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DeepSeek V4 Flash 0423 vs GPT-5.1-Codex

DeepSeek V4 Flash 0423 leads GPT-5.1-Codex on the Artificial Analysis Intelligence Index, 24.4 to 23.7, and DeepSeek V4 Flash 0423 costs 35.7x less per output token ($0.28 vs $10.00 per million). Updated October 22, 2026; 9 benchmarks have a result for both.

Replace DeepSeek V4 Flash 0423 Replace GPT-5.1-Codex
DeepSeek V4 Flash 0423DeepSeek
Coding
No data
Overall
No data
Output $/M
$0.28
6
GPT-5.1-CodexOpenAI
Coding
15.3
Overall
No data
Output $/M
$10.00
3
vs boards won of 9 shared

DeepSeek V4 Flash 0423 or GPT-5.1-Codex: which to pick

Only GPT-5.1-Codex has a coding score, 15.3 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 35.7x cheaper ($0.28 vs $10.00 per million) and a larger context window (1.05M against 400K tokens).

Model ADeepSeek V4 Flash 0423DeepSeekNot benchmarked1 independent benchmarkNo cost per solved taskModel BGPT-5.1-CodexOpenAI15.3/1002 independent benchmarks$0.892 per solved task
Shared results
9
DeepSeek V4 Flash 0423 only
40
GPT-5.1-Codex only
17
Like-for-like categories
3 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, GPT-5.1-Codex 15.3 from 2 benchmarks.
Widest category
Coding, 29.7 points apart on average over 3 shared benchmarks.
Widest single gap
LMArena WebDev: HTML: DeepSeek V4 Flash 0423 1558 against GPT-5.1-Codex 1346, 35.3 points apart on a 0 to 100 scale.
Output price
DeepSeek V4 Flash 0423 $0.28 against GPT-5.1-Codex $10.00 per million output tokens, 35.7x apart.
Context window
DeepSeek V4 Flash 0423 takes 1.05M tokens, GPT-5.1-Codex 400K.

