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DeepSeek V4 Flash 0423 vs Gemini 2.5 Flash

Updated October 21, 2026. 40 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 Gemini 2.5 Flash
DeepSeek V4 Flash 0423DeepSeek
Coding
54.6
Overall
24.4
Output $/M
$0.182
38
Gemini 2.5 FlashGoogle
Coding
28.7
Overall
15.5
Output $/M
$2.50
2
vs boards won of 40 shared

Decision reading

Pick DeepSeek V4 Flash 0423 for coding: it scores 54.6 on the Vibe Coding Index against 28.7 for Gemini 2.5 Flash.

Pick Gemini 2.5 Flash when what matters is LMArena Text: Japanese or LMArena Text: Korean, where it leads.

Model ADeepSeek V4 Flash 0423DeepSeek54.6/1001 independent benchmarkNo cost per solved taskModel BGemini 2.5 FlashGoogle28.7/1001 independent benchmark$0.466 per solved task
Shared results
40
DeepSeek V4 Flash 0423 only
8
Gemini 2.5 Flash only
29
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, Gemini 2.5 Flash 28.7 from 1 benchmark.
Widest category
Web design, 17.9 points apart on average over 7 shared benchmarks.
Widest single gap
Design Arena: game dev: DeepSeek V4 Flash 0423 1197 against Gemini 2.5 Flash 1005, 24.0 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Flash 0423 $0.182 against Gemini 2.5 Flash $2.50 per million output tokens, 13.7x apart.
Context window
Both take 1.05M tokens.

