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DeepSeek V3.2 Exp vs GPT-4o (2024-08-06)

DeepSeek V3.2 Exp leads GPT-4o (2024-08-06) on the Artificial Analysis Intelligence Index, 16.6 to 7.7, and DeepSeek V3.2 Exp costs 24.4x less per output token ($0.41 vs $10.00 per million). Updated October 22, 2026; 36 benchmarks have a result for both.

Replace DeepSeek V3.2 Exp Replace GPT-4o (2024-08-06)
DeepSeek V3.2 ExpDeepSeek
Coding
No data
Overall
No data
Output $/M
$0.41
36
GPT-4o (2024-08-06)OpenAI
Coding
No data
Overall
26.3
Output $/M
$10.00
0
vs boards won of 36 shared

DeepSeek V3.2 Exp or GPT-4o (2024-08-06): which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick DeepSeek V3.2 Exp for output tokens 24.4x cheaper ($0.41 vs $10.00 per million) and a larger context window (164K against 128K tokens).

Model ADeepSeek V3.2 ExpDeepSeekNot benchmarked1 independent benchmarkNo cost per solved taskModel BGPT-4o (2024-08-06)OpenAINot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
36
DeepSeek V3.2 Exp only
24
GPT-4o (2024-08-06) only
22
Like-for-like categories
7 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-4o (2024-08-06) not benchmarked.
Widest category
Coding, 34.7 points apart on average over 5 shared benchmarks.
Widest single gap
Aider polyglot: DeepSeek V3.2 Exp 74.2% against GPT-4o (2024-08-06) 23.1%, 51.1 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 Exp $0.41 against GPT-4o (2024-08-06) $10.00 per million output tokens, 24.4x apart.
Context window
DeepSeek V3.2 Exp takes 164K tokens, GPT-4o (2024-08-06) 128K.

