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DeepSeek V4 Flash 0423 vs Gemma 4 31B

Updated October 21, 2026. 36 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 4 31B
DeepSeek V4 Flash 0423DeepSeek
Coding
54.6
Overall
24.4
Output $/M
$0.182
11
Gemma 4 31BGoogle
Coding
38.3
Overall
14.7
Output $/M
$0.40
25
vs boards won of 36 shared

Decision reading

Pick DeepSeek V4 Flash 0423 for coding: it scores 54.6 on the Vibe Coding Index against 38.3 for Gemma 4 31B, though on 1 benchmark against 2.

Pick Gemma 4 31B when what matters is WeirdML or ALE-Bench, where it leads.

Model ADeepSeek V4 Flash 0423DeepSeek54.6/1001 independent benchmarkNo cost per solved taskModel BGemma 4 31BGoogle38.3/1002 independent benchmarksNo cost per solved task
Shared results
36
DeepSeek V4 Flash 0423 only
12
Gemma 4 31B only
32
Like-for-like categories
7 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 54.6 from 1 benchmark, Gemma 4 31B 38.3 from 2 benchmarks.
Widest category
Agentic work, 22.1 points apart on average over 1 shared benchmark.
Widest single gap
LMArena WebDev: React: DeepSeek V4 Flash 0423 1585 against Gemma 4 31B 1359, 37.6 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Flash 0423 $0.182 against Gemma 4 31B $0.40 per million output tokens, 2.2x apart.
Context window
DeepSeek V4 Flash 0423 takes 1.05M tokens, Gemma 4 31B 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 0423BarsGemma 4 31BTies
Coding44
Intelligence20
Agentic work10
Reasoning03
Math02
Knowledge13
General313
All shared boards1125
CodingLike-for-likeFour of these boards feed the Vibe Coding Index44
Coding benchmarks for DeepSeek V4 Flash 0423 and Gemma 4 31B
BenchmarkDeepSeek V4 Flash 0423Gemma 4 31BLeader
LiveBench codingNot measured49.0%Only Gemma 4 31B measured
LMArena WebDev15821367DeepSeek V4 Flash 0423
AA Coding Index52.043.4DeepSeek V4 Flash 0423
SciCodeNot measured45.5%Only Gemma 4 31B measured
Terminal-Bench Hard (Artificial Analysis run)Not measured36.4%Only Gemma 4 31B measured
WeirdML45.6%52.3%Gemma 4 31B
ALE-Bench678.2925.5Gemma 4 31B
LMArena Text: coding14571459Gemma 4 31B
LMArena Text: software and IT14531463Gemma 4 31B
LMArena WebDev: React15851359DeepSeek V4 Flash 0423
LMArena WebDev: HTML15581380DeepSeek V4 Flash 0423
Neither model has a result on the other 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for DeepSeek V4 Flash 0423 and Gemma 4 31B
BenchmarkDeepSeek V4 Flash 0423Gemma 4 31BLeader
AA Intelligence Index24.414.7DeepSeek V4 Flash 0423
Epoch Capabilities Index146.1143.0DeepSeek V4 Flash 0423
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for DeepSeek V4 Flash 0423 and Gemma 4 31B
BenchmarkDeepSeek V4 Flash 0423Gemma 4 31BLeader
AA Agentic Index26.34.2DeepSeek V4 Flash 0423
LiveBench researchNot measured44.4%Only Gemma 4 31B measured
LiveBench browserNot measured4.0%Only Gemma 4 31B measured
LiveBench long agenticNot measured13.3%Only Gemma 4 31B measured
τ²-Bench TelecomNot measured65.5%Only Gemma 4 31B measured
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
Reasoning benchmarks for DeepSeek V4 Flash 0423 and Gemma 4 31B
BenchmarkDeepSeek V4 Flash 0423Gemma 4 31BLeader
LiveBench reasoningNot measured68.3%Only Gemma 4 31B measured
GPQA Diamond (Artificial Analysis run)Not measured85.7%Only Gemma 4 31B measured
Humanity's Last Exam (Artificial Analysis run)Not measured23.6%Only Gemma 4 31B measured
GPQA Diamond (Epoch AI run)Not measured75.8%Only Gemma 4 31B measured
SimpleBench46.3%Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: hard prompts14441448Gemma 4 31B
LMArena Text: expert14411464Gemma 4 31B
LMArena Text: hard prompts in English14491456Gemma 4 31B
LMArena Vision: diagramsNot measured1283Only Gemma 4 31B measured
LMArena Vision: homeworkNot measured1310Only Gemma 4 31B measured
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for DeepSeek V4 Flash 0423 and Gemma 4 31B
BenchmarkDeepSeek V4 Flash 0423Gemma 4 31BLeader
OTIS Mock AIMENot measured73.3%Only Gemma 4 31B measured
LMArena Text: math14261463Gemma 4 31B
LMArena Text: mathematical work14321465Gemma 4 31B
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 Gemma 4 31B
BenchmarkDeepSeek V4 Flash 0423Gemma 4 31BLeader
CaseLaw (Vals AI)Not measured52.6%Only Gemma 4 31B measured
MortgageTax (Vals AI)Not measured61.4%Only Gemma 4 31B measured
SimpleQA VerifiedNot measured10.4%Only Gemma 4 31B measured
LMArena Text: science14461457Gemma 4 31B
LMArena Text: medicine and healthcare14481451Gemma 4 31B
LMArena Text: legal and government14391438DeepSeek V4 Flash 0423
LMArena Text: business and finance14251430Gemma 4 31B
Neither model has a result on the other 10 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 Gemma 4 31B
BenchmarkDeepSeek V4 Flash 0423Gemma 4 31BLeader
Design Arena: websites1208Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: UI components1164Not 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
LiveBench designNot measured58.4%Only Gemma 4 31B measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index313
General benchmarks for DeepSeek V4 Flash 0423 and Gemma 4 31B
BenchmarkDeepSeek V4 Flash 0423Gemma 4 31BLeader
LiveBench data analysisNot measured53.3%Only Gemma 4 31B measured
SAGE (Vals AI)Not measured55.0%Only Gemma 4 31B measured
IFBenchNot measured75.6%Only Gemma 4 31B measured
Long context reasoning (AA-LCR)Not measured69.7%Only Gemma 4 31B measured
DTBench86.4%82.7%DeepSeek V4 Flash 0423
LMCA35.9%39.3%Gemma 4 31B
Surface Evolver BenchNot measured30.6%Only Gemma 4 31B measured
LMArena Text14321443Gemma 4 31B
LMArena Text: instruction following14211433Gemma 4 31B
LMArena Text: creative writing14031417Gemma 4 31B
LMArena VisionNot measured1276Only Gemma 4 31B measured
LMArena Text: English14411451Gemma 4 31B
LMArena Text: non-English14191430Gemma 4 31B
LMArena Text: Chinese14631475Gemma 4 31B
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: French14391436DeepSeek V4 Flash 0423
LMArena Text: Spanish14361442Gemma 4 31B
LMArena Text: Russian14271460Gemma 4 31B
LMArena Text: Polish14361433DeepSeek V4 Flash 0423
LMArena Text: multi-turn14491452Gemma 4 31B
LMArena Text: longer queries14341446Gemma 4 31B
LMArena Text: writing and language14101432Gemma 4 31B
LMArena Text: entertainment and media14001405Gemma 4 31B
LMArena Vision: EnglishNot measured1271Only Gemma 4 31B measured
LMArena Vision: ChineseNot measured1326Only Gemma 4 31B measured
LMArena Vision: reading textNot measured1282Only Gemma 4 31B measured
LMArena Vision: humorNot measured1280Only Gemma 4 31B measured
LMArena Vision: captioningNot measured1218Only Gemma 4 31B measured
LMArena Vision: recognizing thingsNot measured1226Only Gemma 4 31B measured
LMArena Vision: creative writingNot measured1285Only Gemma 4 31B measured
LMArena DocumentNot measured1425Only Gemma 4 31B measured
Neither model has a result on the other 13 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 4 31B
BenchmarkDeepSeek V4 Flash 0423Gemma 4 31BLeader
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$0.0002Fits in one request
Gemma 4 31B$0.0004Fits in one request

