October 22, 2026; 47 benchmarks have a result for both.

Replace DeepSeek V4 Flash 0731 Replace Qwen3.8 Max (0902)

DeepSeek V4 Flash 0731DeepSeek

Coding: 51.8
Overall: 55.2
Output $/M: $0.396

Qwen3.8 Max (0902)Qwen

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

vs boards won of 47 shared, 1 tied

DeepSeek V4 Flash 0731 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 51.8 for DeepSeek V4 Flash 0731.

Pick DeepSeek V4 Flash 0731 for output tokens 15.2x cheaper ($0.396 vs $6.00 per million) and a lower cost per solved task ($0.01 against $1.32).

Model ADeepSeek V4 Flash 0731DeepSeek51.8/1004 independent benchmarks$0.01 per solved task Model BQwen3.8 Max (0902)Qwen62.4/1005 independent benchmarks$1.32 per solved task

47866

Shared results: 47
DeepSeek V4 Flash 0731 only: 8
Qwen3.8 Max (0902) only: 66
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 51.8.
4 and 5 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 45.4 against 34.3.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 56.0 against 41.0.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 93.7% against 89.9%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 88.6% against 86.2%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 1275 against 1237.
Same board, both measured
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 Max (0902) $0.0050 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 Max (0902) $1.32.
Published attempt cost and pass rate

At a glance

Coding index: DeepSeek V4 Flash 0731 51.8 from 4 benchmarks, Qwen3.8 Max (0902) 62.4 from 5 benchmarks.
Widest category: Coding, 12.3 points apart on average over 12 shared benchmarks.
Widest single gap: CyberBench (Vals AI): DeepSeek V4 Flash 0731 72.1% against Qwen3.8 Max (0902) 28.6%, 43.5 points apart on a 0 to 100 scale.
Output price: DeepSeek V4 Flash 0731 $0.396 against Qwen3.8 Max (0902) $6.00 per million output tokens, 15.2x apart.
Context window: DeepSeek V4 Flash 0731 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 Flash 0731	Qwen3.8 Max (0902)	Ties
Coding	5	6	1 tie
Intelligence	0	2
Agentic work	0	6
Reasoning	0	6
Math	0	4
Knowledge	1	5
Web design	0	7
General	0	4
All shared boards	6	40	1 tie

CodingLike-for-likeSix of these boards feed the Vibe Coding Index56

Coding benchmarks for DeepSeek V4 Flash 0731 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Flash 0731	Qwen3.8 Max (0902)	Leader
LiveBench coding	Not measured	60.3%	Only Qwen3.8 Max (0902) measured
LMArena WebDev	Not measured	1674	Only Qwen3.8 Max (0902) measured
AA Coding Index	69.1	76.2	Qwen3.8 Max (0902)
LiveCodeBench (Vals AI)	87.3%	87.8%	Qwen3.8 Max (0902)
SWE-bench Verified (Vals AI)	88.8%	85.6%	DeepSeek V4 Flash 0731
Terminal-Bench 4 (Vals AI)	18.7%	34.3%	Qwen3.8 Max (0902)
Vibe Code Bench (Vals AI)	74.7%	64.7%	DeepSeek V4 Flash 0731
Code migration (Vals AI)	38.6%	24.0%	DeepSeek V4 Flash 0731
ProgramBench (Vals AI)	0.0%	0.0%	Tie
IOI (Vals AI)	32.7%	68.9%	Qwen3.8 Max (0902)
Terminal-Bench 2.1 (Vals AI)	67.0%	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)	50.7%	42.0%	DeepSeek V4 Flash 0731
CyberBench (Vals AI)	72.1%	28.6%	DeepSeek V4 Flash 0731
SciCode	50.3%	53.2%	Qwen3.8 Max (0902)
FrontierCode	18.8%	Not measured	Only DeepSeek V4 Flash 0731 measured
FrontierSWE	Not measured	17.8%	Only Qwen3.8 Max (0902) 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	1502	Only Qwen3.8 Max (0902) measured
LMArena Text: software and IT	Not measured	1498	Only Qwen3.8 Max (0902) measured
LMArena WebDev: React	Not measured	1692	Only Qwen3.8 Max (0902) measured
LMArena WebDev: HTML	Not measured	1649	Only Qwen3.8 Max (0902) measured
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 Flash 0731 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Flash 0731	Qwen3.8 Max (0902)	Leader
AA Intelligence Index	34.3	45.4	Qwen3.8 Max (0902)
Epoch Capabilities Index	154.8	157.4	Qwen3.8 Max (0902)

