DeepSeek V4 Pro 0423 vs Qwen3.5-27B

DeepSeek V4 Pro 0423 leads Qwen3.5-27B on the Artificial Analysis Intelligence Index, 30.4 to 22.9, and Qwen3.5-27B costs 1.9x less per output token ($1.56 vs $2.976 per million). Updated October 22, 2026; 37 benchmarks have a result for both.

Replace DeepSeek V4 Pro 0423 Replace Qwen3.5-27B

DeepSeek V4 Pro 0423DeepSeek

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

Qwen3.5-27BQwen

Coding: No data
Overall: No data
Output $/M: $1.56

vs boards won of 37 shared

DeepSeek V4 Pro 0423 or Qwen3.5-27B: which to pick

Only DeepSeek V4 Pro 0423 has a coding score, 38.7 on the Vibe Coding Index; Qwen3.5-27B has none, so the two cannot be ranked on coding.

Pick Qwen3.5-27B for output tokens 1.9x cheaper ($1.56 vs $2.976 per million).

Model ADeepSeek V4 Pro 0423DeepSeek38.7/1005 independent benchmarks$0.57 per solved task Model BQwen3.5-27BQwenNot benchmarked1 independent benchmarkNo cost per solved task

374716

Shared results: 37
DeepSeek V4 Pro 0423 only: 47
Qwen3.5-27B only: 16
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 22.9.
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
No call
Only DeepSeek V4 Pro 0423 has a Design Arena: websites result, 1234.
Directional only
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
Qwen3.5-27B
DeepSeek V4 Pro 0423 $0.0028, Qwen3.5-27B $0.0010 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, Qwen3.5-27B not benchmarked.
Widest category: Agentic work, 19.3 points apart on average over 1 shared benchmark.
Widest single gap: ALE-Bench: DeepSeek V4 Pro 0423 1006.1 against Qwen3.5-27B 349.4, 21.9 points apart on a 0 to 100 scale.
Output price: DeepSeek V4 Pro 0423 $2.976 against Qwen3.5-27B $1.56 per million output tokens, 1.9x apart.
Context window: DeepSeek V4 Pro 0423 takes 1.05M tokens, Qwen3.5-27B 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.

Boards won, where both have a result
Category	DeepSeek V4 Pro 0423	Bars	Qwen3.5-27B	Ties
Coding	7		0
Intelligence	1		0
Agentic work	1		0
Reasoning	3		0
Math	2		0
Knowledge	4		0
General	19		0
All shared boards	37		0

CodingLike-for-likeSix of these boards feed the Vibe Coding Index70

Coding benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.5-27B	Leader
LMArena WebDev	1447	1358	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
Terminal-Bench Hard (Artificial Analysis run)	Not measured	32.6%	Only Qwen3.5-27B measured
FrontierCode	17.6%	Not measured	Only DeepSeek V4 Pro 0423 measured
WeirdML	48.9%	39.5%	DeepSeek V4 Pro 0423
ALE-Bench	1006.1	349.4	DeepSeek V4 Pro 0423
LMArena Text: coding	1470	1427	DeepSeek V4 Pro 0423
LMArena Text: software and IT	1469	1431	DeepSeek V4 Pro 0423
LMArena WebDev: React	1441	1346	DeepSeek V4 Pro 0423
LMArena WebDev: HTML	1435	1386	DeepSeek V4 Pro 0423
Neither model has a result on the other 21 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10

Intelligence benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.5-27B	Leader
AA Intelligence Index	30.4	22.9	DeepSeek V4 Pro 0423
Epoch Capabilities Index	149.1	Not measured	Only DeepSeek V4 Pro 0423 measured

Agentic workLike-for-likePublic boards, shown as published, kept out of the index10

Agentic work benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.5-27B	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	93.9%	Only Qwen3.5-27B measured
Vending-Bench 2	$3,285	$202	DeepSeek V4 Pro 0423
Neither model has a result on the other 31 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index30

Reasoning benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.5-27B	Leader
GPQA Diamond	89.4%	Not measured	Only DeepSeek V4 Pro 0423 measured
GPQA Diamond (Artificial Analysis run)	Not measured	85.8%	Only Qwen3.5-27B measured
Humanity's Last Exam (Artificial Analysis run)	Not measured	23.9%	Only Qwen3.5-27B measured
GPQA Diamond (Epoch AI run)	90.9%	Not measured	Only DeepSeek V4 Pro 0423 measured
Chess puzzles	19.0%	Not measured	Only DeepSeek V4 Pro 0423 measured
Mystery game puzzles	17.0%	Not measured	Only DeepSeek V4 Pro 0423 measured
LMArena Text: hard prompts	1461	1415	DeepSeek V4 Pro 0423
LMArena Text: expert	1463	1427	DeepSeek V4 Pro 0423
LMArena Text: hard prompts in English	1466	1429	DeepSeek V4 Pro 0423
LMArena Vision: diagrams	Not measured	1249	Only Qwen3.5-27B measured
LMArena Vision: homework	Not measured	1256	Only Qwen3.5-27B 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 index20

Math benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.5-27B	Leader
OTIS Mock AIME	96.7%	Not measured	Only DeepSeek V4 Pro 0423 measured
FrontierMath, tiers 1 to 3	45.3%	Not measured	Only DeepSeek V4 Pro 0423 measured
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	1429	DeepSeek V4 Pro 0423
LMArena Text: mathematical work	1455	1429	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

