Roboflow

Gemini 2.5 Flash-Lite vs Gemini 3 Pro

Compare Gemini 2.5 Flash-Lite and Gemini 3 Pro side-by-side. See how these vision models stack up in Image Captioning, Object Detection, OCR, Open Prompt, and Classification.

Compare Gemini 2.5 Flash-Lite vs Gemini 3 Pro live

Run the same image across every model that supports a task and compare their outputs side-by-side.

Detect and compare bounding boxes across models on the same image.

Open Object Detection in the full playground
GoogleGemini 2.5 Flash-Lite
Run to compare this model.
GoogleGemini 3 Pro

Gemini 3 Pro is deprecated and can no longer be run. Details and evals are still available on its model page.

Models in this comparison

Gemini 2.5 Flash-Lite vs Gemini 3 Pro: Overview

Gemini 2.5 Flash-Lite

Gemini 2.5 Flash-Lite, released for general availability on July 22, 2025, is the most cost-efficient model in the Gemini 2.5 family, designed for high-volume and latency-sensitive tasks. It is multimodal, supporting text, images, video, audio, and PDFs as inputs, with text as its primary output. The model handles up to 1 million input tokens and generates outputs up to 64K tokens, making it suitable for large-scale document or media processing at low cost. It is built on a Sparse Mixture-of-Experts architecture with native multimodal support, though exact parameter counts are undisclosed.

Flash-Lite offers the lowest usage cost among Gemini 2.5 models. It introduces developer controls for “thinking mode,” allowing fine-tuning of reasoning depth vs. efficiency. It also integrates native tools such as code execution, search grounding, and URL context. While strong on translation, classification, coding, and general multimodal reasoning, it lacks support for image or audio generation in its stable release and is less capable than Gemini 2.5 Flash or Pro on complex reasoning-heavy workflows.

Gemini 3 Pro

Gemini 3 Pro is Google DeepMind’s flagship multimodal frontier model, built for high-accuracy reasoning and large-scale context understanding across text, images, audio, video, code, and documents. It delivers major gains over Gemini 2.5 Pro, supported by a 1M-token window and strong performance on Google-reported benchmarks such as GPQA Diamond, MMMU-Pro, and Video-MMMU.

The model excels at structured outputs, tool use, and agentic coding, enabling complex multi-step workflows and analysis of entire books, codebases, or long videos in a single prompt. Positioned as Google’s top production model, it balances advanced reasoning with broad multimodal capabilities, making it well suited for research assistants, automation agents, coding systems, and enterprise-scale document and media analysis.

Gemini 2.5 Flash-Lite vs Gemini 3 Pro Comparison Table

PropertyGemini 2.5 Flash-LiteGemini 3 Pro
OrganizationGoogleGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2025Nov 2025
Context Window1.0M1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.100
Output $/1M$0.400
Vision Tasks
CaptioningDemo
ClassificationDemo
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
53.73%
Avg Response Time7.19s
Median input tokensincl. image tokens294
Median output tokens6
Est. cost / taskon this benchmark<$0.0001
Defect Detection
66.7%(10/15)
Document Understanding
66.7%(6/9)
Object Counting
10%(1/10)
Object Understanding
71.4%(10/14)
Spatial Understanding
47.4%(9/19)
OCR
Overall Score
77.73%
Avg Response Time7.45s
Median input tokensincl. image tokens290
Median output tokens12
Est. cost / taskon this benchmark<$0.0001
Focused Scene OCR
75.8%(75/99)
Handwritten Math
70%(7/10)
License Plate Recognition
90%(27/30)
Text Recognition
80%(24/30)
VQA & Extraction
75%(45/60)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology