LLMBenchmark.ai

Enter opens the highlighted result. Esc closes.

DeepSeek V3.2 Exp vs GPT-5.4

Updated October 21, 2026. 50 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace DeepSeek V3.2 Exp Replace GPT-5.4
DeepSeek V3.2 ExpDeepSeek
Coding
55.0
Overall
16.6
Output $/M
$0.41
2
GPT-5.4OpenAI
Coding
37.7
Overall
39.0
Output $/M
$15.00
48
vs boards won of 50 shared

Decision reading

Pick DeepSeek V3.2 Exp for coding: it scores 55.0 on the Vibe Coding Index against 37.7 for GPT-5.4.

Pick GPT-5.4 when the context window is larger, 1.05M against 164K tokens, or when what matters is LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model ADeepSeek V3.2 ExpDeepSeek55.0/1002 independent benchmarksNo cost per solved taskModel BGPT-5.4OpenAI37.7/1001 independent benchmark$1.02 per solved task
Shared results
50
DeepSeek V3.2 Exp only
7
GPT-5.4 only
59
Like-for-like categories
8 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 V3.2 Exp 55.0 from 2 benchmarks, GPT-5.4 37.7 from 1 benchmark.
Widest category
Agentic work, 53.2 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: DeepSeek V3.2 Exp 33.9% against GPT-5.4 87.1%, 53.2 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 Exp $0.41 against GPT-5.4 $15.00 per million output tokens, 36.6x apart.
Context window
DeepSeek V3.2 Exp takes 164K tokens, GPT-5.4 1.05M.

