Gemini 3.0 FlashvsGemini 2.5 Flash

Across 8 shared benchmarks, Gemini 3.0 Flash leads overall: Gemini 3.0 Flash wins 7, Gemini 2.5 Flash wins 0, with 1 ties and an average score difference of +18.93.

Google Deep Mind
Gemini 3.0 Flash

Google Deep Mind · 2025-12-17 · Chat model

Google Deep Mind
Gemini 2.5 Flash

Google Deep Mind · 2025-04-17 · Reasoning model

Gemini 3.0 Flash7 wins(88%)Ties1(0%)0 winsGemini 2.5 Flash

Benchmark scores

Grouped by capability, sorted by largest gap within each. 8 shared benchmarks.

General Knowledge

Gemini 3.0 Flash 3/3
BenchmarkGemini 3.0 FlashGemini 2.5 FlashDiff
HLE43.5040 / 16111131 / 161+32.50
LiveBench56.3579 / 115Normal (No Tools)47.74101 / 115Thinking High (No Tools)+8.61
GPQA Diamond90.4018 / 17982.8063 / 179+7.60

Math and Reasoning

Gemini 3.0 Flash 1/2
BenchmarkGemini 3.0 FlashGemini 2.5 FlashDiff
AIME202599.708 / 1067270 / 106+27.70
FrontierMath - Tier 44.2040 / 80Normal (No Tools)4.2040 / 80Normal (No Tools)

Claw-style Agent Evaluation

Gemini 3.0 Flash 1/1
BenchmarkGemini 3.0 FlashGemini 2.5 FlashDiff
Pinch Bench85.2016 / 37Thinking (With Tools)70.7031 / 37Thinking (With Tools)+14.50

Coding and Software Engineer

Gemini 3.0 Flash 1/1
BenchmarkGemini 3.0 FlashGemini 2.5 FlashDiff
SWE-bench Verified68.7062 / 1085090 / 108+18.70

Common Sense

Gemini 3.0 Flash 1/1
BenchmarkGemini 3.0 FlashGemini 2.5 FlashDiff
SimpleQA68.707 / 4526.9027 / 45+41.80

Specs

FieldGemini 3.0 FlashGemini 2.5 Flash
PublisherGoogle Deep MindGoogle Deep Mind
Release date2025-12-172025-04-17
Model typeChat modelReasoning model
ArchitectureDenseDense
ParametersNot availableNot available
Context length2000K1000K
Max output64K64K

Summary

  • Gemini 3.0 Flashleads in:General Knowledge (3/3), Math and Reasoning (1/2), Claw-style Agent Evaluation (1/1), Coding and Software Engineer (1/1), Common Sense (1/1)

On average across the 8 shared benchmarks, Gemini 3.0 Flash scores 18.93 higher.

Largest single-benchmark gap: SimpleQA — Gemini 3.0 Flash 68.70 vs Gemini 2.5 Flash 26.90 (+41.80).

Page generated from structured model, pricing and benchmark records. No real-time LLM is used to write the prose.