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DeepSeek V4 Flash 0423 vs Qwen3.6 27B

DeepSeek V4 Flash 0423 leads Qwen3.6 27B on the Artificial Analysis Intelligence Index, 24.4 to 21.4, and DeepSeek V4 Flash 0423 costs 9.6x less per output token ($0.28 vs $2.70 per million). Updated October 21, 2026; 6 benchmarks have a result for both.

Replace DeepSeek V4 Flash 0423 Replace Qwen3.6 27B
DeepSeek V4 Flash 0423DeepSeek
Coding
No data
Overall
No data
Output $/M
$0.28
4
Qwen3.6 27BQwen
Coding
36.8
Overall
45.3
Output $/M
$2.70
2
vs boards won of 6 shared

DeepSeek V4 Flash 0423 or Qwen3.6 27B: which to pick

Only Qwen3.6 27B has a coding score, 36.8 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 9.6x cheaper ($0.28 vs $2.70 per million) and a larger context window (1.05M against 262K tokens).

Model ADeepSeek V4 Flash 0423DeepSeekNot benchmarked1 independent benchmarkNo cost per solved taskModel BQwen3.6 27BQwen36.8/1002 independent benchmarks$1.30 per solved task
Shared results
6
DeepSeek V4 Flash 0423 only
43
Qwen3.6 27B only
18
Like-for-like categories
4 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, Qwen3.6 27B 36.8 from 2 benchmarks.
Widest category
Agentic work, 7.8 points apart on average over 1 shared benchmark.
Widest single gap
DTBench: DeepSeek V4 Flash 0423 86.4% against Qwen3.6 27B 78.1%, 8.3 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Flash 0423 $0.28 against Qwen3.6 27B $2.70 per million output tokens, 9.6x apart.
Context window
DeepSeek V4 Flash 0423 takes 1.05M tokens, Qwen3.6 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
CategoryDeepSeek V4 Flash 0423BarsQwen3.6 27BTies
Coding01
Intelligence11
Agentic work10
General20
All shared boards42
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
Coding benchmarks for DeepSeek V4 Flash 0423 and Qwen3.6 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.6 27BLeader
LMArena WebDev1582Not measuredOnly DeepSeek V4 Flash 0423 measured
AA Coding Index52.053.7Qwen3.6 27B
SWE-bench Verified (Vals AI)Not measured70.0%Only Qwen3.6 27B measured
Vibe Code Bench (Vals AI)Not measured11.9%Only Qwen3.6 27B measured
SciCodeNot measured42.8%Only Qwen3.6 27B measured
Terminal-Bench Hard (Artificial Analysis run)Not measured34.8%Only Qwen3.6 27B measured
WeirdML45.6%Not measuredOnly DeepSeek V4 Flash 0423 measured
ALE-Bench678.2Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: coding1457Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: software and IT1453Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena WebDev: React1585Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena WebDev: HTML1558Not measuredOnly DeepSeek V4 Flash 0423 measured
Neither model has a result on the other 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
Intelligence benchmarks for DeepSeek V4 Flash 0423 and Qwen3.6 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.6 27BLeader
AA Intelligence Index24.421.4DeepSeek V4 Flash 0423
Epoch Capabilities Index146.1146.2Qwen3.6 27B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for DeepSeek V4 Flash 0423 and Qwen3.6 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.6 27BLeader
AA Agentic Index26.318.5DeepSeek V4 Flash 0423
τ²-Bench TelecomNot measured94.2%Only Qwen3.6 27B measured
Neither model has a result on the other 39 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Reasoning benchmarks for DeepSeek V4 Flash 0423 and Qwen3.6 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.6 27BLeader
GPQA Diamond (Artificial Analysis run)Not measured84.2%Only Qwen3.6 27B measured
Humanity's Last Exam (Artificial Analysis run)Not measured23.1%Only Qwen3.6 27B measured
GPQA Diamond (Epoch AI run)Not measured85.9%Only Qwen3.6 27B measured
SimpleBench46.3%Not measuredOnly DeepSeek V4 Flash 0423 measured
Mystery game puzzlesNot measured7.0%Only Qwen3.6 27B measured
LMArena Text: hard prompts1444Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: expert1441Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: hard prompts in English1449Not measuredOnly DeepSeek V4 Flash 0423 measured
Kagi LLM Benchmark52.2%Not measuredOnly DeepSeek V4 Flash 0423 measured
Neither model has a result on the other 13 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Math benchmarks for DeepSeek V4 Flash 0423 and Qwen3.6 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.6 27BLeader
OTIS Mock AIMENot measured91.1%Only Qwen3.6 27B measured
FrontierMath, tiers 1 to 3Not measured35.1%Only Qwen3.6 27B measured
LMArena Text: math1426Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: mathematical work1432Not measuredOnly DeepSeek V4 Flash 0423 measured
Neither model has a result on the other 9 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Knowledge benchmarks for DeepSeek V4 Flash 0423 and Qwen3.6 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.6 27BLeader
CaseLaw (Vals AI)Not measured53.2%Only Qwen3.6 27B measured
CorpFin (Vals AI)Not measured62.3%Only Qwen3.6 27B measured
TaxEval (Vals AI)Not measured71.3%Only Qwen3.6 27B measured
MortgageTax (Vals AI)Not measured68.3%Only Qwen3.6 27B measured
LMArena Text: science1446Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: medicine and healthcare1448Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: legal and government1439Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: business and finance1425Not measuredOnly DeepSeek V4 Flash 0423 measured
Neither model has a result on the other 10 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for DeepSeek V4 Flash 0423 and Qwen3.6 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.6 27BLeader
Design Arena: websites1208Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: UI components1165Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: game dev1197Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: data viz1127Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: 3D1189Not 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
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
General benchmarks for DeepSeek V4 Flash 0423 and Qwen3.6 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.6 27BLeader
SAGE (Vals AI)Not measured45.6%Only Qwen3.6 27B measured
IFBenchNot measured67.6%Only Qwen3.6 27B measured
Long context reasoning (AA-LCR)Not measured77.3%Only Qwen3.6 27B measured
DTBench86.4%78.1%DeepSeek V4 Flash 0423
LMCA35.9%34.5%DeepSeek V4 Flash 0423
LMArena Text1432Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: instruction following1421Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: creative writing1403Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: English1441Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: non-English1419Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: Chinese1463Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: German1419Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: Japanese1389Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: Korean1383Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: French1439Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: Spanish1436Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: Russian1427Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: Polish1436Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: multi-turn1449Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: longer queries1434Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: writing and language1410Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: entertainment and media1400Not measuredOnly DeepSeek V4 Flash 0423 measured
Neither model has a result on the other 24 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 Qwen3.6 27B
BenchmarkDeepSeek V4 Flash 0423Qwen3.6 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
Qwen3.6 27B$0.0018Fits in one request