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-5.4Ties
Coding06
Intelligence02
Agentic work01
Reasoning05
Math02
Knowledge13
Web design16
General023
All shared boards248
CodingLike-for-likeFour of these boards feed the Vibe Coding Index06
Coding benchmarks for DeepSeek V3.2 Exp and GPT-5.4
BenchmarkDeepSeek V3.2 ExpGPT-5.4Leader
Aider polyglot74.2%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena WebDev12721464GPT-5.4
AA Coding IndexNot measured71.1Only GPT-5.4 measured
LiveCodeBench (Vals AI)Not measured84.1%Only GPT-5.4 measured
SWE-bench Verified (Vals AI)Not measured78.2%Only GPT-5.4 measured
Vibe Code Bench (Vals AI)Not measured67.4%Only GPT-5.4 measured
Code migration (Vals AI)Not measured35.0%Only GPT-5.4 measured
ProgramBench (Vals AI)Not measured0.5%Only GPT-5.4 measured
SkillsBench (Vals AI)Not measured51.7%Only GPT-5.4 measured
LiveCodeBench (Artificial Analysis run)78.9%Not measuredOnly DeepSeek V3.2 Exp measured
Terminal-Bench Hard (Artificial Analysis run)31.1%57.6%GPT-5.4
Terminal-BenchNot measured81.8%Only GPT-5.4 measured
GSO-BenchNot measured31.4%Only GPT-5.4 measured
WeirdML39.5%77.7%GPT-5.4
MirrorCodeNot measured15.6%Only GPT-5.4 measured
ALE-BenchNot measured1607.0Only GPT-5.4 measured
LMArena Text: coding14381496GPT-5.4
LMArena Text: software and IT14381491GPT-5.4
LMArena WebDev: ReactNot measured1454Only GPT-5.4 measured
LMArena WebDev: HTML12811478GPT-5.4
LMArena WebDev: from an imageNot measured1436Only GPT-5.4 measured
Neither model has a result on the other 17 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for DeepSeek V3.2 Exp and GPT-5.4
BenchmarkDeepSeek V3.2 ExpGPT-5.4Leader
AA Intelligence Index16.639.0GPT-5.4
Epoch Capabilities Index144.9157.3GPT-5.4
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for DeepSeek V3.2 Exp and GPT-5.4
BenchmarkDeepSeek V3.2 ExpGPT-5.4Leader
Design Arena: agents building web appsNot measured1059Only GPT-5.4 measured
Design Arena: agents building full-stack appsNot measured1017Only GPT-5.4 measured
Design Arena: agents building mobile appsNot measured1087Only GPT-5.4 measured
Design Arena: agents building Android appsNot measured1037Only GPT-5.4 measured
Design Arena: agents building Godot gamesNot measured1135Only GPT-5.4 measured
τ²-Bench Telecom33.9%87.1%GPT-5.4
APEX-AgentsNot measured52.4%Only GPT-5.4 measured
METR time horizonsNot measured74.3%Only GPT-5.4 measured
DeepResearch BenchNot measured35.1%Only GPT-5.4 measured
PostTrainBenchNot measured19.0%Only GPT-5.4 measured
TheAgentCompany42.9%Not measuredOnly DeepSeek V3.2 Exp measured
Vending-Bench 2Not measured$6,144Only GPT-5.4 measured
Neither model has a result on the other 27 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for DeepSeek V3.2 Exp and GPT-5.4
BenchmarkDeepSeek V3.2 ExpGPT-5.4Leader
GPQA DiamondNot measured91.7%Only GPT-5.4 measured
MMMU Pro (Vals AI)Not measured87.5%Only GPT-5.4 measured
GPQA Diamond (Artificial Analysis run)79.7%92.0%GPT-5.4
Humanity's Last Exam (Artificial Analysis run)14.9%43.7%GPT-5.4
GPQA Diamond (Epoch AI run)Not measured93.3%Only GPT-5.4 measured
ARC-AGINot measured93.7%Only GPT-5.4 measured
ARC-AGI-2Not measured74.0%Only GPT-5.4 measured
Chess puzzlesNot measured48.0%Only GPT-5.4 measured
Mystery game puzzlesNot measured37.0%Only GPT-5.4 measured
EBR-benchNot measured25.4%Only GPT-5.4 measured
LMArena Text: hard prompts14291485GPT-5.4
LMArena Text: expert14221507GPT-5.4
LMArena Text: hard prompts in English14441479GPT-5.4
LMArena Vision: diagramsNot measured1327Only GPT-5.4 measured
LMArena Vision: homeworkNot measured1336Only GPT-5.4 measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for DeepSeek V3.2 Exp and GPT-5.4
BenchmarkDeepSeek V3.2 ExpGPT-5.4Leader
AIMENot measured96.7%Only GPT-5.4 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 measured97.8%Only GPT-5.4 measured
FrontierMath, tiers 1 to 3Not measured78.6%Only GPT-5.4 measured
FrontierMath, tier 4Not measured49.0%Only GPT-5.4 measured
ProofBenchNot measured56.0%Only GPT-5.4 measured
LMArena Text: math14221488GPT-5.4
LMArena Text: mathematical work14331497GPT-5.4
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
Knowledge benchmarks for DeepSeek V3.2 Exp and GPT-5.4
BenchmarkDeepSeek V3.2 ExpGPT-5.4Leader
MMLU ProNot measured87.5%Only GPT-5.4 measured
LegalBench (Vals AI)Not measured86.0%Only GPT-5.4 measured
CaseLaw (Vals AI)Not measured63.8%Only GPT-5.4 measured
CorpFin (Vals AI)Not measured65.3%Only GPT-5.4 measured
