DeepSeek V4 Pro 0423 vs Qwen3.6 Plus

Qwen3.6 Plus leads DeepSeek V4 Pro 0423 on coding, 44.1 to 38.7 on the Vibe Coding Index, and Qwen3.6 Plus costs 1.4x less per output token ($1.95 vs $2.716 per million). Updated October 21, 2026; 70 benchmarks have a result for both.

Replace DeepSeek V4 Pro 0423 Replace Qwen3.6 Plus

DeepSeek V4 Pro 0423DeepSeek

Coding: 38.7
Overall: 50.0
Output $/M: $2.716

Qwen3.6 PlusQwen

Coding: 44.1
Overall: 56.2
Output $/M: $1.95

vs boards won of 70 shared, 1 tied

DeepSeek V4 Pro 0423 or Qwen3.6 Plus: which to pick

Pick Qwen3.6 Plus for coding: it scores 44.1 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423, though on 3 benchmarks against 5.

Pick DeepSeek V4 Pro 0423 for a lower cost per solved task ($0.57 against $1.19) and a larger context window (1.05M against 1M tokens).

Model ADeepSeek V4 Pro 0423DeepSeek38.7/1005 independent benchmarks$0.57 per solved task Model BQwen3.6 PlusQwen44.1/1003 independent benchmarks$1.19 per solved task

701413

Shared results: 70
DeepSeek V4 Pro 0423 only: 14
Qwen3.6 Plus only: 13
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
Qwen3.6 Plus
Qwen3.6 Plus scores 44.1 against 38.7.
5 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 30.4 against 27.0.
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
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 89.4% against 87.4%.
Same board, both measured
Math
AIME
No call
Only Qwen3.6 Plus has a AIME result, 94.6%.
Directional only
Knowledge
MMLU Pro
Qwen3.6 Plus
Qwen3.6 Plus leads, 87.7% against 87.3%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.6 Plus
Qwen3.6 Plus leads, 1243 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
Qwen3.6 Plus
DeepSeek V4 Pro 0423 $0.0026, Qwen3.6 Plus $0.0013 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 $0.57, Qwen3.6 Plus $1.19.
Published attempt cost and pass rate

At a glance

Coding index: DeepSeek V4 Pro 0423 38.7 from 5 benchmarks, Qwen3.6 Plus 44.1 from 3 benchmarks.
Widest category: Agentic work, 11.8 points apart on average over 5 shared benchmarks.
Widest single gap: Vibe Code Bench (Vals AI): DeepSeek V4 Pro 0423 49.9% against Qwen3.6 Plus 25.6%, 24.4 points apart on a 0 to 100 scale.
Output price: DeepSeek V4 Pro 0423 $2.716 against Qwen3.6 Plus $1.95 per million output tokens, 1.4x apart.
Context window: DeepSeek V4 Pro 0423 takes 1.05M tokens, Qwen3.6 Plus 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	Qwen3.6 Plus	Ties
Coding	8	4	1 tie
Intelligence	2	0
Agentic work	4	1
Reasoning	7	0
Math	3	1
Knowledge	7	4
Web design	4	4
General	19	1
All shared boards	54	15	1 tie

CodingLike-for-likeSix of these boards feed the Vibe Coding Index84

Coding benchmarks for DeepSeek V4 Pro 0423 and Qwen3.6 Plus
Benchmark	DeepSeek V4 Pro 0423	Qwen3.6 Plus	Leader
LMArena WebDev	1447	1461	Qwen3.6 Plus
AA Coding Index	59.4	54.5	DeepSeek V4 Pro 0423
LiveCodeBench (Vals AI)	87.5%	86.0%	DeepSeek V4 Pro 0423
SWE-bench Verified (Vals AI)	77.4%	73.4%	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%	25.6%	DeepSeek V4 Pro 0423
Code migration (Vals AI)	26.2%	11.1%	DeepSeek V4 Pro 0423
ProgramBench (Vals AI)	0.0%	0.0%	Tie
Terminal-Bench 2.1 (Vals AI)	50.2%	53.2%	Qwen3.6 Plus
SkillsBench (Vals AI)	51.3%	Not measured	Only DeepSeek V4 Pro 0423 measured
Terminal-Bench Hard (Artificial Analysis run)	Not measured	43.9%	Only Qwen3.6 Plus 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	670.1	DeepSeek V4 Pro 0423
LMArena Text: coding	1470	1467	DeepSeek V4 Pro 0423
LMArena Text: software and IT	1469	1462	DeepSeek V4 Pro 0423
LMArena WebDev: React	1441	1454	Qwen3.6 Plus
LMArena WebDev: HTML	1435	1463	Qwen3.6 Plus
LMArena WebDev: from an image	Not measured	1472	Only Qwen3.6 Plus measured
Neither model has a result on the other 20 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20

