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DeepSeek V4 Flash 0423 vs Gemma 2 27B

Updated October 21, 2026. 31 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace DeepSeek V4 Flash 0423 Replace Gemma 2 27B
DeepSeek V4 Flash 0423DeepSeek
Coding
No data
Overall
24.4
Output $/M
$0.28
31
Gemma 2 27BGoogle
Coding
No data
Overall
No data
Output $/M
$0.65
0
vs boards won of 31 shared

Decision reading

Neither model has a coding score yet, so the two cannot be ranked on coding.

DeepSeek V4 Flash 0423 has the edge where output tokens cost 2.3x less, and where the context window is larger, 1.05M against 8K tokens.

Model ADeepSeek V4 Flash 0423DeepSeekNot benchmarked1 independent benchmarkNo cost per solved taskModel BGemma 2 27BGoogleNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
31
DeepSeek V4 Flash 0423 only
17
Gemma 2 27B only
5
Like-for-like categories
6 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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, Gemma 2 27B not benchmarked.
Widest category
Reasoning, 42.1 points apart on average over 3 shared benchmarks.
Widest single gap
LMArena Text: expert: DeepSeek V4 Flash 0423 1441 against Gemma 2 27B 1171, 44.9 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Flash 0423 $0.28 against Gemma 2 27B $0.65 per million output tokens, 2.3x apart.
Context window
DeepSeek V4 Flash 0423 takes 1.05M tokens, Gemma 2 27B 8K.

