DeepSeek V4 Pro 0423 vs Qwen3.8 27B

Qwen3.8 27B leads DeepSeek V4 Pro 0423 on coding, 74.2 to 38.7 on the Vibe Coding Index, at about the same output price ($2.55 vs $2.976 per million). Updated October 22, 2026; 56 benchmarks have a result for both.

Replace DeepSeek V4 Pro 0423 Replace Qwen3.8 27B

DeepSeek V4 Pro 0423DeepSeek

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

Qwen3.8 27BQwen

Coding: 74.2
Overall: 59.6
Output $/M: $2.55

vs boards won of 56 shared, 2 tied

DeepSeek V4 Pro 0423 or Qwen3.8 27B: which to pick

Pick Qwen3.8 27B for coding: it scores 74.2 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423, though on 4 benchmarks against 5.

Pick DeepSeek V4 Pro 0423 for a lower cost per solved task ($0.57 against $1.26) 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 BQwen3.8 27BQwen74.2/1004 independent benchmarks$1.26 per solved task

562829

Shared results: 56
DeepSeek V4 Pro 0423 only: 28
Qwen3.8 27B only: 29
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
Qwen3.8 27B
Qwen3.8 27B scores 74.2 against 38.7.
5 and 4 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.8 27B
Qwen3.8 27B leads, 33.7 against 30.4.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 27B
Qwen3.8 27B leads, 45.8 against 26.3.
Same board, both measured
Reasoning
GPQA Diamond
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 89.4% against 88.9%.
Same board, both measured
Knowledge
MMLU Pro
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 87.3% against 84.3%.
Same board, both measured
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 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.8 27B
DeepSeek V4 Pro 0423 $0.0028, Qwen3.8 27B $0.0017 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 27B $1.26.
Published attempt cost and pass rate

At a glance

Coding index: DeepSeek V4 Pro 0423 38.7 from 5 benchmarks, Qwen3.8 27B 74.2 from 4 benchmarks.
Widest category: Coding, 11.0 points apart on average over 13 shared benchmarks.
Widest single gap: LMArena WebDev: React: DeepSeek V4 Pro 0423 1441 against Qwen3.8 27B 1602, 26.6 points apart on a 0 to 100 scale.
Output price: DeepSeek V4 Pro 0423 $2.976 against Qwen3.8 27B $2.55 per million output tokens, 1.2x apart.
Context window: DeepSeek V4 Pro 0423 takes 1.05M tokens, Qwen3.8 27B 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 27B	Ties
Coding	3	9	1 tie
Intelligence	0	2
Agentic work	0	5
Reasoning	3	1
Math	0	2	1 tie
Knowledge	7	1
General	17	4
All shared boards	30	24	2 ties

CodingLike-for-likeSix of these boards feed the Vibe Coding Index39

Coding benchmarks for DeepSeek V4 Pro 0423 and Qwen3.8 27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 27B	Leader
LiveBench coding	Not measured	86.8%	Only Qwen3.8 27B measured
LMArena WebDev	1447	1593	Qwen3.8 27B
AA Coding Index	59.4	68.1	Qwen3.8 27B
LiveCodeBench (Vals AI)	87.5%	84.0%	DeepSeek V4 Pro 0423
SWE-bench Verified (Vals AI)	77.4%	86.0%	Qwen3.8 27B
Terminal-Bench 4 (Vals AI)	11.1%	Not measured	Only DeepSeek V4 Pro 0423 measured
Vibe Code Bench (Vals AI)	49.9%	64.8%	Qwen3.8 27B
Code migration (Vals AI)	26.2%	14.2%	DeepSeek V4 Pro 0423
ProgramBench (Vals AI)	0.0%	0.0%	Tie
IOI (Vals AI)	Not measured	39.1%	Only Qwen3.8 27B measured
Terminal-Bench 2.1 (Vals AI)	50.2%	58.4%	Qwen3.8 27B
SkillsBench (Vals AI)	51.3%	38.1%	DeepSeek V4 Pro 0423
CyberBench (Vals AI)	Not measured	67.8%	Only Qwen3.8 27B measured
SciCode	Not measured	46.6%	Only Qwen3.8 27B 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	Not measured	Only DeepSeek V4 Pro 0423 measured
LMArena Text: coding	1470	1483	Qwen3.8 27B
LMArena Text: software and IT	1469	1478	Qwen3.8 27B
LMArena WebDev: React	1441	1602	Qwen3.8 27B
LMArena WebDev: HTML	1435	1570	Qwen3.8 27B
LMArena WebDev: from an image	Not measured	1577	Only Qwen3.8 27B measured
Neither model has a result on the other 17 boards here

