Model comparison

DeepSeek V4.1 Flash vs GPT-5.2

GPT-5.2 is the stronger model overall, scoring 54.1 to 52.8 on the Noometry Index. DeepSeek V4.1 Flash costs 18× 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. 32 shared benchmarks.

DeepSeek V4.1 Flash

DeepSeek

52.8

Rank #38 Confirmed

GPT-5.2 OpenAI

54.1

Rank #34 Confirmed

Summary

They share 32 benchmarks with published results for both. DeepSeek V4.1 Flash scores higher in 5 categories and GPT-5.2 in 5 categories; 9 gaps are clear of the uncertainty.
The widest gap is in multimodal, where GPT-5.2 leads 51.3 to 39.1.
The biggest single-benchmark swing is ProofBench: 54% for DeepSeek V4.1 Flash and 15% for GPT-5.2.
DeepSeek V4.1 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
DeepSeek V4.1 Flash accepts more context: 1M tokens versus 400K.
DeepSeek V4.1 Flash has downloadable open weights; the other is API-only.

Side by side

DeepSeek V4.1 Flash and GPT-5.2 specifications
	DeepSeek V4.1 Flash	GPT-5.2
Provider	DeepSeek	OpenAI
Noometry Index	52.8	54.1
Released	2026-09-09	2025-12-11
Weights	Open	Proprietary
Context window	1M	400K
Max output	393K	128K
Input $ / M tokens	$0.15	$1.75
Output $ / M tokens	$0.60	$14
Results tracked	37	67

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Category by category

Coding DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 52.9 (#32), GPT-5.2: 51.6 (#37)

Coding benchmarks
Benchmark	DeepSeek V4.1 Flash	GPT-5.2
LMArena WebDev	1619	1416
LMArena Coding	1506	1447
ALE-Bench	1,092	1,294
SWE-bench Verified	—	73.8%
SWE-bench Verified (bash only)	—	72.8%
SWE-bench Multilingual	—	66.7%
SciCode	51.9%	—
GSO	—	27.4%
WeirdML	—	72.2%
AlgoTune	—	2.05

Agentic & Tool Use GPT-5.2 leads

DeepSeek V4.1 Flash: 31.2 (#69), GPT-5.2: 40.2 (#24)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek V4.1 Flash	GPT-5.2
Terminal-Bench	—	64.9%
APEX-Agents	39.5%	—
Berkeley Function Calling Leaderboard	—	55.9%
GDPval	—	49.7%
Remote Labor Index	—	2.5%
τ²-bench Airline	—	83%
τ²-bench Banking	—	32.2%
τ²-bench Retail	—	81.6%
τ²-bench Telecom	—	89.7%
DeepResearch Bench	—	41.1%
GDP.pdf	19.8%	—
LMArena Search	—	1207
METR Time Horizons	—	75.3%
Vending-Bench 2	—	3,591

Reasoning Too close to call

DeepSeek V4.1 Flash: 50.2 (#36), GPT-5.2: 50.2 (#35)

Reasoning benchmarks
Benchmark	DeepSeek V4.1 Flash	GPT-5.2
NYT Connections (extended)	89.6%	83.6%
LMArena Hard Prompts	1483	1445
Mystery Game Puzzles	43%	23%
DTBench	89.9%	90.9%
LMCA	47%	43.9%
Epoch Capabilities Index	154.9	153.45
ARC-AGI-2	—	52.9%
SimpleBench	—	45.8%
Kagi LLM Benchmark	—	73.3%
ARC-AGI-1	—	86.2%
CritPt	14.3%	—
Chess Puzzles	—	49%
EnigmaEval	—	10.4%
EBR-Bench	—	23%
Surface Evolver Bench	46.3%	—
ForecastBench	—	60.1

Math DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 66.7 (#25), GPT-5.2: 60.0 (#38)

Math benchmarks
Benchmark	DeepSeek V4.1 Flash	GPT-5.2
FrontierMath (Tiers 1-3)	67.4%	67.4%
FrontierMath Tier 4	26.8%	31.7%
OTIS Mock AIME 2024-2025	98.3%	96.1%
ProofBench	54%	15%
LMArena Math	1477	1440
MathArena Final-Answer Competitions	—	72%
FrontierMath (Feb 2025 set)	—	40.7%
FrontierMath Tier 4 (v1)	—	18.8%

Knowledge GPT-5.2 leads

DeepSeek V4.1 Flash: 57.9 (#38), GPT-5.2: 59.3 (#32)

Knowledge benchmarks
Benchmark	DeepSeek V4.1 Flash	GPT-5.2
GPQA Diamond	89.8%	91.4%
LMArena Expert	1506	1445
Humanity's Last Exam	—	27.8%
SimpleQA Verified	—	37.1%
Vectara Hallucination Rate	—	8.4%

Multimodal GPT-5.2 leads

DeepSeek V4.1 Flash: 39.1 (#61), GPT-5.2: 51.3 (#7)

Multimodal benchmarks
Benchmark	DeepSeek V4.1 Flash	GPT-5.2
LMArena Vision	1277	1268
Furniture Assembly	34.2%	38.3%
VPCT	—	84%
LMArena Document	—	1405

Multilingual DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 55.0 (#35), GPT-5.2: 53.4 (#67)

Multilingual benchmarks
Benchmark	DeepSeek V4.1 Flash	GPT-5.2
LMArena Non-English	1448	1425
LMArena Chinese	1497	1460
LMArena French	1452	1455
LMArena German	1484	1448
LMArena Japanese	1412	1420
LMArena Korean	1452	1392
LMArena Russian	1471	1440
LMArena Spanish	1459	1433

Instruction Following DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 77.3 (#26), GPT-5.2: 74.7 (#89)

Instruction Following benchmarks
Benchmark	DeepSeek V4.1 Flash	GPT-5.2
LMArena Instruction Following	1474	1417

Long Context DeepSeek V4.1 Flash leads

DeepSeek V4.1 Flash: 45.2 (#47), GPT-5.2: 44.0 (#78)

Long Context benchmarks
Benchmark	DeepSeek V4.1 Flash	GPT-5.2
LMArena Longer Query	1475	1428
CL-bench	—	18.2%

Writing & Preference GPT-5.2 leads

DeepSeek V4.1 Flash: 65.4 (#48), GPT-5.2: 66.8 (#32)

Writing & Preference benchmarks
Benchmark	DeepSeek V4.1 Flash	GPT-5.2
LMArena Text	1462	1439
LMArena Creative Writing	1435	1401
EQ-Bench Creative Writing	1540	1703
LMArena Multi-Turn	1457	1458

Frequently asked questions

Is DeepSeek V4.1 Flash better than GPT-5.2?

Which is cheaper, DeepSeek V4.1 Flash or GPT-5.2?

DeepSeek V4.1 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GPT-5.2 lists at $1.75 and $14.

Is DeepSeek V4.1 Flash or GPT-5.2 better for coding?

DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.9 versus 51.6 in the Noometry coding category.

Which has the bigger context window?

DeepSeek V4.1 Flash does, with 1M tokens against 400K.

How many benchmarks do DeepSeek V4.1 Flash and GPT-5.2 share?

32 benchmarks have published results for both models. DeepSeek V4.1 Flash has 37 scored results on Noometry and GPT-5.2 has 67.