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 0423BarsGemma 2 27BTies
Coding20
Intelligence10
Reasoning30
Math20
Knowledge40
General190
All shared boards310
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
Coding benchmarks for DeepSeek V4 Flash 0423 and Gemma 2 27B
BenchmarkDeepSeek V4 Flash 0423Gemma 2 27BLeader
LMArena WebDev1582Not measuredOnly DeepSeek V4 Flash 0423 measured
AA Coding Index52.0Not measuredOnly DeepSeek V4 Flash 0423 measured
WeirdML45.6%Not measuredOnly DeepSeek V4 Flash 0423 measured
ALE-Bench678.2Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: coding14571211DeepSeek V4 Flash 0423
LMArena Text: software and IT14531228DeepSeek V4 Flash 0423
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 30 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V4 Flash 0423 and Gemma 2 27B
BenchmarkDeepSeek V4 Flash 0423Gemma 2 27BLeader
AA Intelligence Index24.4Not measuredOnly DeepSeek V4 Flash 0423 measured
Epoch Capabilities Index146.1122.1DeepSeek V4 Flash 0423
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Agentic work benchmarks for DeepSeek V4 Flash 0423 and Gemma 2 27B
BenchmarkDeepSeek V4 Flash 0423Gemma 2 27BLeader
AA Agentic Index26.3Not measuredOnly DeepSeek V4 Flash 0423 measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
Reasoning benchmarks for DeepSeek V4 Flash 0423 and Gemma 2 27B
BenchmarkDeepSeek V4 Flash 0423Gemma 2 27BLeader
GPQA Diamond (Epoch AI run)Not measured36.5%Only Gemma 2 27B measured
SimpleBench46.3%Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: hard prompts14441198DeepSeek V4 Flash 0423
LMArena Text: expert14411171DeepSeek V4 Flash 0423
LMArena Text: hard prompts in English14491206DeepSeek V4 Flash 0423
Neither model has a result on the other 16 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for DeepSeek V4 Flash 0423 and Gemma 2 27B
BenchmarkDeepSeek V4 Flash 0423Gemma 2 27BLeader
OTIS Mock AIMENot measured1.4%Only Gemma 2 27B measured
MATH level 5Not measured27.9%Only Gemma 2 27B measured
LMArena Text: math14261212DeepSeek V4 Flash 0423
LMArena Text: mathematical work14321221DeepSeek V4 Flash 0423
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for DeepSeek V4 Flash 0423 and Gemma 2 27B
BenchmarkDeepSeek V4 Flash 0423Gemma 2 27BLeader
LegalBench (Vals AI)Not measured68.0%Only Gemma 2 27B measured
MMLUNot measured75.7%Only Gemma 2 27B measured
LMArena Text: science14461228DeepSeek V4 Flash 0423
LMArena Text: medicine and healthcare14481198DeepSeek V4 Flash 0423
LMArena Text: legal and government14391241DeepSeek V4 Flash 0423
LMArena Text: business and finance14251208DeepSeek V4 Flash 0423
Neither model has a result on the other 11 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 Gemma 2 27B
BenchmarkDeepSeek V4 Flash 0423Gemma 2 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 index190
General benchmarks for DeepSeek V4 Flash 0423 and Gemma 2 27B
BenchmarkDeepSeek V4 Flash 0423Gemma 2 27BLeader
DTBench86.4%48.0%DeepSeek V4 Flash 0423
LMCA35.9%7.1%DeepSeek V4 Flash 0423
LMArena Text14321231DeepSeek V4 Flash 0423
LMArena Text: instruction following14211206DeepSeek V4 Flash 0423
LMArena Text: creative writing14031240DeepSeek V4 Flash 0423
LMArena Text: English14411242DeepSeek V4 Flash 0423
LMArena Text: non-English14191217DeepSeek V4 Flash 0423
LMArena Text: Chinese14631221DeepSeek V4 Flash 0423
LMArena Text: German14191209DeepSeek V4 Flash 0423
LMArena Text: Japanese13891175DeepSeek V4 Flash 0423
LMArena Text: Korean13831174DeepSeek V4 Flash 0423
LMArena Text: French14391248DeepSeek V4 Flash 0423
LMArena Text: Spanish14361228DeepSeek V4 Flash 0423
LMArena Text: Russian14271234DeepSeek V4 Flash 0423
LMArena Text: Polish14361277DeepSeek V4 Flash 0423
LMArena Text: multi-turn14491224DeepSeek V4 Flash 0423
LMArena Text: longer queries14341230DeepSeek V4 Flash 0423
LMArena Text: writing and language14101249DeepSeek V4 Flash 0423
LMArena Text: entertainment and media14001212DeepSeek V4 Flash 0423
Neither model has a result on the other 27 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 Gemma 2 27B
BenchmarkDeepSeek V4 Flash 0423Gemma 2 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
Gemma 2 27B$0.0010Fits 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
Gemma 2 27B$0.034Exceeds the context window

DeepSeek V4 Flash 0423 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Flash 0423$0.031Fits in one request
Gemma 2 27B$0.137Exceeds the context window

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 Gemma 2 27B
SpecificationDeepSeek V4 Flash 0423Google Gemma 2 27B
MakerDeepSeekGoogle
WeightsOpen weightsOpen weights
ReleasedApril 24, 2026July 13, 2024
Context window1.05M8K
Input $/Mtok$0.14$0.65
Output $/Mtok$0.28$0.65
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it10
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4 Flash 0423 better than Gemma 2 27B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. DeepSeek V4 Flash 0423 has the edge where output tokens cost 2.3x less, and where the context window is larger, 1.05M against 8K tokens.

Which is cheaper, DeepSeek V4 Flash 0423 or Gemma 2 27B?

DeepSeek V4 Flash 0423 costs $0.14 per million input tokens and $0.28 per million output tokens; Gemma 2 27B costs $0.65 and $0.65. Cost per solved task is known for neither of them.

Which has the bigger context window, DeepSeek V4 Flash 0423 or Gemma 2 27B?

DeepSeek V4 Flash 0423, with 1.05M tokens against 8K.

How many benchmarks is the DeepSeek V4 Flash 0423 vs Gemma 2 27B comparison based on?

31 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 Gemma 2 27B's on 0. Where only one model has a result, the page says so instead of naming a winner.

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