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

Intelligence benchmarks for DeepSeek V4 Pro 0423 and Qwen3.8 27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 27B	Leader
AA Intelligence Index	30.4	33.7	Qwen3.8 27B
Epoch Capabilities Index	149.1	151.7	Qwen3.8 27B

Agentic workLike-for-likePublic boards, shown as published, kept out of the index05

Agentic work benchmarks for DeepSeek V4 Pro 0423 and Qwen3.8 27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 27B	Leader
AA Agentic Index	26.3	45.8	Qwen3.8 27B
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
LiveBench research	Not measured	59.4%	Only Qwen3.8 27B measured
LiveBench browser	Not measured	32.0%	Only Qwen3.8 27B measured
LiveBench long agentic	Not measured	27.9%	Only Qwen3.8 27B measured
Finance Agent (Vals AI)	44.1%	48.5%	Qwen3.8 27B
Tax Agent Bench (Vals AI)	25.2%	30.9%	Qwen3.8 27B
Legal Research Bench (Vals AI)	23.1%	36.1%	Qwen3.8 27B
Excel Modeling Benchmark (Vals AI)	51.6%	59.7%	Qwen3.8 27B
Terminal-Bench-Science (Vals AI)	Not measured	0.0%	Only Qwen3.8 27B measured
APEX-Agents	Not measured	47.5%	Only Qwen3.8 27B measured
Vending-Bench 2	$3,285	Not measured	Only DeepSeek V4 Pro 0423 measured
Neither model has a result on the other 27 boards here

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

Reasoning benchmarks for DeepSeek V4 Pro 0423 and Qwen3.8 27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 27B	Leader
GPQA Diamond	89.4%	88.9%	DeepSeek V4 Pro 0423
LiveBench reasoning	Not measured	80.0%	Only Qwen3.8 27B measured
MMMU Pro (Vals AI)	Not measured	83.9%	Only Qwen3.8 27B measured
GPQA Diamond (Artificial Analysis run)	Not measured	90.5%	Only Qwen3.8 27B measured
Humanity's Last Exam (Artificial Analysis run)	Not measured	33.9%	Only Qwen3.8 27B measured
GPQA Diamond (Epoch AI run)	90.9%	Not measured	Only DeepSeek V4 Pro 0423 measured
ARC-AGI	Not measured	87.5%	Only Qwen3.8 27B measured
ARC-AGI-2	Not measured	42.4%	Only Qwen3.8 27B 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	1459	DeepSeek V4 Pro 0423
LMArena Text: expert	1463	1479	Qwen3.8 27B
LMArena Text: hard prompts in English	1466	1461	DeepSeek V4 Pro 0423
LMArena Vision: diagrams	Not measured	1285	Only Qwen3.8 27B measured
LMArena Vision: homework	Not measured	1295	Only Qwen3.8 27B measured
Kagi LLM Benchmark	53.5%	Not measured	Only DeepSeek V4 Pro 0423 measured
Neither model has a result on the other 6 boards here

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

Math benchmarks for DeepSeek V4 Pro 0423 and Qwen3.8 27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 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%	16.0%	Tie
LMArena Text: math	1443	1455	Qwen3.8 27B
LMArena Text: mathematical work	1455	1458	Qwen3.8 27B
Neither model has a result on the other 7 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index71

Knowledge benchmarks for DeepSeek V4 Pro 0423 and Qwen3.8 27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 27B	Leader
MMLU Pro	87.3%	84.3%	DeepSeek V4 Pro 0423
LegalBench (Vals AI)	80.3%	82.4%	Qwen3.8 27B
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%	70.8%	DeepSeek V4 Pro 0423
MortgageTax (Vals AI)	Not measured	64.9%	Only Qwen3.8 27B measured
MedCode (Vals AI)	40.5%	28.7%	DeepSeek V4 Pro 0423
SimpleQA Verified	47.0%	Not measured	Only DeepSeek V4 Pro 0423 measured
LMArena Text: science	1470	1467	DeepSeek V4 Pro 0423
LMArena Text: medicine and healthcare	1466	1437	DeepSeek V4 Pro 0423
LMArena Text: legal and government	1464	1447	DeepSeek V4 Pro 0423
LMArena Text: business and finance	1443	1438	DeepSeek V4 Pro 0423
Neither model has a result on the other 6 boards here

Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared

Web design benchmarks for DeepSeek V4 Pro 0423 and Qwen3.8 27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 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
LiveBench design	Not measured	75.9%	Only Qwen3.8 27B measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index174

