Model comparison

DeepSeek-V3.2-Exp vs Gemini 2.5 Flash-Lite

DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 37.0 on the Noometry Index. Gemini 2.5 Flash-Lite costs 1.7× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.

Last verified October 10, 2026. 25 shared benchmarks.

DeepSeek-V3.2-Exp

DeepSeek

44.3

Rank #78 Confirmed

Gemini 2.5 Flash-Lite

Google

37.0

Rank #211 Confirmed

Summary

They share 25 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 8 categories and Gemini 2.5 Flash-Lite in 1 category; 8 gaps are clear of the uncertainty.
The widest gap is in knowledge, where DeepSeek-V3.2-Exp leads 51.7 to 32.5.
The biggest single-benchmark swing is Fiction.LiveBench: 83.3% for DeepSeek-V3.2-Exp and 47.2% for Gemini 2.5 Flash-Lite.
Gemini 2.5 Flash-Lite is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.26 / $0.38 for DeepSeek-V3.2-Exp.
Gemini 2.5 Flash-Lite accepts more context: 1.05M tokens versus 164K.
DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.

Side by side

DeepSeek-V3.2-Exp and Gemini 2.5 Flash-Lite specifications
	DeepSeek-V3.2-Exp	Gemini 2.5 Flash-Lite
Provider	DeepSeek	Google
Noometry Index	44.3	37.0
Released	2025-09-29	2025-06-17
Weights	Open	Proprietary
Context window	164K	1.05M
Max output	66K	66K
Input $ / M tokens	$0.26	$0.10
Output $ / M tokens	$0.38	$0.40
Results tracked	49	33

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Category by category

Coding DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 46.5 (#65), Gemini 2.5 Flash-Lite: 38.5 (#173)

Coding benchmarks
Benchmark	DeepSeek-V3.2-Exp	Gemini 2.5 Flash-Lite
WeirdML	39.5%	35.2%
LMArena Coding	1454	1373
SWE-bench Verified (bash only)	70%	—
Aider Polyglot	74.2%	—
LMArena WebDev	1362	—
SWE-bench Multilingual	59%	—
SciCode	38.9%	—
ALE-Bench	—	325.9

Agentic & Tool Use DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 32.7 (#59), Gemini 2.5 Flash-Lite: 28.0 (#96)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3.2-Exp	Gemini 2.5 Flash-Lite
Berkeley Function Calling Leaderboard	56.7%	36.9%
Terminal-Bench	39.6%	—
APEX-Agents	21.3%	—
TheAgentCompany	42.9%	—
Vending-Bench 2	1,034	—

Reasoning Too close to call

DeepSeek-V3.2-Exp: 22.1 (#208), Gemini 2.5 Flash-Lite: 22.2 (#205)

Reasoning benchmarks
Benchmark	DeepSeek-V3.2-Exp	Gemini 2.5 Flash-Lite
Kagi LLM Benchmark	52.2%	40.5%
LMArena Hard Prompts	1434	1377
DTBench	87.7%	62.8%
LMCA	29.1%	18.1%
Epoch Capabilities Index	146.27	133.94
ARC-AGI-2	4%	—
NYT Connections (extended)	36.7%	—
ARC-AGI-1	57%	—
CritPt	2.9%	—
Chess Puzzles	14%	—
Thematic Generalization	65%	—

Math DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 41.7 (#87), Gemini 2.5 Flash-Lite: 38.0 (#144)

Math benchmarks
Benchmark	DeepSeek-V3.2-Exp	Gemini 2.5 Flash-Lite
LMArena Math	1435	1373
MathArena Final-Answer Competitions	57.7%	—
OTIS Mock AIME 2024-2025	87.8%	—
ProofBench	8%	—
Omni-MATH	—	48%
FrontierMath (Feb 2025 set)	22.1%	—
FrontierMath Tier 4 (v1)	2.1%	—

Knowledge DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 51.7 (#66), Gemini 2.5 Flash-Lite: 32.5 (#210)

Knowledge benchmarks
Benchmark	DeepSeek-V3.2-Exp	Gemini 2.5 Flash-Lite
Vectara Hallucination Rate	5.3%	3.3%
LMArena Expert	1436	1373
GPQA Diamond	83.4%	—
MMLU-Pro	—	53.7%
GPQA (HELM)	—	30.9%

Multimodal Not comparable

DeepSeek-V3.2-Exp: —, Gemini 2.5 Flash-Lite: 29.1 (#114)

Multimodal benchmarks
Benchmark	DeepSeek-V3.2-Exp	Gemini 2.5 Flash-Lite
LMArena Vision	—	1198
VPCT	—	30%

Multilingual DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 52.2 (#90), Gemini 2.5 Flash-Lite: 49.3 (#134)

Multilingual benchmarks
Benchmark	DeepSeek-V3.2-Exp	Gemini 2.5 Flash-Lite
LMArena Non-English	1409	1369
LMArena Chinese	1461	1404
LMArena French	1433	1388
LMArena German	1440	1389
LMArena Japanese	1374	1359
LMArena Korean	1371	1360
LMArena Russian	1424	1373
LMArena Spanish	1440	1396

Instruction Following DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 74.5 (#93), Gemini 2.5 Flash-Lite: 70.0 (#168)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.2-Exp	Gemini 2.5 Flash-Lite
LMArena Instruction Following	1413	1367
IFEval	—	81%

Long Context DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 47.6 (#16), Gemini 2.5 Flash-Lite: 33.3 (#262)

Long Context benchmarks
Benchmark	DeepSeek-V3.2-Exp	Gemini 2.5 Flash-Lite
Fiction.LiveBench	83.3%	47.2%
LMArena Longer Query	1428	1373
CL-bench	13.2%	—
CL-bench Life	9.5%	—

Writing & Preference DeepSeek-V3.2-Exp leads

DeepSeek-V3.2-Exp: 62.4 (#77), Gemini 2.5 Flash-Lite: 56.8 (#135)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.2-Exp	Gemini 2.5 Flash-Lite
LMArena Text	1425	1379
LMArena Creative Writing	1403	1367
LMArena Multi-Turn	1427	1366
EQ-Bench Creative Writing	1515	—
WildBench	—	81.8%

Frequently asked questions

Is DeepSeek-V3.2-Exp better than Gemini 2.5 Flash-Lite?

Which is cheaper, DeepSeek-V3.2-Exp or Gemini 2.5 Flash-Lite?

Gemini 2.5 Flash-Lite is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; DeepSeek-V3.2-Exp lists at $0.26 and $0.38.

Is DeepSeek-V3.2-Exp or Gemini 2.5 Flash-Lite better for coding?

DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 38.5 in the Noometry coding category.

Which has the bigger context window?

Gemini 2.5 Flash-Lite does, with 1.05M tokens against 164K.

How many benchmarks do DeepSeek-V3.2-Exp and Gemini 2.5 Flash-Lite share?

25 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Gemini 2.5 Flash-Lite has 33.