Model comparison

DeepSeek-V3.1 vs Gemini 2.5 Flash-Lite

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 37.0 on the Noometry Index. Gemini 2.5 Flash-Lite costs 2.4× less per token, which makes it the better buy when DeepSeek-V3.1's lead doesn't matter for your workload.

Last verified October 10, 2026. 24 shared benchmarks.

DeepSeek-V3.1

DeepSeek

42.8

Rank #108 Confirmed

Gemini 2.5 Flash-Lite

Google

37.0

Rank #211 Confirmed

Summary

They share 24 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 8 categories and Gemini 2.5 Flash-Lite in 0 categories; 7 gaps are clear of the uncertainty.
The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.7 to 32.5.
The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 62.8% for Gemini 2.5 Flash-Lite.
Gemini 2.5 Flash-Lite is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.25 / $0.95 for DeepSeek-V3.1.
Gemini 2.5 Flash-Lite accepts more context: 1.05M tokens versus 164K.
DeepSeek-V3.1 has downloadable open weights; the other is API-only.

Side by side

DeepSeek-V3.1 and Gemini 2.5 Flash-Lite specifications
	DeepSeek-V3.1	Gemini 2.5 Flash-Lite
Provider	DeepSeek	Google
Noometry Index	42.8	37.0
Released	2025-08-21	2025-06-17
Weights	Open	Proprietary
Context window	164K	1.05M
Max output	8K	66K
Input $ / M tokens	$0.25	$0.10
Output $ / M tokens	$0.95	$0.40
Results tracked	27	33

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Category by category

Coding DeepSeek-V3.1 leads

DeepSeek-V3.1: 40.3 (#144), Gemini 2.5 Flash-Lite: 38.5 (#173)

Coding benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash-Lite
WeirdML	38.4%	35.2%
LMArena Coding	1417	1373
ALE-Bench	—	325.9

Agentic & Tool Use Not comparable

DeepSeek-V3.1: —, Gemini 2.5 Flash-Lite: 28.0 (#96)

Agentic & Tool Use benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash-Lite
Berkeley Function Calling Leaderboard	—	36.9%

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.9 (#110), Gemini 2.5 Flash-Lite: 22.2 (#205)

Reasoning benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash-Lite
Kagi LLM Benchmark	53.2%	40.5%
LMArena Hard Prompts	1417	1377
DTBench	82.7%	62.8%
LMCA	24.3%	18.1%
Epoch Capabilities Index	139.92	133.94
SimpleBench	40%	—
ForecastBench	58	—

Math Too close to call

DeepSeek-V3.1: 38.9 (#122), Gemini 2.5 Flash-Lite: 38.0 (#144)

Math benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash-Lite
LMArena Math	1420	1373
Omni-MATH	—	48%

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.7 (#90), Gemini 2.5 Flash-Lite: 32.5 (#210)

Knowledge benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash-Lite
Vectara Hallucination Rate	5.5%	3.3%
LMArena Expert	1405	1373
MMLU-Pro	—	53.7%
GPQA (HELM)	—	30.9%

Multimodal Not comparable

DeepSeek-V3.1: —, Gemini 2.5 Flash-Lite: 29.1 (#114)

Multimodal benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash-Lite
LMArena Vision	—	1198
VPCT	—	30%

Multilingual DeepSeek-V3.1 leads

DeepSeek-V3.1: 51.6 (#106), Gemini 2.5 Flash-Lite: 49.3 (#134)

Multilingual benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash-Lite
LMArena Non-English	1400	1369
LMArena Chinese	1469	1404
LMArena French	1447	1388
LMArena German	1411	1389
LMArena Japanese	1378	1359
LMArena Korean	1337	1360
LMArena Russian	1405	1373
LMArena Spanish	1431	1396

Instruction Following DeepSeek-V3.1 leads

DeepSeek-V3.1: 73.9 (#110), Gemini 2.5 Flash-Lite: 70.0 (#168)

Instruction Following benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash-Lite
LMArena Instruction Following	1400	1367
IFEval	—	81%

Long Context DeepSeek-V3.1 leads

DeepSeek-V3.1: 36.3 (#232), Gemini 2.5 Flash-Lite: 33.3 (#262)

Long Context benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash-Lite
Fiction.LiveBench	52.8%	47.2%
LMArena Longer Query	1422	1373

Writing & Preference DeepSeek-V3.1 leads

DeepSeek-V3.1: 60.3 (#98), Gemini 2.5 Flash-Lite: 56.8 (#135)

Writing & Preference benchmarks
Benchmark	DeepSeek-V3.1	Gemini 2.5 Flash-Lite
LMArena Text	1420	1379
LMArena Creative Writing	1401	1367
LMArena Multi-Turn	1408	1366
EQ-Bench Creative Writing	1436	—
WildBench	—	81.8%

Frequently asked questions

Is DeepSeek-V3.1 better than Gemini 2.5 Flash-Lite?

Which is cheaper, DeepSeek-V3.1 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-V3.1 lists at $0.25 and $0.95.

Is DeepSeek-V3.1 or Gemini 2.5 Flash-Lite better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 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-V3.1 and Gemini 2.5 Flash-Lite share?

24 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Gemini 2.5 Flash-Lite has 33.