Knowledge benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.5-27B	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	1437	DeepSeek V4 Pro 0423
LMArena Text: medicine and healthcare	1466	1425	DeepSeek V4 Pro 0423
LMArena Text: legal and government	1464	1405	DeepSeek V4 Pro 0423
LMArena Text: business and finance	1443	1399	DeepSeek V4 Pro 0423
Neither model has a result on the other 7 boards here

Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared

Web design benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.5-27B	Leader
Design Arena: websites	1234	Not measured	Only DeepSeek V4 Pro 0423 measured
Design Arena: UI components	1219	Not measured	Only DeepSeek V4 Pro 0423 measured
Design Arena: game dev	1228	Not measured	Only DeepSeek V4 Pro 0423 measured
Design Arena: data viz	1203	Not measured	Only DeepSeek V4 Pro 0423 measured
Design Arena: 3D	1253	Not measured	Only DeepSeek V4 Pro 0423 measured
Design Arena: all code categories	1240	Not measured	Only DeepSeek V4 Pro 0423 measured
Design Arena: SVG	1139	Not measured	Only DeepSeek V4 Pro 0423 measured
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 index190

General benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.5-27B	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	75.6%	Only Qwen3.5-27B measured
Long context reasoning (AA-LCR)	Not measured	77.7%	Only Qwen3.5-27B measured
DTBench	90.7%	82.4%	DeepSeek V4 Pro 0423
LMCA	41.2%	34.0%	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	1409	DeepSeek V4 Pro 0423
LMArena Text: instruction following	1445	1393	DeepSeek V4 Pro 0423
LMArena Text: creative writing	1446	1362	DeepSeek V4 Pro 0423
LMArena Vision	Not measured	1241	Only Qwen3.5-27B measured
LMArena Text: English	1459	1425	DeepSeek V4 Pro 0423
LMArena Text: non-English	1439	1390	DeepSeek V4 Pro 0423
LMArena Text: Chinese	1481	1478	DeepSeek V4 Pro 0423
LMArena Text: German	1458	1392	DeepSeek V4 Pro 0423
LMArena Text: Japanese	1446	1346	DeepSeek V4 Pro 0423
LMArena Text: Korean	1409	1359	DeepSeek V4 Pro 0423
LMArena Text: French	1473	1410	DeepSeek V4 Pro 0423
LMArena Text: Spanish	1457	1407	DeepSeek V4 Pro 0423
LMArena Text: Russian	1453	1390	DeepSeek V4 Pro 0423
LMArena Text: Polish	1461	1375	DeepSeek V4 Pro 0423
LMArena Text: multi-turn	1468	1410	DeepSeek V4 Pro 0423
LMArena Text: longer queries	1458	1413	DeepSeek V4 Pro 0423
LMArena Text: writing and language	1447	1380	DeepSeek V4 Pro 0423
LMArena Text: entertainment and media	1431	1363	DeepSeek V4 Pro 0423
LMArena Vision: English	Not measured	1246	Only Qwen3.5-27B measured
LMArena Vision: Chinese	Not measured	1301	Only Qwen3.5-27B measured
LMArena Vision: reading text	Not measured	1246	Only Qwen3.5-27B measured
LMArena Vision: humor	Not measured	1227	Only Qwen3.5-27B measured
LMArena Vision: captioning	Not measured	1170	Only Qwen3.5-27B measured
LMArena Vision: recognizing things	Not measured	1210	Only Qwen3.5-27B measured
LMArena Vision: creative writing	Not measured	1224	Only Qwen3.5-27B 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

Image generation benchmarks for DeepSeek V4 Pro 0423 and Qwen3.5-27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.5-27B	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

Qwen3.5-27B$0.0010Fits in one request

Qwen3.5-27B costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Pro 0423$0.077Fits in one request

Qwen3.5-27B$0.014Fits in one request

Qwen3.5-27B costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Pro 0423$0.301Fits in one request

Qwen3.5-27B$0.055Fits in one request

Qwen3.5-27B 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 Qwen3.5-27B
Specification	DeepSeek V4 Pro 0423	Qwen3.5-27B
Maker	DeepSeek	Qwen
Weights	Open weights	Open weights
Released	April 24, 2026	February 25, 2026
Context window	1.05M	262K
Input $/Mtok	$1.358	$0.195
Output $/Mtok	$2.976	$1.56
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 Qwen3.5-27B for coding?

Only DeepSeek V4 Pro 0423 has a coding score, 38.7 on the Vibe Coding Index; Qwen3.5-27B has none, so the two cannot be ranked on coding. Pick Qwen3.5-27B for output tokens 1.9x cheaper ($1.56 vs $2.976 per million).

Which is cheaper, DeepSeek V4 Pro 0423 or Qwen3.5-27B?

DeepSeek V4 Pro 0423 costs $1.358 per million input tokens and $2.976 per million output tokens; Qwen3.5-27B costs $0.195 and $1.56. Cost per solved task is known for DeepSeek V4 Pro 0423 only.

Which has the bigger context window, DeepSeek V4 Pro 0423 or Qwen3.5-27B?

DeepSeek V4 Pro 0423, with 1.05M tokens against 262K.

How many benchmarks is the DeepSeek V4 Pro 0423 vs Qwen3.5-27B comparison based on?

37 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 Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.

DeepSeek V4 Pro 0423 or Qwen3.5-27B: 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

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