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DeepSeek V3.2 Exp vs GPT-5.3-Codex

GPT-5.3-Codex leads DeepSeek V3.2 Exp on the Artificial Analysis Intelligence Index, 32.5 to 16.6, and DeepSeek V3.2 Exp costs 34.1x less per output token ($0.41 vs $14.00 per million). Updated October 22, 2026; 18 benchmarks have a result for both.

Replace DeepSeek V3.2 Exp Replace GPT-5.3-Codex
DeepSeek V3.2 ExpDeepSeek
Coding
No data
Overall
No data
Output $/M
$0.41
4
GPT-5.3-CodexOpenAI
Coding
60.0
Overall
No data
Output $/M
$14.00
14
vs boards won of 18 shared

DeepSeek V3.2 Exp or GPT-5.3-Codex: which to pick

Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; DeepSeek V3.2 Exp has none, so the two cannot be ranked on coding.

Pick DeepSeek V3.2 Exp for output tokens 34.1x cheaper ($0.41 vs $14.00 per million) and work measured by Design Arena: websites or Design Arena: UI components, where it leads.

Model ADeepSeek V3.2 ExpDeepSeekNot benchmarked1 independent benchmarkNo cost per solved taskModel BGPT-5.3-CodexOpenAI60.0/1003 independent benchmarks$0.59 per solved task
Shared results
18
DeepSeek V3.2 Exp only
41
GPT-5.3-Codex only
16
Like-for-like categories
6 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 V3.2 Exp not benchmarked, GPT-5.3-Codex 60.0 from 3 benchmarks.
Widest category
Agentic work, 52.0 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: DeepSeek V3.2 Exp 33.9% against GPT-5.3-Codex 86.0%, 52.0 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 Exp $0.41 against GPT-5.3-Codex $14.00 per million output tokens, 34.1x apart.
Context window
DeepSeek V3.2 Exp takes 164K tokens, GPT-5.3-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 V3.2 ExpBarsGPT-5.3-CodexTies
Coding04
Intelligence02
Agentic work01
Reasoning02
Web design43
General02
All shared boards414
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for DeepSeek V3.2 Exp and GPT-5.3-Codex
BenchmarkDeepSeek V3.2 ExpGPT-5.3-CodexLeader
Aider polyglot74.2%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena WebDev12721407GPT-5.3-Codex
LiveCodeBench (Vals AI)Not measured87.3%Only GPT-5.3-Codex measured
SWE-bench Verified (Vals AI)Not measured78.0%Only GPT-5.3-Codex measured
Vibe Code Bench (Vals AI)Not measured61.8%Only GPT-5.3-Codex measured
IOI (Vals AI)Not measured53.8%Only GPT-5.3-Codex measured
LiveCodeBench (Artificial Analysis run)78.9%Not measuredOnly DeepSeek V3.2 Exp measured
Terminal-Bench Hard (Artificial Analysis run)31.1%53.0%GPT-5.3-Codex
Terminal-BenchNot measured78.4%Only GPT-5.3-Codex measured
WeirdML39.5%79.3%GPT-5.3-Codex
ALE-BenchNot measured1655.2Only GPT-5.3-Codex measured
LMArena Text: coding1438Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: software and IT1438Not measuredOnly DeepSeek V3.2 Exp measured
LMArena WebDev: ReactNot measured1395Only GPT-5.3-Codex measured
LMArena WebDev: HTML12811422GPT-5.3-Codex
LMArena WebDev: from an imageNot measured1443Only GPT-5.3-Codex measured
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for DeepSeek V3.2 Exp and GPT-5.3-Codex
BenchmarkDeepSeek V3.2 ExpGPT-5.3-CodexLeader
AA Intelligence Index16.632.5GPT-5.3-Codex
Epoch Capabilities Index144.9157.1GPT-5.3-Codex
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for DeepSeek V3.2 Exp and GPT-5.3-Codex
BenchmarkDeepSeek V3.2 ExpGPT-5.3-CodexLeader
Design Arena: agents building web appsNot measured1032Only GPT-5.3-Codex measured
Design Arena: agents building full-stack appsNot measured983Only GPT-5.3-Codex measured
Design Arena: agents building mobile appsNot measured1053Only GPT-5.3-Codex measured
Design Arena: agents building Android appsNot measured1084Only GPT-5.3-Codex measured
Design Arena: agents building Godot gamesNot measured1124Only GPT-5.3-Codex measured
τ²-Bench Telecom33.9%86.0%GPT-5.3-Codex
METR time horizonsNot measured74.5%Only GPT-5.3-Codex measured
TheAgentCompany42.9%Not measuredOnly DeepSeek V3.2 Exp measured
Vending-Bench 2Not measured$5,940Only GPT-5.3-Codex measured
Berkeley Function Calling Leaderboard (BFCL)56.7%Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 31 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
Reasoning benchmarks for DeepSeek V3.2 Exp and GPT-5.3-Codex
BenchmarkDeepSeek V3.2 ExpGPT-5.3-CodexLeader
GPQA Diamond (Artificial Analysis run)79.7%91.5%GPT-5.3-Codex
Humanity's Last Exam (Artificial Analysis run)14.9%42.5%GPT-5.3-Codex
LMArena Text: hard prompts1429Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: expert1422Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: hard prompts in English1444Not measuredOnly DeepSeek V3.2 Exp measured
Kagi LLM Benchmark52.2%Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 16 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Math benchmarks for DeepSeek V3.2 Exp and GPT-5.3-Codex
BenchmarkDeepSeek V3.2 ExpGPT-5.3-CodexLeader
AA Math Index87.7Not measuredOnly DeepSeek V3.2 Exp measured
AIME 2025 (Artificial Analysis run)87.7%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: math1422Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: mathematical work1433Not measuredOnly DeepSeek V3.2 Exp 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 V3.2 Exp and GPT-5.3-Codex
BenchmarkDeepSeek V3.2 ExpGPT-5.3-CodexLeader
MMLU Pro (Artificial Analysis run)85.0%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: science1443Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: medicine and healthcare1468Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: legal and government1419Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: business and finance1412Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 13 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index43
Web design benchmarks for DeepSeek V3.2 Exp and GPT-5.3-Codex
BenchmarkDeepSeek V3.2 ExpGPT-5.3-CodexLeader
Design Arena: websites11781162DeepSeek V3.2 Exp
Design Arena: UI components11641134DeepSeek V3.2 Exp
Design Arena: game dev11441161GPT-5.3-Codex
Design Arena: data viz11521164GPT-5.3-Codex
Design Arena: 3D11481007DeepSeek V3.2 Exp
Design Arena: all code categories11701144DeepSeek V3.2 Exp
Design Arena: SVG10311129GPT-5.3-Codex
Design Arena: ASCII artNot measured1152Only GPT-5.3-Codex measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
General benchmarks for DeepSeek V3.2 Exp and GPT-5.3-Codex
BenchmarkDeepSeek V3.2 ExpGPT-5.3-CodexLeader
IFBench54.1%75.4%GPT-5.3-Codex
Long context reasoning (AA-LCR)72.3%83.3%GPT-5.3-Codex
DTBench87.7%Not measuredOnly DeepSeek V3.2 Exp measured
LMCA29.1%Not measuredOnly DeepSeek V3.2 Exp measured
Fiction.LiveBench83.3%Not measuredOnly DeepSeek V3.2 Exp measured
CL-bench13.2%Not measuredOnly DeepSeek V3.2 Exp measured
CL-bench Life9.5%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text1425Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: instruction following1401Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: creative writing1404Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: English1441Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: non-English1404Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: Chinese1461Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: German1440Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: Japanese1331Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: Korean1372Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: French1434Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: Spanish1441Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: Russian1412Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: Polish1435Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: multi-turn1422Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: longer queries1418Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: writing and language1407Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: entertainment and media1407Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 22 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V3.2 Exp and GPT-5.3-Codex
BenchmarkDeepSeek V3.2 ExpGPT-5.3-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 V3.2 Exp$0.0005Fits in one request
GPT-5.3-Codex$0.0088Fits in one request

