Noometry

Model comparison

DeepSeek-V3.1 vs Gemini 2.0 Flash-Lite

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 37.8 on the Noometry Index.

Last verified . 19 shared benchmarks.

DeepSeek-V3.1 DeepSeek

42.8

Rank #108 Confirmed

Gemini 2.0 Flash-Lite Google

37.8

Rank #194 Confirmed

Summary

  • They share 19 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 7 categories and Gemini 2.0 Flash-Lite in 1 category; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where DeepSeek-V3.1 leads 43.7 to 35.0.
  • The biggest single-benchmark swing is DTBench: 82.7% for DeepSeek-V3.1 and 52.5% for Gemini 2.0 Flash-Lite.
  • DeepSeek-V3.1 has downloadable open weights; the other is API-only.

Side by side

DeepSeek-V3.1Gemini 2.0 Flash-Lite
ProviderDeepSeekGoogle
Noometry Index42.837.8
Released2025-08-212025-02-05
WeightsOpenProprietary
Context window164K—
Max output8K—
Input $ / M tokens$0.25—
Output $ / M tokens$0.95—
Results tracked2732

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Category by category

Coding DeepSeek-V3.1 leads

DeepSeek-V3.1: 40.3 (#144), Gemini 2.0 Flash-Lite: 37.9 (#185)

BenchmarkDeepSeek-V3.1Gemini 2.0 Flash-Lite
LMArena Coding14171322
WeirdML38.4%—
LiveBench Coding—47.1%

Reasoning DeepSeek-V3.1 leads

DeepSeek-V3.1: 27.9 (#110), Gemini 2.0 Flash-Lite: 22.0 (#210)

BenchmarkDeepSeek-V3.1Gemini 2.0 Flash-Lite
LMArena Hard Prompts14171324
DTBench82.7%52.5%
ForecastBench5857.1
SimpleBench40%—
Kagi LLM Benchmark53.2%—
LiveBench Reasoning—50.1%
LiveBench Data Analysis—65.5%
LMCA24.3%—
Epoch Capabilities Index139.92—
LiveBench—54.3%

Math DeepSeek-V3.1 leads

DeepSeek-V3.1: 38.9 (#122), Gemini 2.0 Flash-Lite: 34.1 (#196)

BenchmarkDeepSeek-V3.1Gemini 2.0 Flash-Lite
LMArena Math14201309
Omni-MATH—37.4%
LiveBench Math—58.1%

Knowledge DeepSeek-V3.1 leads

DeepSeek-V3.1: 43.7 (#90), Gemini 2.0 Flash-Lite: 35.0 (#189)

BenchmarkDeepSeek-V3.1Gemini 2.0 Flash-Lite
LMArena Expert14051305
MMLU-Pro—72%
Vectara Hallucination Rate5.5%—
GPQA (HELM)—50%

Multimodal Not comparable

DeepSeek-V3.1: —, Gemini 2.0 Flash-Lite: 31.2 (#109)

BenchmarkDeepSeek-V3.1Gemini 2.0 Flash-Lite
LMArena Vision—1100

Multilingual DeepSeek-V3.1 leads

DeepSeek-V3.1: 51.6 (#106), Gemini 2.0 Flash-Lite: 46.0 (#161)

BenchmarkDeepSeek-V3.1Gemini 2.0 Flash-Lite
LMArena Non-English14001323
LMArena Chinese14691339
LMArena French14471347
LMArena German14111306
LMArena Japanese13781301
LMArena Korean13371325
LMArena Russian14051328
LMArena Spanish14311313

Instruction Following DeepSeek-V3.1 leads

DeepSeek-V3.1: 73.9 (#110), Gemini 2.0 Flash-Lite: 70.4 (#163)

BenchmarkDeepSeek-V3.1Gemini 2.0 Flash-Lite
LMArena Instruction Following14001305
LiveBench Instruction Following—78.3%
IFEval—82.4%

Long Context Gemini 2.0 Flash-Lite leads

DeepSeek-V3.1: 36.3 (#232), Gemini 2.0 Flash-Lite: 40.1 (#160)

BenchmarkDeepSeek-V3.1Gemini 2.0 Flash-Lite
LMArena Longer Query14221320
Fiction.LiveBench52.8%—

Writing & Preference DeepSeek-V3.1 leads

DeepSeek-V3.1: 60.3 (#98), Gemini 2.0 Flash-Lite: 51.7 (#177)

BenchmarkDeepSeek-V3.1Gemini 2.0 Flash-Lite
LMArena Text14201330
LMArena Creative Writing14011319
LMArena Multi-Turn14081307
EQ-Bench Creative Writing1436—
WildBench—79%
LiveBench Language—34.3%

Frequently asked questions

Is DeepSeek-V3.1 better than Gemini 2.0 Flash-Lite?

DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 37.8 on the Noometry Index.

Is DeepSeek-V3.1 or Gemini 2.0 Flash-Lite better for coding?

DeepSeek-V3.1 scores higher on coding benchmarks: 40.3 versus 37.9 in the Noometry coding category.

How many benchmarks do DeepSeek-V3.1 and Gemini 2.0 Flash-Lite share?

19 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Gemini 2.0 Flash-Lite has 32.

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