DeepSeek V4 Flash 0423 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Flash 0423$0.0073Fits in one request
Qwen3.6 27B$0.031Fits in one request

DeepSeek V4 Flash 0423 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Flash 0423$0.029Fits in one request
Qwen3.6 27B$0.117Fits 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 Qwen3.6 27B
SpecificationDeepSeek V4 Flash 0423Qwen3.6 27B
MakerDeepSeekQwen
WeightsOpen weightsOpen weights
ReleasedApril 24, 2026April 27, 2026
Context window1.05M262K
Input $/Mtok$0.13$0.45
Output $/Mtok$0.28$2.70
Coding indexNot benchmarked36.8
Independent measurements behind it12
Cost per solved taskNot computable$1.30
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4 Flash 0423 better than Qwen3.6 27B for coding?

Only Qwen3.6 27B has a coding score, 36.8 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 9.6x cheaper ($0.28 vs $2.70 per million) and a larger context window (1.05M against 262K tokens).

Which is cheaper, DeepSeek V4 Flash 0423 or Qwen3.6 27B?

DeepSeek V4 Flash 0423 costs $0.13 per million input tokens and $0.28 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Cost per solved task is known for Qwen3.6 27B only.

Which has the bigger context window, DeepSeek V4 Flash 0423 or Qwen3.6 27B?

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

How many benchmarks is the DeepSeek V4 Flash 0423 vs Qwen3.6 27B comparison based on?

6 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Flash 0423's coding index rests on 1 independent benchmark and Qwen3.6 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.

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