Intelligence benchmarks for DeepSeek V4 Pro 0423 and Qwen3.6 Plus
Benchmark	DeepSeek V4 Pro 0423	Qwen3.6 Plus	Leader
AA Intelligence Index	30.4	27.0	DeepSeek V4 Pro 0423
Epoch Capabilities Index	149.1	147.3	DeepSeek V4 Pro 0423

Agentic workLike-for-likePublic boards, shown as published, kept out of the index41

Agentic work benchmarks for DeepSeek V4 Pro 0423 and Qwen3.6 Plus
Benchmark	DeepSeek V4 Pro 0423	Qwen3.6 Plus	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%	40.9%	DeepSeek V4 Pro 0423
Tax Agent Bench (Vals AI)	25.2%	7.7%	DeepSeek V4 Pro 0423
Legal Research Bench (Vals AI)	23.1%	14.9%	DeepSeek V4 Pro 0423
Excel Modeling Benchmark (Vals AI)	51.6%	32.9%	DeepSeek V4 Pro 0423
τ²-Bench Telecom	Not measured	97.7%	Only Qwen3.6 Plus measured
Vending-Bench 2	$3,285	$5,115	Qwen3.6 Plus
Neither model has a result on the other 31 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index70

Reasoning benchmarks for DeepSeek V4 Pro 0423 and Qwen3.6 Plus
Benchmark	DeepSeek V4 Pro 0423	Qwen3.6 Plus	Leader
GPQA Diamond	89.4%	87.4%	DeepSeek V4 Pro 0423
MMMU Pro (Vals AI)	Not measured	84.2%	Only Qwen3.6 Plus measured
GPQA Diamond (Artificial Analysis run)	Not measured	88.2%	Only Qwen3.6 Plus measured
Humanity's Last Exam (Artificial Analysis run)	Not measured	27.8%	Only Qwen3.6 Plus measured
GPQA Diamond (Epoch AI run)	90.9%	88.4%	DeepSeek V4 Pro 0423
Chess puzzles	19.0%	14.0%	DeepSeek V4 Pro 0423
Mystery game puzzles	17.0%	12.0%	DeepSeek V4 Pro 0423
LMArena Text: hard prompts	1461	1448	DeepSeek V4 Pro 0423
LMArena Text: expert	1463	1454	DeepSeek V4 Pro 0423
LMArena Text: hard prompts in English	1466	1455	DeepSeek V4 Pro 0423
Kagi LLM Benchmark	53.5%	Not measured	Only DeepSeek V4 Pro 0423 measured
Neither model has a result on the other 11 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index31

Math benchmarks for DeepSeek V4 Pro 0423 and Qwen3.6 Plus
Benchmark	DeepSeek V4 Pro 0423	Qwen3.6 Plus	Leader
AIME	Not measured	94.6%	Only Qwen3.6 Plus measured
OTIS Mock AIME	96.7%	93.3%	DeepSeek V4 Pro 0423
FrontierMath, tiers 1 to 3	45.3%	38.2%	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	1449	Qwen3.6 Plus
LMArena Text: mathematical work	1455	1448	DeepSeek V4 Pro 0423
Neither model has a result on the other 6 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index74

Knowledge benchmarks for DeepSeek V4 Pro 0423 and Qwen3.6 Plus
Benchmark	DeepSeek V4 Pro 0423	Qwen3.6 Plus	Leader
MMLU Pro	87.3%	87.7%	Qwen3.6 Plus
LegalBench (Vals AI)	80.3%	84.2%	Qwen3.6 Plus
CaseLaw (Vals AI)	59.4%	51.5%	DeepSeek V4 Pro 0423
CorpFin (Vals AI)	61.4%	61.9%	Qwen3.6 Plus
TaxEval (Vals AI)	72.1%	74.7%	Qwen3.6 Plus
MortgageTax (Vals AI)	Not measured	68.0%	Only Qwen3.6 Plus measured
MedCode (Vals AI)	40.5%	36.9%	DeepSeek V4 Pro 0423
SimpleQA Verified	47.0%	44.1%	DeepSeek V4 Pro 0423
LMArena Text: science	1470	1439	DeepSeek V4 Pro 0423
LMArena Text: medicine and healthcare	1466	1442	DeepSeek V4 Pro 0423
LMArena Text: legal and government	1464	1437	DeepSeek V4 Pro 0423
LMArena Text: business and finance	1443	1430	DeepSeek V4 Pro 0423
Neither model has a result on the other 6 boards here

Web designLike-for-likePublic boards, shown as published, kept out of the index44

Web design benchmarks for DeepSeek V4 Pro 0423 and Qwen3.6 Plus
Benchmark	DeepSeek V4 Pro 0423	Qwen3.6 Plus	Leader
Design Arena: websites	1234	1243	Qwen3.6 Plus
Design Arena: UI components	1222	1233	Qwen3.6 Plus
Design Arena: game dev	1229	1211	DeepSeek V4 Pro 0423
Design Arena: data viz	1203	1228	Qwen3.6 Plus
Design Arena: 3D	1254	1202	DeepSeek V4 Pro 0423
Design Arena: all code categories	1240	1236	DeepSeek V4 Pro 0423
Design Arena: SVG	1140	1161	Qwen3.6 Plus
Design Arena: ASCII art	1149	1113	DeepSeek V4 Pro 0423
Neither model has a result on the other 1 board here

