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DeepSeek V4 Flash 0423 vs Qwen3.8 27B

Qwen3.8 27B leads DeepSeek V4 Flash 0423 on the Artificial Analysis Intelligence Index, 33.7 to 24.4, and DeepSeek V4 Flash 0423 costs 9.1x less per output token ($0.28 vs $2.55 per million). Updated October 22, 2026; 37 benchmarks have a result for both.

Replace DeepSeek V4 Flash 0423 Replace Qwen3.8 27B
DeepSeek V4 Flash 0423DeepSeek
Coding
No data
Overall
No data
Output $/M
$0.28
7
Qwen3.8 27BQwen
Coding
74.2
Overall
59.6
Output $/M
$2.55
30
vs boards won of 37 shared

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

Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; DeepSeek V4 Flash 0423 has none, so the two cannot be ranked on coding.

Pick DeepSeek V4 Flash 0423 for output tokens 9.1x cheaper ($0.28 vs $2.55 per million) and a larger context window (1.05M against 1M tokens).

Model ADeepSeek V4 Flash 0423DeepSeekNot benchmarked1 independent benchmarkNo cost per solved taskModel BQwen3.8 27BQwen74.2/1004 independent benchmarks$1.26 per solved task
Shared results
37
DeepSeek V4 Flash 0423 only
12
Qwen3.8 27B only
48
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.

At a glance

Coding index
DeepSeek V4 Flash 0423 not benchmarked, Qwen3.8 27B 74.2 from 4 benchmarks.
Widest category
Agentic work, 19.5 points apart on average over 1 shared benchmark.
Widest single gap
AA Agentic Index: DeepSeek V4 Flash 0423 26.3 against Qwen3.8 27B 45.8, 19.5 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Flash 0423 $0.28 against Qwen3.8 27B $2.55 per million output tokens, 9.1x apart.
Context window
DeepSeek V4 Flash 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
CategoryDeepSeek V4 Flash 0423BarsQwen3.8 27BTies
Coding06
Intelligence02
Agentic work01
Reasoning03
Math02
Knowledge13
General613
All shared boards730
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
Coding benchmarks for DeepSeek V4 Flash 0423 and Qwen3.8 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.8 27BLeader
LiveBench codingNot measured86.8%Only Qwen3.8 27B measured
LMArena WebDev15821593Qwen3.8 27B
AA Coding Index52.068.1Qwen3.8 27B
LiveCodeBench (Vals AI)Not measured84.0%Only Qwen3.8 27B measured
SWE-bench Verified (Vals AI)Not measured86.0%Only Qwen3.8 27B measured
Vibe Code Bench (Vals AI)Not measured64.8%Only Qwen3.8 27B measured
Code migration (Vals AI)Not measured14.2%Only Qwen3.8 27B measured
ProgramBench (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
IOI (Vals AI)Not measured39.1%Only Qwen3.8 27B measured
Terminal-Bench 2.1 (Vals AI)Not measured58.4%Only Qwen3.8 27B measured
SkillsBench (Vals AI)Not measured38.1%Only Qwen3.8 27B measured
CyberBench (Vals AI)Not measured67.8%Only Qwen3.8 27B measured
SciCodeNot measured46.6%Only Qwen3.8 27B measured
WeirdML45.6%Not measuredOnly DeepSeek V4 Flash 0423 measured
ALE-Bench678.2Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: coding14571483Qwen3.8 27B
LMArena Text: software and IT14531478Qwen3.8 27B
LMArena WebDev: React15851602Qwen3.8 27B
LMArena WebDev: HTML15581570Qwen3.8 27B
LMArena WebDev: from an imageNot measured1577Only Qwen3.8 27B measured
Neither model has a result on the other 19 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for DeepSeek V4 Flash 0423 and Qwen3.8 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.8 27BLeader
AA Intelligence Index24.433.7Qwen3.8 27B
Epoch Capabilities Index146.1151.7Qwen3.8 27B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for DeepSeek V4 Flash 0423 and Qwen3.8 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.8 27BLeader
AA Agentic Index26.345.8Qwen3.8 27B
LiveBench researchNot measured59.4%Only Qwen3.8 27B measured
LiveBench browserNot measured32.0%Only Qwen3.8 27B measured
LiveBench long agenticNot measured27.9%Only Qwen3.8 27B measured
Finance Agent (Vals AI)Not measured48.5%Only Qwen3.8 27B measured
Tax Agent Bench (Vals AI)Not measured30.9%Only Qwen3.8 27B measured
Legal Research Bench (Vals AI)Not measured36.1%Only Qwen3.8 27B measured
Excel Modeling Benchmark (Vals AI)Not measured59.7%Only Qwen3.8 27B measured
Terminal-Bench-Science (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
APEX-AgentsNot measured47.5%Only Qwen3.8 27B measured
Neither model has a result on the other 31 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
Reasoning benchmarks for DeepSeek V4 Flash 0423 and Qwen3.8 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.8 27BLeader
GPQA DiamondNot measured88.9%Only Qwen3.8 27B measured
LiveBench reasoningNot measured80.0%Only Qwen3.8 27B measured
MMMU Pro (Vals AI)Not measured83.9%Only Qwen3.8 27B measured
GPQA Diamond (Artificial Analysis run)Not measured90.5%Only Qwen3.8 27B measured
Humanity's Last Exam (Artificial Analysis run)Not measured33.9%Only Qwen3.8 27B measured
ARC-AGINot measured87.5%Only Qwen3.8 27B measured
ARC-AGI-2Not measured42.4%Only Qwen3.8 27B measured
SimpleBench46.3%Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: hard prompts14441459Qwen3.8 27B
LMArena Text: expert14411479Qwen3.8 27B
LMArena Text: hard prompts in English14491461Qwen3.8 27B
LMArena Vision: diagramsNot measured1285Only Qwen3.8 27B measured
LMArena Vision: homeworkNot measured1295Only Qwen3.8 27B measured
Kagi LLM Benchmark52.2%Not measuredOnly DeepSeek V4 Flash 0423 measured
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for DeepSeek V4 Flash 0423 and Qwen3.8 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.8 27BLeader
ProofBenchNot measured16.0%Only Qwen3.8 27B measured
LMArena Text: math14261455Qwen3.8 27B
LMArena Text: mathematical work14321458Qwen3.8 27B
Neither model has a result on the other 10 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
Knowledge benchmarks for DeepSeek V4 Flash 0423 and Qwen3.8 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.8 27BLeader
MMLU ProNot measured84.3%Only Qwen3.8 27B measured
LegalBench (Vals AI)Not measured82.4%Only Qwen3.8 27B measured
TaxEval (Vals AI)Not measured70.8%Only Qwen3.8 27B measured
MortgageTax (Vals AI)Not measured64.9%Only Qwen3.8 27B measured
MedCode (Vals AI)Not measured28.7%Only Qwen3.8 27B measured
LMArena Text: science14461467Qwen3.8 27B
LMArena Text: medicine and healthcare14481437DeepSeek V4 Flash 0423
LMArena Text: legal and government14391447Qwen3.8 27B
LMArena Text: business and finance14251438Qwen3.8 27B
Neither model has a result on the other 9 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for DeepSeek V4 Flash 0423 and Qwen3.8 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.8 27BLeader
Design Arena: websites1207Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: UI components1162Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: game dev1196Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: data viz1127Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: 3D1188Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: all code categories1205Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: SVG1155Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: ASCII art1109Not measuredOnly DeepSeek V4 Flash 0423 measured
LiveBench designNot measured75.9%Only Qwen3.8 27B measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index613
General benchmarks for DeepSeek V4 Flash 0423 and Qwen3.8 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.8 27BLeader
LiveBench data analysisNot measured76.6%Only Qwen3.8 27B measured
SAGE (Vals AI)Not measured52.4%Only Qwen3.8 27B measured
MedScribe (Vals AI)Not measured83.8%Only Qwen3.8 27B measured
Long context reasoning (AA-LCR)Not measured82.0%Only Qwen3.8 27B measured
DTBench86.4%88.0%Qwen3.8 27B
LMCA35.9%41.4%Qwen3.8 27B
Surface Evolver BenchNot measured45.0%Only Qwen3.8 27B measured
LMArena Text14321440Qwen3.8 27B
LMArena Text: instruction following14211436Qwen3.8 27B
LMArena Text: creative writing14031383DeepSeek V4 Flash 0423
LMArena VisionNot measured1269Only Qwen3.8 27B measured
LMArena Text: English14411448Qwen3.8 27B
LMArena Text: non-English14191428Qwen3.8 27B
LMArena Text: Chinese14631501Qwen3.8 27B
LMArena Text: German14191443Qwen3.8 27B
LMArena Text: Japanese13891387DeepSeek V4 Flash 0423
LMArena Text: Korean13831384Qwen3.8 27B
LMArena Text: French14391465Qwen3.8 27B
LMArena Text: Spanish14361449Qwen3.8 27B
LMArena Text: Russian14271415DeepSeek V4 Flash 0423
LMArena Text: Polish14361439Qwen3.8 27B
LMArena Text: multi-turn14491440DeepSeek V4 Flash 0423
LMArena Text: longer queries14341448Qwen3.8 27B
LMArena Text: writing and language14101405DeepSeek V4 Flash 0423
LMArena Text: entertainment and media14001393DeepSeek V4 Flash 0423
LMArena Vision: EnglishNot measured1267Only Qwen3.8 27B measured
LMArena Vision: ChineseNot measured1326Only Qwen3.8 27B measured
LMArena Vision: reading textNot measured1281Only Qwen3.8 27B measured
LMArena Vision: humorNot measured1235Only Qwen3.8 27B measured
LMArena Vision: creative writingNot measured1237Only Qwen3.8 27B measured
Neither model has a result on the other 16 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V4 Flash 0423 and Qwen3.8 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.8 27BLeader
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 0423$0.0003Fits in one request
Qwen3.8 27B$0.0017Fits in one request