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

Agentic work benchmarks for DeepSeek V4 Flash 0731 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Flash 0731	Qwen3.8 Max (0902)	Leader
AA Agentic Index	41.0	56.0	Qwen3.8 Max (0902)
Design Arena: agents building web apps	Not measured	1300	Only Qwen3.8 Max (0902) measured
Design Arena: agents building full-stack apps	Not measured	1305	Only Qwen3.8 Max (0902) measured
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	Not measured	1234	Only Qwen3.8 Max (0902) measured
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)	49.5%	50.6%	Qwen3.8 Max (0902)
Tax Agent Bench (Vals AI)	28.1%	32.1%	Qwen3.8 Max (0902)
Legal Research Bench (Vals AI)	30.3%	47.6%	Qwen3.8 Max (0902)
Excel Modeling Benchmark (Vals AI)	57.0%	60.1%	Qwen3.8 Max (0902)
BioMysteryBench (Vals AI)	64.4%	Not measured	Only DeepSeek V4 Flash 0731 measured
Terminal-Bench-Science (Vals AI)	4.3%	12.9%	Qwen3.8 Max (0902)
APEX-Agents	Not measured	63.3%	Only Qwen3.8 Max (0902) 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 index06

Reasoning benchmarks for DeepSeek V4 Flash 0731 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Flash 0731	Qwen3.8 Max (0902)	Leader
GPQA Diamond	89.9%	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)	90.8%	92.8%	Qwen3.8 Max (0902)
Humanity's Last Exam (Artificial Analysis run)	38.6%	43.1%	Qwen3.8 Max (0902)
GPQA Diamond (Epoch AI run)	91.0%	92.7%	Qwen3.8 Max (0902)
ARC-AGI	89.0%	Not measured	Only DeepSeek V4 Flash 0731 measured
ARC-AGI-2	61.4%	Not measured	Only DeepSeek V4 Flash 0731 measured
SimpleBench	61.1%	Not measured	Only DeepSeek V4 Flash 0731 measured
Chess puzzles	32.0%	46.0%	Qwen3.8 Max (0902)
Mystery game puzzles	34.0%	38.0%	Qwen3.8 Max (0902)
LMArena Text: hard prompts	Not measured	1496	Only Qwen3.8 Max (0902) measured
LMArena Text: expert	Not measured	1507	Only Qwen3.8 Max (0902) measured
LMArena Text: hard prompts in English	Not measured	1503	Only Qwen3.8 Max (0902) measured
LMArena Vision: diagrams	Not measured	1323	Only Qwen3.8 Max (0902) measured
LMArena Vision: homework	Not measured	1336	Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 5 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index04

Math benchmarks for DeepSeek V4 Flash 0731 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Flash 0731	Qwen3.8 Max (0902)	Leader
OTIS Mock AIME	94.4%	100.0%	Qwen3.8 Max (0902)
FrontierMath, tiers 1 to 3	57.5%	74.7%	Qwen3.8 Max (0902)
FrontierMath, tier 4	24.4%	46.3%	Qwen3.8 Max (0902)
ProofBench	56.0%	58.0%	Qwen3.8 Max (0902)
LMArena Text: math	Not measured	1501	Only Qwen3.8 Max (0902) measured
LMArena Text: mathematical work	Not measured	1491	Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 7 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index15

Knowledge benchmarks for DeepSeek V4 Flash 0731 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Flash 0731	Qwen3.8 Max (0902)	Leader
MMLU Pro	86.2%	88.6%	Qwen3.8 Max (0902)
LegalBench (Vals AI)	77.7%	83.6%	Qwen3.8 Max (0902)
CorpFin (Vals AI)	61.9%	65.8%	Qwen3.8 Max (0902)
TaxEval (Vals AI)	70.7%	75.5%	Qwen3.8 Max (0902)
MortgageTax (Vals AI)	Not measured	64.0%	Only Qwen3.8 Max (0902) measured
MedCode (Vals AI)	41.4%	40.7%	DeepSeek V4 Flash 0731
SimpleQA Verified	33.6%	47.3%	Qwen3.8 Max (0902)
LMArena Text: science	Not measured	1512	Only Qwen3.8 Max (0902) measured
LMArena Text: medicine and healthcare	Not measured	1503	Only Qwen3.8 Max (0902) measured
LMArena Text: legal and government	Not measured	1492	Only Qwen3.8 Max (0902) measured
LMArena Text: business and finance	Not measured	1480	Only Qwen3.8 Max (0902) measured
Neither model has a result on the other 7 boards here

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

Web design benchmarks for DeepSeek V4 Flash 0731 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Flash 0731	Qwen3.8 Max (0902)	Leader
Design Arena: websites	1237	1275	Qwen3.8 Max (0902)
Design Arena: UI components	1233	1338	Qwen3.8 Max (0902)
Design Arena: game dev	1207	1312	Qwen3.8 Max (0902)
Design Arena: data viz	1180	1281	Qwen3.8 Max (0902)
Design Arena: 3D	1208	1336	Qwen3.8 Max (0902)
Design Arena: all code categories	1229	1293	Qwen3.8 Max (0902)
Design Arena: SVG	1184	Not measured	Only DeepSeek V4 Flash 0731 measured
Design Arena: ASCII art	1090	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 index04