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 0423BarsGPT-5.1-CodexTies
Coding21
Intelligence10
Web design32
All shared boards63
CodingLike-for-likeSix of these boards feed the Vibe Coding Index21
Coding benchmarks for DeepSeek V4 Flash 0423 and GPT-5.1-Codex
BenchmarkDeepSeek V4 Flash 0423GPT-5.1-CodexLeader
SWE-bench VerifiedNot measured66.0%Only GPT-5.1-Codex measured
LMArena WebDev15821337DeepSeek V4 Flash 0423
AA Coding Index52.0Not measuredOnly DeepSeek V4 Flash 0423 measured
LiveCodeBench (Vals AI)Not measured85.5%Only GPT-5.1-Codex measured
Vibe Code Bench (Vals AI)Not measured13.1%Only GPT-5.1-Codex measured
LiveCodeBench (Artificial Analysis run)Not measured84.9%Only GPT-5.1-Codex measured
Terminal-Bench Hard (Artificial Analysis run)Not measured34.8%Only GPT-5.1-Codex measured
Terminal-BenchNot measured57.8%Only GPT-5.1-Codex measured
WeirdML45.6%Not measuredOnly DeepSeek V4 Flash 0423 measured
ALE-Bench678.21244.9GPT-5.1-Codex
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: HTML15581346DeepSeek V4 Flash 0423
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V4 Flash 0423 and GPT-5.1-Codex
BenchmarkDeepSeek V4 Flash 0423GPT-5.1-CodexLeader
AA Intelligence Index24.423.7DeepSeek V4 Flash 0423
Epoch Capabilities Index146.1Not measuredOnly DeepSeek V4 Flash 0423 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
Agentic work benchmarks for DeepSeek V4 Flash 0423 and GPT-5.1-Codex
BenchmarkDeepSeek V4 Flash 0423GPT-5.1-CodexLeader
AA Agentic Index26.3Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: agents building web appsNot measured1018Only GPT-5.1-Codex measured
Design Arena: agents building full-stack appsNot measured1086Only GPT-5.1-Codex measured
Design Arena: agents building mobile appsNot measured1191Only GPT-5.1-Codex measured
τ²-Bench TelecomNot measured83.0%Only GPT-5.1-Codex measured
Neither model has a result on the other 36 boards here
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Reasoning benchmarks for DeepSeek V4 Flash 0423 and GPT-5.1-Codex
BenchmarkDeepSeek V4 Flash 0423GPT-5.1-CodexLeader
GPQA Diamond (Artificial Analysis run)Not measured86.0%Only GPT-5.1-Codex measured
Humanity's Last Exam (Artificial Analysis run)Not measured25.7%Only GPT-5.1-Codex measured
SimpleBench46.3%Not measuredOnly DeepSeek V4 Flash 0423 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 15 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Math benchmarks for DeepSeek V4 Flash 0423 and GPT-5.1-Codex
BenchmarkDeepSeek V4 Flash 0423GPT-5.1-CodexLeader
AA Math IndexNot measured95.7Only GPT-5.1-Codex measured
AIME 2025 (Artificial Analysis run)Not measured95.7%Only GPT-5.1-Codex 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 index5 boards, none shared
Knowledge benchmarks for DeepSeek V4 Flash 0423 and GPT-5.1-Codex
BenchmarkDeepSeek V4 Flash 0423GPT-5.1-CodexLeader
MMLU Pro (Artificial Analysis run)Not measured86.0%Only GPT-5.1-Codex 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 13 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index32
Web design benchmarks for DeepSeek V4 Flash 0423 and GPT-5.1-Codex
BenchmarkDeepSeek V4 Flash 0423GPT-5.1-CodexLeader
Design Arena: websites12071161DeepSeek V4 Flash 0423
Design Arena: UI components11621234GPT-5.1-Codex
Design Arena: game dev11961137DeepSeek V4 Flash 0423
Design Arena: data viz11271213GPT-5.1-Codex
Design Arena: 3D1188Not measuredOnly DeepSeek V4 Flash 0423 measured
Design Arena: all code categories12051152DeepSeek V4 Flash 0423
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
GeneralDirectional onlyPublic boards, shown as published, kept out of the index21 boards, none shared
General benchmarks for DeepSeek V4 Flash 0423 and GPT-5.1-Codex
BenchmarkDeepSeek V4 Flash 0423GPT-5.1-CodexLeader
IFBenchNot measured70.0%Only GPT-5.1-Codex measured
Long context reasoning (AA-LCR)Not measured69.3%Only GPT-5.1-Codex measured
DTBench86.4%Not measuredOnly DeepSeek V4 Flash 0423 measured
LMCA35.9%Not measuredOnly DeepSeek V4 Flash 0423 measured
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 25 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 GPT-5.1-Codex
BenchmarkDeepSeek V4 Flash 0423GPT-5.1-CodexLeader
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
GPT-5.1-Codex$0.0063Fits 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
GPT-5.1-Codex$0.092Fits in one request

DeepSeek V4 Flash 0423 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V4 Flash 0423$0.031Fits in one request
GPT-5.1-Codex$0.350Fits 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 GPT-5.1-Codex
SpecificationDeepSeek V4 Flash 0423OpenAI GPT-5.1-Codex
MakerDeepSeekOpenAI
WeightsOpen weightsNo public weights listed
ReleasedApril 24, 2026November 13, 2025
Context window1.05M400K
Input $/Mtok$0.14$1.25
Output $/Mtok$0.28$10.00
Coding indexNot benchmarked15.3
Independent measurements behind it12
Cost per solved taskNot computable$0.892
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V4 Flash 0423 better than GPT-5.1-Codex for coding?

Only GPT-5.1-Codex has a coding score, 15.3 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 35.7x cheaper ($0.28 vs $10.00 per million) and a larger context window (1.05M against 400K tokens).

Which is cheaper, DeepSeek V4 Flash 0423 or GPT-5.1-Codex?

DeepSeek V4 Flash 0423 costs $0.14 per million input tokens and $0.28 per million output tokens; GPT-5.1-Codex costs $1.25 and $10.00. Cost per solved task is known for GPT-5.1-Codex only.

Which has the bigger context window, DeepSeek V4 Flash 0423 or GPT-5.1-Codex?

DeepSeek V4 Flash 0423, with 1.05M tokens against 400K.

How many benchmarks is the DeepSeek V4 Flash 0423 vs GPT-5.1-Codex comparison based on?

9 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Flash 0423's coding index rests on 1 independent benchmark and GPT-5.1-Codex's on 2. Where only one model has a result, the page says so instead of naming a winner.

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