Model comparison

DeepSeek-V3.2-Exp vs GPT-5.2

GPT-5.2 is the stronger model overall, scoring 54.1 to 44.3 on the Noometry Index. DeepSeek-V3.2-Exp costs 17× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.

Last verified October 10, 2026. 41 shared benchmarks.

DeepSeek-V3.2-Exp

DeepSeek

44.3

Rank #78 Confirmed

GPT-5.2 OpenAI

54.1

Rank #34 Confirmed

Summary

They share 41 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 1 category and GPT-5.2 in 8 categories; 8 gaps are clear of the uncertainty.
The widest gap is in reasoning, where GPT-5.2 leads 50.2 to 22.1.
The biggest single-benchmark swing is ARC-AGI-2: 4% for DeepSeek-V3.2-Exp and 52.9% for GPT-5.2.
DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
GPT-5.2 accepts more context: 400K tokens versus 164K.
DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.

Side by side

DeepSeek-V3.2-Exp and GPT-5.2 specifications
	DeepSeek-V3.2-Exp	GPT-5.2
Provider	DeepSeek	OpenAI
Noometry Index	44.3	54.1
Released	2025-09-29	2025-12-11
Weights	Open	Proprietary
Context window	164K	400K
Max output	66K	128K
Input $ / M tokens	$0.26	$1.75
Output $ / M tokens	$0.38	$14
Results tracked	49	67

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Category by category

Coding GPT-5.2 leads

DeepSeek-V3.2-Exp: 46.5 (#65), GPT-5.2: 51.6 (#37)

Coding benchmarks
Benchmark	DeepSeek-V3.2-Exp	GPT-5.2
SWE-bench Verified (bash only)	70%	72.8%
LMArena WebDev	1362	1416
SWE-bench Multilingual	59%	66.7%
WeirdML	39.5%	72.2%
LMArena Coding	1454	1447
SWE-bench Verified	—	73.8%
Aider Polyglot	74.2%	—
SciCode	38.9%	—
GSO	—	27.4%
ALE-Bench	—	1,294
AlgoTune	—	2.05

Agentic & Tool Use GPT-5.2 leads

DeepSeek-V3.2-Exp: 32.7 (#59), GPT-5.2: 40.2 (#24)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3.2-Exp	GPT-5.2
Terminal-Bench	39.6%	64.9%
Berkeley Function Calling Leaderboard	56.7%	55.9%
Vending-Bench 2	1,034	3,591
APEX-Agents	21.3%	—
GDPval	—	49.7%
Remote Labor Index	—	2.5%
TheAgentCompany	42.9%	—
τ²-bench Airline	—	83%
τ²-bench Banking	—	32.2%
τ²-bench Retail	—	81.6%
τ²-bench Telecom	—	89.7%
DeepResearch Bench	—	41.1%
LMArena Search	—	1207
METR Time Horizons	—	75.3%

Reasoning GPT-5.2 leads

DeepSeek-V3.2-Exp: 22.1 (#208), GPT-5.2: 50.2 (#35)

Reasoning benchmarks
Benchmark	DeepSeek-V3.2-Exp	GPT-5.2
ARC-AGI-2	4%	52.9%
Kagi LLM Benchmark	52.2%	73.3%
NYT Connections (extended)	36.7%	83.6%
ARC-AGI-1	57%	86.2%
Chess Puzzles	14%	49%
LMArena Hard Prompts	1434	1445
DTBench	87.7%	90.9%
LMCA	29.1%	43.9%
Epoch Capabilities Index	146.27	153.45
SimpleBench	—	45.8%
CritPt	2.9%	—
EnigmaEval	—	10.4%
Thematic Generalization	65%	—
EBR-Bench	—	23%
Mystery Game Puzzles	—	23%
ForecastBench	—	60.1

Math GPT-5.2 leads

DeepSeek-V3.2-Exp: 41.7 (#87), GPT-5.2: 60.0 (#38)

Math benchmarks
Benchmark	DeepSeek-V3.2-Exp	GPT-5.2
MathArena Final-Answer Competitions	57.7%	72%
OTIS Mock AIME 2024-2025	87.8%	96.1%
ProofBench	8%	15%
LMArena Math	1435	1440
FrontierMath (Feb 2025 set)	22.1%	40.7%
FrontierMath Tier 4 (v1)	2.1%	18.8%
FrontierMath (Tiers 1-3)	—	67.4%
FrontierMath Tier 4	—	31.7%

Knowledge GPT-5.2 leads

DeepSeek-V3.2-Exp: 51.7 (#66), GPT-5.2: 59.3 (#32)

Knowledge benchmarks
Benchmark	DeepSeek-V3.2-Exp	GPT-5.2
GPQA Diamond	83.4%	91.4%
Vectara Hallucination Rate	5.3%	8.4%
LMArena Expert	1436	1445
Humanity's Last Exam	—	27.8%
SimpleQA Verified	—	37.1%

Multimodal Not comparable

DeepSeek-V3.2-Exp: —, GPT-5.2: 51.3 (#7)

Multimodal benchmarks
Benchmark	DeepSeek-V3.2-Exp	GPT-5.2
LMArena Vision	—	1268
VPCT	—	84%
Furniture Assembly	—	38.3%
LMArena Document	—	1405

Multilingual GPT-5.2 leads

DeepSeek-V3.2-Exp: 52.2 (#90), GPT-5.2: 53.4 (#67)

Multilingual benchmarks
Benchmark	DeepSeek-V3.2-Exp	GPT-5.2
LMArena Non-English	1409	1425
LMArena Chinese	1461	1460
LMArena French	1433	1455
LMArena German	1440	1448
LMArena Japanese	1374	1420
LMArena Korean	1371	1392
LMArena Russian	1424	1440
LMArena Spanish	1440	1433

Instruction Following Too close to call

DeepSeek-V3.2-Exp: 74.5 (#93), GPT-5.2: 74.7 (#89)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.2-Exp	GPT-5.2
LMArena Instruction Following	1413	1417

Long Context DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 47.6 (#16), GPT-5.2: 44.0 (#78)

Long Context benchmarks
Benchmark	DeepSeek-V3.2-Exp	GPT-5.2
CL-bench	13.2%	18.2%
LMArena Longer Query	1428	1428
Fiction.LiveBench	83.3%	—
CL-bench Life	9.5%	—

Writing & Preference GPT-5.2 leads

DeepSeek-V3.2-Exp: 62.4 (#77), GPT-5.2: 66.8 (#32)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.2-Exp	GPT-5.2
LMArena Text	1425	1439
LMArena Creative Writing	1403	1401
EQ-Bench Creative Writing	1515	1703
LMArena Multi-Turn	1427	1458

Frequently asked questions

Is DeepSeek-V3.2-Exp better than GPT-5.2?

Which is cheaper, DeepSeek-V3.2-Exp or GPT-5.2?

DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; GPT-5.2 lists at $1.75 and $14.

Is DeepSeek-V3.2-Exp or GPT-5.2 better for coding?

GPT-5.2 scores higher on coding benchmarks: 51.6 versus 46.5 in the Noometry coding category.

Which has the bigger context window?

GPT-5.2 does, with 400K tokens against 164K.

How many benchmarks do DeepSeek-V3.2-Exp and GPT-5.2 share?

41 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and GPT-5.2 has 67.