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DeepSeek V4 Pro 0423 vs Gemma 2 27B

Updated October 21, 2026. 34 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace DeepSeek V4 Pro 0423 Replace Gemma 2 27B
DeepSeek V4 Pro 0423DeepSeek
Coding
38.7
Overall
30.4
Output $/M
$2.716
34
Gemma 2 27BGoogle
Coding
No data
Overall
No data
Output $/M
$0.65
0
vs boards won of 34 shared

Decision reading

Only DeepSeek V4 Pro 0423 has a coding score, 38.7 on the Vibe Coding Index; Gemma 2 27B has none, so the two cannot be ranked on coding.

Gemma 2 27B has the edge where output tokens cost 4.2x less.

Model ADeepSeek V4 Pro 0423DeepSeek38.7/1005 independent benchmarks$0.57 per solved taskModel BGemma 2 27BGoogleNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
34
DeepSeek V4 Pro 0423 only
49
Gemma 2 27B only
2
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 Pro 0423 38.7 from 5 benchmarks, Gemma 2 27B not benchmarked.
Widest category
Math, 57.6 points apart on average over 3 shared benchmarks.
Widest single gap
OTIS Mock AIME: DeepSeek V4 Pro 0423 96.7% against Gemma 2 27B 1.4%, 95.3 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Pro 0423 $2.716 against Gemma 2 27B $0.65 per million output tokens, 4.2x apart.
Context window
DeepSeek V4 Pro 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 Pro 0423BarsGemma 2 27BTies
Coding20
Intelligence10
Reasoning40
Math30
Knowledge50
General190
All shared boards340
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
Coding benchmarks for DeepSeek V4 Pro 0423 and Gemma 2 27B
BenchmarkDeepSeek V4 Pro 0423Gemma 2 27BLeader
LMArena WebDev1447Not measuredOnly DeepSeek V4 Pro 0423 measured
AA Coding Index59.4Not measuredOnly DeepSeek V4 Pro 0423 measured
LiveCodeBench (Vals AI)87.5%Not measuredOnly DeepSeek V4 Pro 0423 measured
SWE-bench Verified (Vals AI)77.4%Not measuredOnly DeepSeek V4 Pro 0423 measured
Terminal-Bench 4 (Vals AI)11.1%Not measuredOnly DeepSeek V4 Pro 0423 measured
Vibe Code Bench (Vals AI)49.9%Not measuredOnly DeepSeek V4 Pro 0423 measured
Code migration (Vals AI)26.2%Not measuredOnly DeepSeek V4 Pro 0423 measured
ProgramBench (Vals AI)0.0%Not measuredOnly DeepSeek V4 Pro 0423 measured
Terminal-Bench 2.1 (Vals AI)50.2%Not measuredOnly DeepSeek V4 Pro 0423 measured
SkillsBench (Vals AI)51.3%Not measuredOnly DeepSeek V4 Pro 0423 measured
FrontierCode17.6%Not measuredOnly DeepSeek V4 Pro 0423 measured
WeirdML48.9%Not measuredOnly DeepSeek V4 Pro 0423 measured
ALE-Bench1006.1Not measuredOnly DeepSeek V4 Pro 0423 measured
LMArena Text: coding14701211DeepSeek V4 Pro 0423
LMArena Text: software and IT14691228DeepSeek V4 Pro 0423
LMArena WebDev: React1441Not measuredOnly DeepSeek V4 Pro 0423 measured
LMArena WebDev: HTML1435Not measuredOnly DeepSeek V4 Pro 0423 measured
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 Gemma 2 27B
BenchmarkDeepSeek V4 Pro 0423Gemma 2 27BLeader
AA Intelligence Index30.4Not measuredOnly DeepSeek V4 Pro 0423 measured
Epoch Capabilities Index149.1122.1DeepSeek V4 Pro 0423
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Agentic work benchmarks for DeepSeek V4 Pro 0423 and Gemma 2 27B
BenchmarkDeepSeek V4 Pro 0423Gemma 2 27BLeader
AA Agentic Index26.3Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: agents building web apps1000Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: agents building full-stack apps948Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: agents building Godot games1059Not measuredOnly DeepSeek V4 Pro 0423 measured
Finance Agent (Vals AI)44.1%Not measuredOnly DeepSeek V4 Pro 0423 measured
Tax Agent Bench (Vals AI)25.2%Not measuredOnly DeepSeek V4 Pro 0423 measured
Legal Research Bench (Vals AI)23.1%Not measuredOnly DeepSeek V4 Pro 0423 measured
Excel Modeling Benchmark (Vals AI)51.6%Not measuredOnly DeepSeek V4 Pro 0423 measured
Vending-Bench 2$3,285Not measuredOnly DeepSeek V4 Pro 0423 measured
Neither model has a result on the other 30 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
Reasoning benchmarks for DeepSeek V4 Pro 0423 and Gemma 2 27B
BenchmarkDeepSeek V4 Pro 0423Gemma 2 27BLeader
GPQA Diamond89.4%Not measuredOnly DeepSeek V4 Pro 0423 measured
GPQA Diamond (Epoch AI run)90.9%36.5%DeepSeek V4 Pro 0423
Chess puzzles19.0%Not measuredOnly DeepSeek V4 Pro 0423 measured
Mystery game puzzles17.0%Not measuredOnly DeepSeek V4 Pro 0423 measured
LMArena Text: hard prompts14611198DeepSeek V4 Pro 0423
LMArena Text: expert14631171DeepSeek V4 Pro 0423
LMArena Text: hard prompts in English14661206DeepSeek V4 Pro 0423
Neither model has a result on the other 14 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for DeepSeek V4 Pro 0423 and Gemma 2 27B
BenchmarkDeepSeek V4 Pro 0423Gemma 2 27BLeader
OTIS Mock AIME96.7%1.4%DeepSeek V4 Pro 0423
FrontierMath, tiers 1 to 345.3%Not measuredOnly DeepSeek V4 Pro 0423 measured
FrontierMath, tier 42.4%Not measuredOnly DeepSeek V4 Pro 0423 measured
ProofBench16.0%Not measuredOnly DeepSeek V4 Pro 0423 measured
MATH level 5Not measured27.9%Only Gemma 2 27B measured
LMArena Text: math14431212DeepSeek V4 Pro 0423
LMArena Text: mathematical work14551221DeepSeek V4 Pro 0423
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for DeepSeek V4 Pro 0423 and Gemma 2 27B
BenchmarkDeepSeek V4 Pro 0423Gemma 2 27BLeader
MMLU Pro87.3%Not measuredOnly DeepSeek V4 Pro 0423 measured
LegalBench (Vals AI)80.3%68.0%DeepSeek V4 Pro 0423
CaseLaw (Vals AI)59.4%Not measuredOnly DeepSeek V4 Pro 0423 measured
CorpFin (Vals AI)61.4%Not measuredOnly DeepSeek V4 Pro 0423 measured
TaxEval (Vals AI)72.1%Not measuredOnly DeepSeek V4 Pro 0423 measured
MedCode (Vals AI)40.5%Not measuredOnly DeepSeek V4 Pro 0423 measured
SimpleQA Verified47.0%Not measuredOnly DeepSeek V4 Pro 0423 measured
MMLUNot measured75.7%Only Gemma 2 27B measured
LMArena Text: science14701228DeepSeek V4 Pro 0423
LMArena Text: medicine and healthcare14661198DeepSeek V4 Pro 0423
LMArena Text: legal and government14641241DeepSeek V4 Pro 0423
LMArena Text: business and finance14431208DeepSeek V4 Pro 0423
Neither model has a result on the other 5 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 Gemma 2 27B
BenchmarkDeepSeek V4 Pro 0423Gemma 2 27BLeader
Design Arena: websites1235Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: UI components1222Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: game dev1229Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: data viz1202Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: 3D1254Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: all code categories1240Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: SVG1140Not measuredOnly DeepSeek V4 Pro 0423 measured
Design Arena: ASCII art1148Not measuredOnly 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 Gemma 2 27B
BenchmarkDeepSeek V4 Pro 0423Gemma 2 27BLeader
Public Benefits Bench (Vals AI)62.9%Not measuredOnly DeepSeek V4 Pro 0423 measured
MedScribe (Vals AI)75.1%Not measuredOnly DeepSeek V4 Pro 0423 measured
DTBench90.7%48.0%DeepSeek V4 Pro 0423
LMCA41.2%7.1%DeepSeek V4 Pro 0423
Surface Evolver Bench40.0%Not measuredOnly DeepSeek V4 Pro 0423 measured
CL-bench Life13.5%Not measuredOnly DeepSeek V4 Pro 0423 measured
LMArena Text14511231DeepSeek V4 Pro 0423
LMArena Text: instruction following14451206DeepSeek V4 Pro 0423
LMArena Text: creative writing14461240DeepSeek V4 Pro 0423
LMArena Text: English14591242DeepSeek V4 Pro 0423
LMArena Text: non-English14391217DeepSeek V4 Pro 0423
LMArena Text: Chinese14811221DeepSeek V4 Pro 0423
LMArena Text: German14581209DeepSeek V4 Pro 0423
LMArena Text: Japanese14461175DeepSeek V4 Pro 0423
LMArena Text: Korean14091174DeepSeek V4 Pro 0423
LMArena Text: French14731248DeepSeek V4 Pro 0423
LMArena Text: Spanish14571228DeepSeek V4 Pro 0423
LMArena Text: Russian14531234DeepSeek V4 Pro 0423
LMArena Text: Polish14611277DeepSeek V4 Pro 0423
LMArena Text: multi-turn14681224DeepSeek V4 Pro 0423
LMArena Text: longer queries14581230DeepSeek V4 Pro 0423
LMArena Text: writing and language14471249DeepSeek V4 Pro 0423
LMArena Text: entertainment and media14311212DeepSeek V4 Pro 0423
Neither model has a result on the other 23 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 Gemma 2 27B
BenchmarkDeepSeek V4 Pro 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 Pro 0423$0.0026Fits in one request
Gemma 2 27B$0.0010Fits in one request

