Noometry

Model comparison

DeepSeek-V3.2-Speciale vs Gemini 2.0 Flash-Lite

DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.1 to 37.3 on the Noometry Index.

Last verified . 0 shared benchmarks.

DeepSeek-V3.2-Speciale DeepSeek

39.1

Rank #159 Reported

Gemini 2.0 Flash-Lite Google

37.3

Rank #189 Confirmed

Summary

  • The widest gap is in reasoning, where DeepSeek-V3.2-Speciale leads 32.1 to 21.2.
  • DeepSeek-V3.2-Speciale has downloadable open weights; the other is API-only.

Side by side

DeepSeek-V3.2-SpecialeGemini 2.0 Flash-Lite
ProviderDeepSeekGoogle
Noometry Index39.137.3
Released2025-12-012025-02-05
WeightsOpenProprietary
Context window128K—
Max output128K—
Input $ / M tokens$0.58—
Output $ / M tokens$1.68—
Results tracked332

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Category by category

Coding DeepSeek-V3.2-Speciale leads

DeepSeek-V3.2-Speciale: 39.9 (#136), Gemini 2.0 Flash-Lite: 37.4 (#181)

BenchmarkDeepSeek-V3.2-SpecialeGemini 2.0 Flash-Lite
WeirdML46.7%—
LiveBench Coding—47.1%
LMArena Coding—1322

Reasoning DeepSeek-V3.2-Speciale leads

DeepSeek-V3.2-Speciale: 32.1 (#65), Gemini 2.0 Flash-Lite: 21.2 (#203)

BenchmarkDeepSeek-V3.2-SpecialeGemini 2.0 Flash-Lite
SimpleBench52.6%—
LiveBench Reasoning—50.1%
LMArena Hard Prompts—1324
DTBench—52.5%
LiveBench Data Analysis—65.5%
ForecastBench—57.1
LiveBench—54.3%

Math Not comparable

DeepSeek-V3.2-Speciale: —, Gemini 2.0 Flash-Lite: 31.8 (#192)

BenchmarkDeepSeek-V3.2-SpecialeGemini 2.0 Flash-Lite
Omni-MATH—37.4%
LiveBench Math—58.1%
LMArena Math—1309

Knowledge Not comparable

DeepSeek-V3.2-Speciale: —, Gemini 2.0 Flash-Lite: 34.6 (#185)

BenchmarkDeepSeek-V3.2-SpecialeGemini 2.0 Flash-Lite
MMLU-Pro—72%
GPQA (HELM)—50%
LMArena Expert—1305

Multimodal Not comparable

DeepSeek-V3.2-Speciale: —, Gemini 2.0 Flash-Lite: 30.6 (#103)

BenchmarkDeepSeek-V3.2-SpecialeGemini 2.0 Flash-Lite
LMArena Vision—1100

Multilingual Not comparable

DeepSeek-V3.2-Speciale: —, Gemini 2.0 Flash-Lite: 46.2 (#156)

BenchmarkDeepSeek-V3.2-SpecialeGemini 2.0 Flash-Lite
LMArena Non-English—1323
LMArena Chinese—1339
LMArena French—1346
LMArena German—1306
LMArena Japanese—1302
LMArena Korean—1325
LMArena Russian—1328
LMArena Spanish—1313

Instruction Following Not comparable

DeepSeek-V3.2-Speciale: —, Gemini 2.0 Flash-Lite: 70.6 (#158)

BenchmarkDeepSeek-V3.2-SpecialeGemini 2.0 Flash-Lite
LiveBench Instruction Following—78.3%
IFEval—82.4%
LMArena Instruction Following—1305

Long Context Not comparable

DeepSeek-V3.2-Speciale: —, Gemini 2.0 Flash-Lite: 40.1 (#155)

BenchmarkDeepSeek-V3.2-SpecialeGemini 2.0 Flash-Lite
LMArena Longer Query—1321

Writing & Preference Gemini 2.0 Flash-Lite leads

DeepSeek-V3.2-Speciale: 46.1 (#217), Gemini 2.0 Flash-Lite: 51.9 (#172)

BenchmarkDeepSeek-V3.2-SpecialeGemini 2.0 Flash-Lite
LMArena Text—1330
LMArena Creative Writing—1319
EQ-Bench Creative Writing1276—
WildBench—79%
LMArena Multi-Turn—1307
LiveBench Language—34.3%

Frequently asked questions

Is DeepSeek-V3.2-Speciale better than Gemini 2.0 Flash-Lite?

DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.1 to 37.3 on the Noometry Index.

Is DeepSeek-V3.2-Speciale or Gemini 2.0 Flash-Lite better for coding?

DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 39.9 versus 37.4 in the Noometry coding category.

How many benchmarks do DeepSeek-V3.2-Speciale and Gemini 2.0 Flash-Lite share?

0 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Gemini 2.0 Flash-Lite has 32.

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