GeneralLike-for-likePublic boards, shown as published, kept out of the index191

General benchmarks for DeepSeek V4 Pro 0423 and Qwen3.6 Plus
Benchmark	DeepSeek V4 Pro 0423	Qwen3.6 Plus	Leader
Public Benefits Bench (Vals AI)	62.9%	Not measured	Only DeepSeek V4 Pro 0423 measured
SAGE (Vals AI)	Not measured	44.9%	Only Qwen3.6 Plus measured
MedScribe (Vals AI)	75.1%	77.0%	Qwen3.6 Plus
Vals Multimodal Index	Not measured	51.5%	Only Qwen3.6 Plus measured
IFBench	Not measured	75.2%	Only Qwen3.6 Plus measured
Long context reasoning (AA-LCR)	Not measured	78.3%	Only Qwen3.6 Plus measured
DTBench	90.7%	81.9%	DeepSeek V4 Pro 0423
LMCA	41.2%	33.1%	DeepSeek V4 Pro 0423
CL-bench	Not measured	20.3%	Only Qwen3.6 Plus measured
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	1436	DeepSeek V4 Pro 0423
LMArena Text: instruction following	1445	1424	DeepSeek V4 Pro 0423
LMArena Text: creative writing	1446	1404	DeepSeek V4 Pro 0423
LMArena Text: English	1459	1445	DeepSeek V4 Pro 0423
LMArena Text: non-English	1439	1424	DeepSeek V4 Pro 0423
LMArena Text: Chinese	1481	1477	DeepSeek V4 Pro 0423
LMArena Text: German	1458	1451	DeepSeek V4 Pro 0423
LMArena Text: Japanese	1446	1390	DeepSeek V4 Pro 0423
LMArena Text: Korean	1409	1379	DeepSeek V4 Pro 0423
LMArena Text: French	1473	1455	DeepSeek V4 Pro 0423
LMArena Text: Spanish	1457	1432	DeepSeek V4 Pro 0423
LMArena Text: Russian	1453	1434	DeepSeek V4 Pro 0423
LMArena Text: Polish	1461	1433	DeepSeek V4 Pro 0423
LMArena Text: multi-turn	1468	1438	DeepSeek V4 Pro 0423
LMArena Text: longer queries	1458	1439	DeepSeek V4 Pro 0423
LMArena Text: writing and language	1447	1418	DeepSeek V4 Pro 0423
LMArena Text: entertainment and media	1431	1402	DeepSeek V4 Pro 0423
Neither model has a result on the other 18 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 Qwen3.6 Plus
Benchmark	DeepSeek V4 Pro 0423	Qwen3.6 Plus	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$0.0026Fits in one request

Qwen3.6 Plus$0.0013Fits in one request

Qwen3.6 Plus costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Pro 0423$0.069Fits in one request

Qwen3.6 Plus$0.022Fits in one request

Qwen3.6 Plus costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Pro 0423$0.272Fits in one request

Qwen3.6 Plus$0.085Fits in one request

Qwen3.6 Plus 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 Qwen3.6 Plus
Specification	DeepSeek V4 Pro 0423	Qwen3.6 Plus
Maker	DeepSeek	Qwen
Weights	Open weights	No public weights listed
Released	April 24, 2026	April 2, 2026
Context window	1.05M	1M
Input $/Mtok	$1.225	$0.325
Output $/Mtok	$2.716	$1.95
Coding index	38.7	44.1
Independent measurements behind it	5	3
Cost per solved task	$0.57	$1.19
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V4 Pro 0423 better than Qwen3.6 Plus for coding?

Pick Qwen3.6 Plus for coding: it scores 44.1 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423, though on 3 benchmarks against 5. Pick DeepSeek V4 Pro 0423 for a lower cost per solved task ($0.57 against $1.19) and a larger context window (1.05M against 1M tokens).

Which is cheaper, DeepSeek V4 Pro 0423 or Qwen3.6 Plus?

DeepSeek V4 Pro 0423 costs $1.225 per million input tokens and $2.716 per million output tokens; Qwen3.6 Plus costs $0.325 and $1.95. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0423 costs $0.57 and Qwen3.6 Plus $1.19.

Which has the bigger context window, DeepSeek V4 Pro 0423 or Qwen3.6 Plus?

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

How many benchmarks is the DeepSeek V4 Pro 0423 vs Qwen3.6 Plus comparison based on?

70 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Pro 0423's coding index rests on 5 independent benchmarks and Qwen3.6 Plus's on 3. 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 Qwen3.6 Plus: which to pick

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

Math

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