DeepSeek V4 Flash 0423 costs less

Repository review

50K input + 3K output tokens

DeepSeek V4 Flash 0423$0.0051Fits in one request
Gemma 4 31B$0.011Fits in one request

DeepSeek V4 Flash 0423 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Flash 0423$0.020Fits in one request
Gemma 4 31B$0.044Fits 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 Gemma 4 31B
SpecificationDeepSeek V4 Flash 0423Google Gemma 4 31B
MakerDeepSeekGoogle
WeightsOpen weightsOpen weights
ReleasedApril 24, 2026April 2, 2026
Context window1.05M262K
Input $/Mtok$0.091$0.20
Output $/Mtok$0.182$0.40
Coding index54.638.3
Independent measurements behind it12
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4 Flash 0423 better than Gemma 4 31B for coding?

Pick DeepSeek V4 Flash 0423 for coding: it scores 54.6 on the Vibe Coding Index against 38.3 for Gemma 4 31B, though on 1 benchmark against 2. Pick Gemma 4 31B when what matters is WeirdML or ALE-Bench, where it leads.

Which is cheaper, DeepSeek V4 Flash 0423 or Gemma 4 31B?

DeepSeek V4 Flash 0423 costs $0.091 per million input tokens and $0.182 per million output tokens; Gemma 4 31B costs $0.20 and $0.40. Cost per solved task is known for neither of them.

Which has the bigger context window, DeepSeek V4 Flash 0423 or Gemma 4 31B?

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

How many benchmarks is the DeepSeek V4 Flash 0423 vs Gemma 4 31B comparison based on?

36 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 4 31B's on 2. Where only one model has a result, the page says so instead of naming a winner.

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