General benchmarks for DeepSeek V4 Flash 0731 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Flash 0731	Qwen3.8 Max (0902)	Leader
LiveBench data analysis	Not measured	78.4%	Only Qwen3.8 Max (0902) measured
Public Benefits Bench (Vals AI)	Not measured	67.1%	Only Qwen3.8 Max (0902) measured
SAGE (Vals AI)	Not measured	51.2%	Only Qwen3.8 Max (0902) measured
MedScribe (Vals AI)	80.4%	85.0%	Qwen3.8 Max (0902)
Vals Multimodal Index	Not measured	65.4%	Only Qwen3.8 Max (0902) measured
Long context reasoning (AA-LCR)	79.7%	80.3%	Qwen3.8 Max (0902)
DTBench	90.9%	92.0%	Qwen3.8 Max (0902)
LMCA	41.7%	46.2%	Qwen3.8 Max (0902)
Furniture assembly	Not measured	20.0%	Only Qwen3.8 Max (0902) measured
LMArena Text	Not measured	1483	Only Qwen3.8 Max (0902) measured
LMArena Text: instruction following	Not measured	1481	Only Qwen3.8 Max (0902) measured
LMArena Text: creative writing	Not measured	1477	Only Qwen3.8 Max (0902) measured
LMArena Vision	Not measured	1313	Only Qwen3.8 Max (0902) measured
LMArena Text: English	Not measured	1491	Only Qwen3.8 Max (0902) measured
LMArena Text: non-English	Not measured	1473	Only Qwen3.8 Max (0902) measured
LMArena Text: Chinese	Not measured	1539	Only Qwen3.8 Max (0902) measured
LMArena Text: German	Not measured	1478	Only Qwen3.8 Max (0902) measured
LMArena Text: Japanese	Not measured	1473	Only Qwen3.8 Max (0902) measured
LMArena Text: Korean	Not measured	1460	Only Qwen3.8 Max (0902) measured
LMArena Text: French	Not measured	1498	Only Qwen3.8 Max (0902) measured
LMArena Text: Spanish	Not measured	1489	Only Qwen3.8 Max (0902) measured
LMArena Text: Russian	Not measured	1482	Only Qwen3.8 Max (0902) measured
LMArena Text: Polish	Not measured	1471	Only Qwen3.8 Max (0902) measured
LMArena Text: multi-turn	Not measured	1489	Only Qwen3.8 Max (0902) measured
LMArena Text: longer queries	Not measured	1488	Only Qwen3.8 Max (0902) measured
LMArena Text: writing and language	Not measured	1477	Only Qwen3.8 Max (0902) measured
LMArena Text: entertainment and media	Not measured	1464	Only Qwen3.8 Max (0902) measured
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 14 boards here

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

Image generation benchmarks for DeepSeek V4 Flash 0731 and Qwen3.8 Max (0902)
Benchmark	DeepSeek V4 Flash 0731	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 Flash 0731$0.0003Fits in one request

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

DeepSeek V4 Flash 0731 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Flash 0731$0.0082Fits in one request

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

DeepSeek V4 Flash 0731 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Flash 0731$0.032Fits in one request

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

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.

Specifications of DeepSeek V4 Flash 0731 and Qwen3.8 Max (0902)
Specification	DeepSeek V4 Flash 0731	Qwen3.8 Max (0902)
Maker	DeepSeek	Qwen
Weights	Open weights	No public weights listed
Released	July 31, 2026	September 3, 2026
Context window	1.05M	1M
Input $/Mtok	$0.14	$2.00
Output $/Mtok	$0.396	$6.00
Coding index	51.8	62.4
Independent measurements behind it	4	5
Cost per solved task	$0.01	$1.32
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V4 Flash 0731 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 51.8 for DeepSeek V4 Flash 0731. Pick DeepSeek V4 Flash 0731 for output tokens 15.2x cheaper ($0.396 vs $6.00 per million) and a lower cost per solved task ($0.01 against $1.32).

Which is cheaper, DeepSeek V4 Flash 0731 or Qwen3.8 Max (0902)?

DeepSeek V4 Flash 0731 costs $0.14 per million input tokens and $0.396 per million output tokens; Qwen3.8 Max (0902) costs $2.00 and $6.00. Per solved SWE-bench Verified task, DeepSeek V4 Flash 0731 costs $0.01 and Qwen3.8 Max (0902) $1.32.

Which has the bigger context window, DeepSeek V4 Flash 0731 or Qwen3.8 Max (0902)?

DeepSeek V4 Flash 0731, with 1.05M tokens against 1M.

How many benchmarks is the DeepSeek V4 Flash 0731 vs Qwen3.8 Max (0902) comparison based on?

47 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 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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<iframe src="https://llmbenchmark.ai/embed/compare/deepseek-v4-flash-0731-vs-qwen3-8-max-0902" title="DeepSeek V4 Flash 0731 vs Qwen3.8 Max (0902), LLMBenchmark.ai" width="100%" height="360" style="border:0;max-width:100%" loading="lazy"></iframe>
<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/deepseek-v4-flash-0731-vs-qwen3-8-max-0902">LLMBenchmark.ai, DeepSeek V4 Flash 0731 vs Qwen3.8 Max (0902)</a></p>

DeepSeek V4 Flash 0731 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