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 0423BarsGemini 2.5 FlashTies
Coding20
Intelligence20
Reasoning40
Math20
Knowledge40
Web design70
General172
All shared boards382
CodingLike-for-likeFour of these boards feed the Vibe Coding Index20
Coding benchmarks for DeepSeek V4 Flash 0423 and Gemini 2.5 Flash
BenchmarkDeepSeek V4 Flash 0423Gemini 2.5 FlashLeader
SWE-bench VerifiedNot measured28.7%Only Gemini 2.5 Flash measured
LMArena WebDev1582Not measuredOnly DeepSeek V4 Flash 0423 measured
AA Coding Index52.0Not measuredOnly DeepSeek V4 Flash 0423 measured
LiveCodeBench (Artificial Analysis run)Not measured69.5%Only Gemini 2.5 Flash measured
Terminal-Bench Hard (Artificial Analysis run)Not measured13.6%Only Gemini 2.5 Flash measured
Terminal-BenchNot measured17.1%Only Gemini 2.5 Flash measured
WeirdML45.6%Not measuredOnly DeepSeek V4 Flash 0423 measured
ALE-Bench678.2Not measuredOnly DeepSeek V4 Flash 0423 measured
LMArena Text: coding14571423DeepSeek V4 Flash 0423
LMArena Text: software and IT14531425DeepSeek 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 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for DeepSeek V4 Flash 0423 and Gemini 2.5 Flash
BenchmarkDeepSeek V4 Flash 0423Gemini 2.5 FlashLeader
AA Intelligence Index24.415.5DeepSeek V4 Flash 0423
Epoch Capabilities Index146.1142.9DeepSeek V4 Flash 0423
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Agentic work benchmarks for DeepSeek V4 Flash 0423 and Gemini 2.5 Flash
BenchmarkDeepSeek V4 Flash 0423Gemini 2.5 FlashLeader
AA Agentic Index26.3Not measuredOnly DeepSeek V4 Flash 0423 measured
τ²-Bench TelecomNot measured31.6%Only Gemini 2.5 Flash measured
BALROGNot measured33.5%Only Gemini 2.5 Flash measured
Vending-Bench 2Not measured$549Only Gemini 2.5 Flash measured
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
Reasoning benchmarks for DeepSeek V4 Flash 0423 and Gemini 2.5 Flash
BenchmarkDeepSeek V4 Flash 0423Gemini 2.5 FlashLeader
GPQA Diamond (Artificial Analysis run)Not measured79.0%Only Gemini 2.5 Flash measured
Humanity's Last Exam (Artificial Analysis run)Not measured12.1%Only Gemini 2.5 Flash measured
ARC-AGINot measured33.3%Only Gemini 2.5 Flash measured
ARC-AGI-2Not measured2.5%Only Gemini 2.5 Flash measured
SimpleBench46.3%41.2%DeepSeek V4 Flash 0423
LMArena Text: hard prompts14441422DeepSeek V4 Flash 0423
LMArena Text: expert14411426DeepSeek V4 Flash 0423
LMArena Text: hard prompts in English14491420DeepSeek V4 Flash 0423
LMArena Vision: diagramsNot measured1245Only Gemini 2.5 Flash measured
LMArena Vision: homeworkNot measured1244Only Gemini 2.5 Flash measured
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for DeepSeek V4 Flash 0423 and Gemini 2.5 Flash
BenchmarkDeepSeek V4 Flash 0423Gemini 2.5 FlashLeader
AA Math IndexNot measured73.3Only Gemini 2.5 Flash measured
AIME 2025 (Artificial Analysis run)Not measured73.3%Only Gemini 2.5 Flash measured
LMArena Text: math14261411DeepSeek V4 Flash 0423
LMArena Text: mathematical work14321422DeepSeek 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 Gemini 2.5 Flash
BenchmarkDeepSeek V4 Flash 0423Gemini 2.5 FlashLeader
MedCode (Vals AI)Not measured40.4%Only Gemini 2.5 Flash measured
MMLU Pro (Artificial Analysis run)Not measured83.2%Only Gemini 2.5 Flash measured
LMArena Text: science14461433DeepSeek V4 Flash 0423
LMArena Text: medicine and healthcare14481428DeepSeek V4 Flash 0423
LMArena Text: legal and government14391430DeepSeek V4 Flash 0423
LMArena Text: business and finance14251404DeepSeek V4 Flash 0423
Neither model has a result on the other 11 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index70
Web design benchmarks for DeepSeek V4 Flash 0423 and Gemini 2.5 Flash
BenchmarkDeepSeek V4 Flash 0423Gemini 2.5 FlashLeader
Design Arena: websites12081074DeepSeek V4 Flash 0423
Design Arena: UI components11641005DeepSeek V4 Flash 0423
Design Arena: game dev11971005DeepSeek V4 Flash 0423
Design Arena: data viz11271060DeepSeek V4 Flash 0423
Design Arena: 3D11891021DeepSeek V4 Flash 0423
Design Arena: all code categories12051059DeepSeek V4 Flash 0423
Design Arena: SVG11551018DeepSeek V4 Flash 0423
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 index172
General benchmarks for DeepSeek V4 Flash 0423 and Gemini 2.5 Flash
BenchmarkDeepSeek V4 Flash 0423Gemini 2.5 FlashLeader
SAGE (Vals AI)Not measured44.8%Only Gemini 2.5 Flash measured
MedScribe (Vals AI)Not measured83.0%Only Gemini 2.5 Flash measured
IFBenchNot measured50.3%Only Gemini 2.5 Flash measured
Long context reasoning (AA-LCR)Not measured65.3%Only Gemini 2.5 Flash measured
DTBench86.4%76.5%DeepSeek V4 Flash 0423
LMCA35.9%27.5%DeepSeek V4 Flash 0423
LMArena Text14321417DeepSeek V4 Flash 0423
LMArena Text: instruction following14211405DeepSeek V4 Flash 0423
LMArena Text: creative writing14031400DeepSeek V4 Flash 0423
LMArena VisionNot measured1235Only Gemini 2.5 Flash measured
LMArena Text: English14411418DeepSeek V4 Flash 0423
LMArena Text: non-English14191409DeepSeek V4 Flash 0423
LMArena Text: Chinese14631449DeepSeek V4 Flash 0423
LMArena Text: German14191417DeepSeek V4 Flash 0423
LMArena Text: Japanese13891396Gemini 2.5 Flash
LMArena Text: Korean13831385Gemini 2.5 Flash
LMArena Text: French14391432DeepSeek V4 Flash 0423
LMArena Text: Spanish14361422DeepSeek V4 Flash 0423
LMArena Text: Russian14271415DeepSeek V4 Flash 0423
LMArena Text: Polish14361413DeepSeek V4 Flash 0423
LMArena Text: multi-turn14491408DeepSeek V4 Flash 0423
LMArena Text: longer queries14341419DeepSeek V4 Flash 0423
LMArena Text: writing and language14101405DeepSeek V4 Flash 0423
LMArena Text: entertainment and media14001393DeepSeek V4 Flash 0423
LMArena Vision: EnglishNot measured1239Only Gemini 2.5 Flash measured
LMArena Vision: ChineseNot measured1284Only Gemini 2.5 Flash measured
LMArena Vision: reading textNot measured1237Only Gemini 2.5 Flash measured
LMArena Vision: humorNot measured1225Only Gemini 2.5 Flash measured
LMArena Vision: captioningNot measured1277Only Gemini 2.5 Flash measured
LMArena Vision: recognizing thingsNot measured1252Only Gemini 2.5 Flash measured
LMArena Vision: creative writingNot measured1241Only Gemini 2.5 Flash measured
Neither model has a result on the other 15 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 Gemini 2.5 Flash
BenchmarkDeepSeek V4 Flash 0423Gemini 2.5 FlashLeader
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
Gemini 2.5 Flash$0.0015Fits 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
Gemini 2.5 Flash$0.022Fits 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
Gemini 2.5 Flash$0.085Fits 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 Gemini 2.5 Flash
SpecificationDeepSeek V4 Flash 0423Google Gemini 2.5 Flash
MakerDeepSeekGoogle
WeightsOpen weightsNo public weights listed
ReleasedApril 24, 2026June 17, 2025
Context window1.05M1.05M
Input $/Mtok$0.091$0.30
Output $/Mtok$0.182$2.50
Coding index54.628.7
Independent measurements behind it11
Cost per solved taskNot computable$0.466
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4 Flash 0423 better than Gemini 2.5 Flash for coding?

Pick DeepSeek V4 Flash 0423 for coding: it scores 54.6 on the Vibe Coding Index against 28.7 for Gemini 2.5 Flash. Pick Gemini 2.5 Flash when what matters is LMArena Text: Japanese or LMArena Text: Korean, where it leads.

Which is cheaper, DeepSeek V4 Flash 0423 or Gemini 2.5 Flash?

DeepSeek V4 Flash 0423 costs $0.091 per million input tokens and $0.182 per million output tokens; Gemini 2.5 Flash costs $0.30 and $2.50. Cost per solved task is known for Gemini 2.5 Flash only.

Which has the bigger context window, DeepSeek V4 Flash 0423 or Gemini 2.5 Flash?

Neither: both take 1.05M tokens.

How many benchmarks is the DeepSeek V4 Flash 0423 vs Gemini 2.5 Flash comparison based on?

40 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 Gemini 2.5 Flash's on 1. Where only one model has a result, the page says so instead of naming a winner.

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