General benchmarks for DeepSeek V4 Pro 0423 and Qwen3.8 27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 27B	Leader
LiveBench data analysis	Not measured	76.6%	Only Qwen3.8 27B measured
Public Benefits Bench (Vals AI)	62.9%	Not measured	Only DeepSeek V4 Pro 0423 measured
SAGE (Vals AI)	Not measured	52.4%	Only Qwen3.8 27B measured
MedScribe (Vals AI)	75.1%	83.8%	Qwen3.8 27B
Long context reasoning (AA-LCR)	Not measured	82.0%	Only Qwen3.8 27B measured
DTBench	90.7%	88.0%	DeepSeek V4 Pro 0423
LMCA	41.2%	41.4%	Qwen3.8 27B
Surface Evolver Bench	40.0%	45.0%	Qwen3.8 27B
CL-bench Life	13.5%	Not measured	Only DeepSeek V4 Pro 0423 measured
LMArena Text	1451	1440	DeepSeek V4 Pro 0423
LMArena Text: instruction following	1445	1436	DeepSeek V4 Pro 0423
LMArena Text: creative writing	1446	1383	DeepSeek V4 Pro 0423
LMArena Vision	Not measured	1269	Only Qwen3.8 27B measured
LMArena Text: English	1459	1448	DeepSeek V4 Pro 0423
LMArena Text: non-English	1439	1428	DeepSeek V4 Pro 0423
LMArena Text: Chinese	1481	1501	Qwen3.8 27B
LMArena Text: German	1458	1443	DeepSeek V4 Pro 0423
LMArena Text: Japanese	1446	1387	DeepSeek V4 Pro 0423
LMArena Text: Korean	1409	1384	DeepSeek V4 Pro 0423
LMArena Text: French	1473	1465	DeepSeek V4 Pro 0423
LMArena Text: Spanish	1457	1449	DeepSeek V4 Pro 0423
LMArena Text: Russian	1453	1415	DeepSeek V4 Pro 0423
LMArena Text: Polish	1461	1439	DeepSeek V4 Pro 0423
LMArena Text: multi-turn	1468	1440	DeepSeek V4 Pro 0423
LMArena Text: longer queries	1458	1448	DeepSeek V4 Pro 0423
LMArena Text: writing and language	1447	1405	DeepSeek V4 Pro 0423
LMArena Text: entertainment and media	1431	1393	DeepSeek V4 Pro 0423
LMArena Vision: English	Not measured	1267	Only Qwen3.8 27B measured
LMArena Vision: Chinese	Not measured	1326	Only Qwen3.8 27B measured
LMArena Vision: reading text	Not measured	1281	Only Qwen3.8 27B measured
LMArena Vision: humor	Not measured	1235	Only Qwen3.8 27B measured
LMArena Vision: creative writing	Not measured	1237	Only Qwen3.8 27B 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 Pro 0423 and Qwen3.8 27B
Benchmark	DeepSeek V4 Pro 0423	Qwen3.8 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

Qwen3.8 27B$0.0017Fits in one request

Qwen3.8 27B costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Pro 0423$0.077Fits in one request

Qwen3.8 27B$0.029Fits in one request

Qwen3.8 27B costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Pro 0423$0.301Fits in one request

Qwen3.8 27B$0.111Fits in one request

Qwen3.8 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 Qwen3.8 27B
Specification	DeepSeek V4 Pro 0423	Qwen3.8 27B
Maker	DeepSeek	Qwen
Weights	Open weights	Open weights
Released	April 24, 2026	August 14, 2026
Context window	1.05M	1M
Input $/Mtok	$1.358	$0.425
Output $/Mtok	$2.976	$2.55
Coding index	38.7	74.2
Independent measurements behind it	5	4
Cost per solved task	$0.57	$1.26
Source	OpenRouter listing	OpenRouter listing

Questions

Is DeepSeek V4 Pro 0423 better than Qwen3.8 27B for coding?

Pick Qwen3.8 27B for coding: it scores 74.2 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423, though on 4 benchmarks against 5. Pick DeepSeek V4 Pro 0423 for a lower cost per solved task ($0.57 against $1.26) and a larger context window (1.05M against 1M tokens).

Which is cheaper, DeepSeek V4 Pro 0423 or Qwen3.8 27B?

DeepSeek V4 Pro 0423 costs $1.358 per million input tokens and $2.976 per million output tokens; Qwen3.8 27B costs $0.425 and $2.55. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0423 costs $0.57 and Qwen3.8 27B $1.26.

Which has the bigger context window, DeepSeek V4 Pro 0423 or Qwen3.8 27B?

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

How many benchmarks is the DeepSeek V4 Pro 0423 vs Qwen3.8 27B comparison based on?

56 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 27B's on 4. 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-pro-vs-qwen3-8-27b" title="DeepSeek V4 Pro 0423 vs Qwen3.8 27B, 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-pro-vs-qwen3-8-27b">LLMBenchmark.ai, DeepSeek V4 Pro 0423 vs Qwen3.8 27B</a></p>

DeepSeek V4 Pro 0423 or Qwen3.8 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

Compare next