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-4o (2024-08-06)Ties
Coding50
Intelligence10
Agentic work10
Reasoning50
Math20
Knowledge40
General180
All shared boards360
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
Coding benchmarks for DeepSeek V3.2 Exp and GPT-4o (2024-08-06)
BenchmarkDeepSeek V3.2 ExpGPT-4o (2024-08-06)Leader
Aider polyglot74.2%23.1%DeepSeek V3.2 Exp
SWE-bench LiteNot measured21.7%Only GPT-4o (2024-08-06) measured
LMArena WebDev1272Not measuredOnly DeepSeek V3.2 Exp measured
LiveCodeBench (Artificial Analysis run)78.9%31.7%DeepSeek V3.2 Exp
Terminal-Bench Hard (Artificial Analysis run)31.1%8.3%DeepSeek V3.2 Exp
WeirdML39.5%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: coding14381283DeepSeek V3.2 Exp
LMArena Text: software and IT14381278DeepSeek V3.2 Exp
LMArena WebDev: HTML1281Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 34 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V3.2 Exp and GPT-4o (2024-08-06)
BenchmarkDeepSeek V3.2 ExpGPT-4o (2024-08-06)Leader
AA Intelligence Index16.67.7DeepSeek V3.2 Exp
Epoch Capabilities Index144.9Not measuredOnly DeepSeek V3.2 Exp measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for DeepSeek V3.2 Exp and GPT-4o (2024-08-06)
BenchmarkDeepSeek V3.2 ExpGPT-4o (2024-08-06)Leader
τ²-Bench Telecom33.9%28.9%DeepSeek V3.2 Exp
TheAgentCompany42.9%Not measuredOnly DeepSeek V3.2 Exp measured
Berkeley Function Calling Leaderboard (BFCL)56.7%Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 39 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for DeepSeek V3.2 Exp and GPT-4o (2024-08-06)
BenchmarkDeepSeek V3.2 ExpGPT-4o (2024-08-06)Leader
MMMU Pro (Vals AI)Not measured64.0%Only GPT-4o (2024-08-06) measured
GPQA Diamond (Artificial Analysis run)79.7%52.1%DeepSeek V3.2 Exp
Humanity's Last Exam (Artificial Analysis run)14.9%2.3%DeepSeek V3.2 Exp
GPQA Diamond (Epoch AI run)Not measured49.2%Only GPT-4o (2024-08-06) measured
SimpleBenchNot measured17.8%Only GPT-4o (2024-08-06) measured
Chess puzzlesNot measured6.0%Only GPT-4o (2024-08-06) measured
LMArena Text: hard prompts14291264DeepSeek V3.2 Exp
LMArena Text: expert14221241DeepSeek V3.2 Exp
LMArena Text: hard prompts in English14441277DeepSeek V3.2 Exp
Kagi LLM Benchmark52.2%Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for DeepSeek V3.2 Exp and GPT-4o (2024-08-06)
BenchmarkDeepSeek V3.2 ExpGPT-4o (2024-08-06)Leader
AIMENot measured14.0%Only GPT-4o (2024-08-06) measured
MATH 500 (Vals AI)Not measured75.2%Only GPT-4o (2024-08-06) measured
MGSM (Vals AI)Not measured90.7%Only GPT-4o (2024-08-06) measured
AA Math Index87.7Not measuredOnly DeepSeek V3.2 Exp measured
AIME 2025 (Artificial Analysis run)87.7%Not measuredOnly DeepSeek V3.2 Exp measured
OTIS Mock AIMENot measured6.4%Only GPT-4o (2024-08-06) measured
FrontierMath, tiers 1 to 3Not measured0.4%Only GPT-4o (2024-08-06) measured
MATH level 5Not measured53.3%Only GPT-4o (2024-08-06) measured
LMArena Text: math14221285DeepSeek V3.2 Exp
LMArena Text: mathematical work14331283DeepSeek V3.2 Exp
MathArena14.4%Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for DeepSeek V3.2 Exp and GPT-4o (2024-08-06)
BenchmarkDeepSeek V3.2 ExpGPT-4o (2024-08-06)Leader
MMLU ProNot measured74.1%Only GPT-4o (2024-08-06) measured
LegalBench (Vals AI)Not measured80.1%Only GPT-4o (2024-08-06) measured
CorpFin (Vals AI)Not measured39.4%Only GPT-4o (2024-08-06) measured
TaxEval (Vals AI)Not measured71.1%Only GPT-4o (2024-08-06) measured
MortgageTax (Vals AI)Not measured61.0%Only GPT-4o (2024-08-06) measured
MedQA (Vals AI)Not measured88.2%Only GPT-4o (2024-08-06) measured
MMLU Pro (Artificial Analysis run)85.0%Not measuredOnly DeepSeek V3.2 Exp measured
SimpleQA VerifiedNot measured26.0%Only GPT-4o (2024-08-06) measured
MMLUNot measured84.3%Only GPT-4o (2024-08-06) measured
LMArena Text: science14431274DeepSeek V3.2 Exp
LMArena Text: medicine and healthcare14681252DeepSeek V3.2 Exp
LMArena Text: legal and government14191275DeepSeek V3.2 Exp
LMArena Text: business and finance14121232DeepSeek V3.2 Exp
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for DeepSeek V3.2 Exp and GPT-4o (2024-08-06)
BenchmarkDeepSeek V3.2 ExpGPT-4o (2024-08-06)Leader
Design Arena: websites1179Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: UI components1164Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: game dev1143Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: data viz1152Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: 3D1148Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: all code categories1170Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: SVG1031Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index180
General benchmarks for DeepSeek V3.2 Exp and GPT-4o (2024-08-06)
BenchmarkDeepSeek V3.2 ExpGPT-4o (2024-08-06)Leader
IFBench54.1%36.0%DeepSeek V3.2 Exp
Long context reasoning (AA-LCR)72.3%41.0%DeepSeek V3.2 Exp
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 Text14251283DeepSeek V3.2 Exp
LMArena Text: instruction following14011267DeepSeek V3.2 Exp
LMArena Text: creative writing14041274DeepSeek V3.2 Exp
LMArena VisionNot measured1065Only GPT-4o (2024-08-06) measured
LMArena Text: English14411297DeepSeek V3.2 Exp
LMArena Text: non-English14041262DeepSeek V3.2 Exp
LMArena Text: Chinese14611254DeepSeek V3.2 Exp
LMArena Text: German14401257DeepSeek V3.2 Exp
LMArena Text: Japanese13311234DeepSeek V3.2 Exp
LMArena Text: Korean13721218DeepSeek V3.2 Exp
LMArena Text: French14341263DeepSeek V3.2 Exp
LMArena Text: Spanish14411269DeepSeek V3.2 Exp
LMArena Text: Russian14121272DeepSeek V3.2 Exp
LMArena Text: Polish1435Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: multi-turn14221279DeepSeek V3.2 Exp
LMArena Text: longer queries14181283DeepSeek V3.2 Exp
LMArena Text: writing and language14071290DeepSeek V3.2 Exp
LMArena Text: entertainment and media14071267DeepSeek V3.2 Exp
LMArena Vision: EnglishNot measured1072Only GPT-4o (2024-08-06) measured
LMArena Vision: ChineseNot measured1009Only GPT-4o (2024-08-06) measured
Neither model has a result on the other 21 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-4o (2024-08-06)
BenchmarkDeepSeek V3.2 ExpGPT-4o (2024-08-06)Leader
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-4o (2024-08-06)$0.0075Fits 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-4o (2024-08-06)$0.155Fits 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-4o (2024-08-06)$0.600Exceeds the context window

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-4o (2024-08-06)
SpecificationDeepSeek V3.2 ExpOpenAI GPT-4o (2024-08-06)
MakerDeepSeekOpenAI
WeightsOpen weightsNo public weights listed
ReleasedSeptember 29, 2025August 6, 2024
Context window164K128K
Input $/Mtok$0.27$2.50
Output $/Mtok$0.41$10.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it10
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 Exp better than GPT-4o (2024-08-06) for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for output tokens 24.4x cheaper ($0.41 vs $10.00 per million) and a larger context window (164K against 128K tokens).

Which is cheaper, DeepSeek V3.2 Exp or GPT-4o (2024-08-06)?

DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; GPT-4o (2024-08-06) costs $2.50 and $10.00. Cost per solved task is known for neither of them.

Which has the bigger context window, DeepSeek V3.2 Exp or GPT-4o (2024-08-06)?

DeepSeek V3.2 Exp, with 164K tokens against 128K.

How many benchmarks is the DeepSeek V3.2 Exp vs GPT-4o (2024-08-06) comparison based on?

36 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-4o (2024-08-06)'s on 0. Where only one model has a result, the page says so instead of naming a winner.

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