TaxEval (Vals AI)Not measured74.0%Only GPT-5.4 measured
MortgageTax (Vals AI)Not measured68.3%Only GPT-5.4 measured
MedQA (Vals AI)Not measured96.1%Only GPT-5.4 measured
MedCode (Vals AI)Not measured41.3%Only GPT-5.4 measured
MMLU Pro (Artificial Analysis run)85.0%Not measuredOnly DeepSeek V3.2 Exp measured
SimpleQA VerifiedNot measured45.1%Only GPT-5.4 measured
LMArena Text: science14431479GPT-5.4
LMArena Text: medicine and healthcare14681458DeepSeek V3.2 Exp
LMArena Text: legal and government14191486GPT-5.4
LMArena Text: business and finance14121474GPT-5.4
Neither model has a result on the other 3 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index16
Web design benchmarks for DeepSeek V3.2 Exp and GPT-5.4
BenchmarkDeepSeek V3.2 ExpGPT-5.4Leader
Design Arena: websites11791199GPT-5.4
Design Arena: UI components11661215GPT-5.4
Design Arena: game dev11451195GPT-5.4
Design Arena: data viz11521196GPT-5.4
Design Arena: 3D11481065DeepSeek V3.2 Exp
Design Arena: all code categories11711180GPT-5.4
Design Arena: SVG10311141GPT-5.4
Design Arena: ASCII artNot measured1188Only GPT-5.4 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index023
General benchmarks for DeepSeek V3.2 Exp and GPT-5.4
BenchmarkDeepSeek V3.2 ExpGPT-5.4Leader
SAGE (Vals AI)Not measured43.3%Only GPT-5.4 measured
MedScribe (Vals AI)Not measured77.5%Only GPT-5.4 measured
IFBench54.1%73.9%GPT-5.4
Long context reasoning (AA-LCR)72.3%82.0%GPT-5.4
DTBench87.7%94.4%GPT-5.4
LMCA29.1%52.0%GPT-5.4
Fiction.LiveBench83.3%Not measuredOnly DeepSeek V3.2 Exp measured
CL-bench13.2%27.9%GPT-5.4
Furniture assemblyNot measured37.5%Only GPT-5.4 measured
CL-bench Life9.5%21.7%GPT-5.4
LMArena Text14251469GPT-5.4
LMArena Text: instruction following14011469GPT-5.4
LMArena Text: creative writing14041439GPT-5.4
LMArena VisionNot measured1303Only GPT-5.4 measured
LMArena Text: English14411467GPT-5.4
LMArena Text: non-English14041465GPT-5.4
LMArena Text: Chinese14611520GPT-5.4
LMArena Text: German14401472GPT-5.4
LMArena Text: Japanese13311485GPT-5.4
LMArena Text: Korean13721449GPT-5.4
LMArena Text: French14341493GPT-5.4
LMArena Text: Spanish14411452GPT-5.4
LMArena Text: Russian14121480GPT-5.4
LMArena Text: Polish14351472GPT-5.4
LMArena Text: multi-turn14221482GPT-5.4
LMArena Text: longer queries14181473GPT-5.4
LMArena Text: writing and language14071458GPT-5.4
LMArena Text: entertainment and media14071438GPT-5.4
LMArena Vision: EnglishNot measured1301Only GPT-5.4 measured
LMArena Vision: ChineseNot measured1354Only GPT-5.4 measured
LMArena Vision: reading textNot measured1312Only GPT-5.4 measured
LMArena Vision: humorNot measured1284Only GPT-5.4 measured
LMArena Vision: recognizing thingsNot measured1251Only GPT-5.4 measured
LMArena Vision: creative writingNot measured1283Only GPT-5.4 measured
LMArena DocumentNot measured1471Only GPT-5.4 measured
Neither model has a result on the other 11 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-5.4
BenchmarkDeepSeek V3.2 ExpGPT-5.4Leader
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-5.4$0.010Fits 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-5.4$0.170Fits 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-5.4$0.650Fits 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-5.4
SpecificationDeepSeek V3.2 ExpOpenAI GPT-5.4
MakerDeepSeekOpenAI
WeightsOpen weightsNo public weights listed
ReleasedSeptember 29, 2025March 5, 2026
Context window164K1.05M
Input $/Mtok$0.27$2.50
Output $/Mtok$0.41$15.00
Coding index55.037.7
Independent measurements behind it21
Cost per solved taskNot computable$1.02
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 Exp better than GPT-5.4 for coding?

Pick DeepSeek V3.2 Exp for coding: it scores 55.0 on the Vibe Coding Index against 37.7 for GPT-5.4. Pick GPT-5.4 when the context window is larger, 1.05M against 164K tokens, or when what matters is LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, DeepSeek V3.2 Exp or GPT-5.4?

DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; GPT-5.4 costs $2.50 and $15.00. Cost per solved task is known for GPT-5.4 only.

Which has the bigger context window, DeepSeek V3.2 Exp or GPT-5.4?

GPT-5.4, with 1.05M tokens against 164K.

How many benchmarks is the DeepSeek V3.2 Exp vs GPT-5.4 comparison based on?

50 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2 Exp's coding index rests on 2 independent benchmarks and GPT-5.4's on 1. Where only one model has a result, the page says so instead of naming a winner.

Compare next