Model comparison

DeepSeek-V3 vs Gemini 2.5 Flash-Lite

DeepSeek-V3 is the stronger model overall, scoring 39.5 to 37.0 on the Noometry Index. Gemini 2.5 Flash-Lite costs 2.3× less per token, which makes it the better buy when DeepSeek-V3's lead doesn't matter for your workload.

Last verified October 10, 2026. 29 shared benchmarks.

DeepSeek-V3

DeepSeek

39.5

Rank #166 Confirmed

Gemini 2.5 Flash-Lite

Google

37.0

Rank #211 Confirmed

Summary

They share 29 benchmarks with published results for both. DeepSeek-V3 scores higher in 5 categories and Gemini 2.5 Flash-Lite in 3 categories; 5 gaps are clear of the uncertainty.
The widest gap is in math, where Gemini 2.5 Flash-Lite leads 38.0 to 32.1.
The biggest single-benchmark swing is GPQA (HELM): 53.8% for DeepSeek-V3 and 30.9% for Gemini 2.5 Flash-Lite.
Gemini 2.5 Flash-Lite is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.24 / $0.90 for DeepSeek-V3.
Gemini 2.5 Flash-Lite accepts more context: 1.05M tokens versus 164K.
DeepSeek-V3 has downloadable open weights; the other is API-only.

Side by side

DeepSeek-V3 and Gemini 2.5 Flash-Lite specifications
	DeepSeek-V3	Gemini 2.5 Flash-Lite
Provider	DeepSeek	Google
Noometry Index	39.5	37.0
Released	2024-12-26	2025-06-17
Weights	Open	Proprietary
Context window	164K	1.05M
Max output	164K	66K
Input $ / M tokens	$0.24	$0.10
Output $ / M tokens	$0.90	$0.40
Results tracked	60	33

Sponsored placements are available on pages like this one. Advertise on Noometry

Category by category

Coding DeepSeek-V3 leads

DeepSeek-V3: 42.3 (#106), Gemini 2.5 Flash-Lite: 38.5 (#173)

Coding benchmarks
Benchmark	DeepSeek-V3	Gemini 2.5 Flash-Lite
WeirdML	36.1%	35.2%
LMArena Coding	1368	1373
Aider Polyglot	55.1%	—
SciCode	35.8%	—
BigCodeBench Instruct	50%	—
LiveBench Coding	70.9%	—
BigCodeBench Complete	62.2%	—
ALE-Bench	—	325.9
HumanEval+	86.6%	—
MBPP+	73%	—

Agentic & Tool Use Not comparable

DeepSeek-V3: —, Gemini 2.5 Flash-Lite: 28.0 (#96)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3	Gemini 2.5 Flash-Lite
Berkeley Function Calling Leaderboard	—	36.9%
METR Time Horizons	49.6%	—

Reasoning Gemini 2.5 Flash-Lite leads

DeepSeek-V3: 20.5 (#236), Gemini 2.5 Flash-Lite: 22.2 (#205)

Reasoning benchmarks
Benchmark	DeepSeek-V3	Gemini 2.5 Flash-Lite
Kagi LLM Benchmark	52.3%	40.5%
LMArena Hard Prompts	1365	1377
DTBench	64.8%	62.8%
LMCA	15.5%	18.1%
Epoch Capabilities Index	135.94	133.94
SimpleBench	27.2%	—
CritPt	0%	—
LiveBench Reasoning	65.8%	—
LiveBench Data Analysis	60.9%	—
BIG-Bench Hard	87.5%	—
ForecastBench	59.1	—
HellaSwag	88.9%	—
LiveBench	66.9%	—
PIQA	84.7%	—
WinoGrande	85.2%	—

Math Gemini 2.5 Flash-Lite leads

DeepSeek-V3: 32.1 (#219), Gemini 2.5 Flash-Lite: 38.0 (#144)

Math benchmarks
Benchmark	DeepSeek-V3	Gemini 2.5 Flash-Lite
Omni-MATH	40.3%	48%
LMArena Math	1373	1373
OTIS Mock AIME 2024-2025	37.8%	—
LiveBench Math	73.5%	—
MATH Level 5	75.5%	—
FrontierMath (Feb 2025 set)	1.7%	—

Knowledge DeepSeek-V3 leads

DeepSeek-V3: 37.5 (#155), Gemini 2.5 Flash-Lite: 32.5 (#210)

Knowledge benchmarks
Benchmark	DeepSeek-V3	Gemini 2.5 Flash-Lite
MMLU-Pro	72.3%	53.7%
Vectara Hallucination Rate	6.1%	3.3%
GPQA (HELM)	53.8%	30.9%
LMArena Expert	1351	1373
GPQA Diamond	67.6%	—
Confabulations	26.1%	—
ARC (AI2) Challenge	95.3%	—
MMLU	87.2%	—
TriviaQA	82.9%	—

Multimodal Not comparable

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

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

Multilingual Too close to call

DeepSeek-V3: 48.5 (#143), Gemini 2.5 Flash-Lite: 49.3 (#134)

Multilingual benchmarks
Benchmark	DeepSeek-V3	Gemini 2.5 Flash-Lite
LMArena Non-English	1358	1369
LMArena Chinese	1391	1404
LMArena French	1385	1388
LMArena German	1374	1389
LMArena Japanese	1333	1359
LMArena Korean	1319	1360
LMArena Russian	1373	1373
LMArena Spanish	1358	1396

Instruction Following DeepSeek-V3 leads

DeepSeek-V3: 72.8 (#130), Gemini 2.5 Flash-Lite: 70.0 (#168)

Instruction Following benchmarks
Benchmark	DeepSeek-V3	Gemini 2.5 Flash-Lite
IFEval	83.2%	81%
LMArena Instruction Following	1345	1367
LiveBench Instruction Following	81.5%	—

Long Context Too close to call

DeepSeek-V3: 34.0 (#253), Gemini 2.5 Flash-Lite: 33.3 (#262)

Long Context benchmarks
Benchmark	DeepSeek-V3	Gemini 2.5 Flash-Lite
Fiction.LiveBench	50%	47.2%
LMArena Longer Query	1352	1373

Writing & Preference Too close to call

DeepSeek-V3: 57.4 (#130), Gemini 2.5 Flash-Lite: 56.8 (#135)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3	Gemini 2.5 Flash-Lite
LMArena Text	1375	1379
LMArena Creative Writing	1364	1367
WildBench	83%	81.8%
LMArena Multi-Turn	1389	1366
Short-Story Creative Writing	77%	—
EQ-Bench Creative Writing	1472	—
LiveBench Language	49.1%	—

Frequently asked questions

Is DeepSeek-V3 better than Gemini 2.5 Flash-Lite?

Which is cheaper, DeepSeek-V3 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 lists at $0.24 and $0.90.

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

DeepSeek-V3 scores higher on coding benchmarks: 42.3 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 and Gemini 2.5 Flash-Lite share?

29 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and Gemini 2.5 Flash-Lite has 33.