DeepSeek V3.2 Exp costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2 Exp$0.015Fits in one request
GPT-5.3-Codex$0.130Fits in one request

DeepSeek V3.2 Exp costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2 Exp$0.058Exceeds the context window
GPT-5.3-Codex$0.490Fits in one request

DeepSeek V3.2 Exp 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 V3.2 Exp and GPT-5.3-Codex
SpecificationDeepSeek V3.2 ExpOpenAI GPT-5.3-Codex
MakerDeepSeekOpenAI
WeightsOpen weightsNo public weights listed
ReleasedSeptember 29, 2025February 24, 2026
Context window164K400K
Input $/Mtok$0.27$1.75
Output $/Mtok$0.41$14.00
Coding indexNot benchmarked60.0
Independent measurements behind it13
Cost per solved taskNot computable$0.59
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 Exp better than GPT-5.3-Codex for coding?

Only GPT-5.3-Codex has a coding score, 60.0 on the Vibe Coding Index; DeepSeek V3.2 Exp has none, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for output tokens 34.1x cheaper ($0.41 vs $14.00 per million) and work measured by Design Arena: websites or Design Arena: UI components, where it leads.

Which is cheaper, DeepSeek V3.2 Exp or GPT-5.3-Codex?

DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; GPT-5.3-Codex costs $1.75 and $14.00. Cost per solved task is known for GPT-5.3-Codex only.

Which has the bigger context window, DeepSeek V3.2 Exp or GPT-5.3-Codex?

GPT-5.3-Codex, with 400K tokens against 164K.

How many benchmarks is the DeepSeek V3.2 Exp vs GPT-5.3-Codex comparison based on?

18 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark and GPT-5.3-Codex's on 3. Where only one model has a result, the page says so instead of naming a winner.

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