DeepSeek V4 Flash 0423 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Flash 0423$0.0078Fits in one request
Qwen3.8 27B$0.029Fits in one request

DeepSeek V4 Flash 0423 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Flash 0423$0.031Fits in one request
Qwen3.8 27B$0.111Fits in one request

DeepSeek V4 Flash 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 Flash 0423 and Qwen3.8 27B
SpecificationDeepSeek V4 Flash 0423Qwen3.8 27B
MakerDeepSeekQwen
WeightsOpen weightsOpen weights
ReleasedApril 24, 2026August 14, 2026
Context window1.05M1M
Input $/Mtok$0.14$0.425
Output $/Mtok$0.28$2.55
Coding indexNot benchmarked74.2
Independent measurements behind it14
Cost per solved taskNot computable$1.26
SourceOpenRouter listingOpenRouter listing

Questions

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

Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; DeepSeek V4 Flash 0423 has none, so the two cannot be ranked on coding. Pick DeepSeek V4 Flash 0423 for output tokens 9.1x cheaper ($0.28 vs $2.55 per million) and a larger context window (1.05M against 1M tokens).

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

DeepSeek V4 Flash 0423 costs $0.14 per million input tokens and $0.28 per million output tokens; Qwen3.8 27B costs $0.425 and $2.55. Cost per solved task is known for Qwen3.8 27B only.

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

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

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

37 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Flash 0423's coding index rests on 1 independent benchmark 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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