Gemma 2 27B costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Pro 0423$0.069Fits in one request
Gemma 2 27B$0.034Exceeds the context window

Gemma 2 27B costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Pro 0423$0.272Fits in one request
Gemma 2 27B$0.137Exceeds the context window

Gemma 2 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 Gemma 2 27B
SpecificationDeepSeek V4 Pro 0423Google Gemma 2 27B
MakerDeepSeekGoogle
WeightsOpen weightsOpen weights
ReleasedApril 24, 2026July 13, 2024
Context window1.05M8K
Input $/Mtok$1.225$0.65
Output $/Mtok$2.716$0.65
Coding index38.7Not benchmarked
Independent measurements behind it50
Cost per solved task$0.57Not computable
SourceOpenRouter listingOpenRouter listing

Questions

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

Only DeepSeek V4 Pro 0423 has a coding score, 38.7 on the Vibe Coding Index; Gemma 2 27B has none, so the two cannot be ranked on coding. Gemma 2 27B has the edge where output tokens cost 4.2x less.

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

DeepSeek V4 Pro 0423 costs $1.225 per million input tokens and $2.716 per million output tokens; Gemma 2 27B costs $0.65 and $0.65. Cost per solved task is known for DeepSeek V4 Pro 0423 only.

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

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

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

34 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Pro 0423's coding index rests on 5 independent benchmarks 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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