Gemini 2.5 Flash vs Gemini 2.5 Flash-Lite
Compare Gemini 2.5 Flash and Gemini 2.5 Flash-Lite side-by-side. See how these vision models stack up in Open Prompt, OCR, Classification, Image Captioning, and Object Detection.
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Models in this comparison
Gemini 2.5 Flash vs Gemini 2.5 Flash-Lite: Overview
Gemini 2.5 Flash, released on June 17, 2025, is Google DeepMind’s production-ready, efficiency-focused model in the Gemini 2.5 family. It is multimodal, accepting text, images, video, and audio as inputs, with text as the primary output format. The model supports 1 million input tokens and up to 65K output tokens, enabling it to process very large contexts such as books, long video transcripts, or extensive datasets. Its training knowledge extends to January 2025.
Designed as a price-performance leader, Gemini 2.5 Flash balances speed and reasoning power, making it suitable for everyday enterprise and developer use cases without the higher latency and cost of Pro models. It supports advanced workflows like function calling, code execution, search grounding, URL context ingestion, and structured outputs. While efficient and scalable, output length is still limited compared to its input capacity, and multimodal outputs (e.g. image or audio generation) remain restricted to specialized or preview variants.
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 2.5 Flash vs Gemini 2.5 Flash-Lite Comparison Table
| Property | Gemini 2.5 Flash | Gemini 2.5 Flash-Lite |
|---|---|---|
| Organization | ||
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2025 | Jul 2025 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.300 | $0.100 |
| Output $/1M | $2.50 | $0.400 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| 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 | 55.22% | 53.73% |
| Avg Response Time | 24.91s | 7.19s |
| Median input tokensincl. image tokens | 294 | 294 |
| Median output tokens | 171 | 6 |
| Est. cost / taskon this benchmark | $0.0005 | <$0.0001 |
| Defect Detection | 60%(9/15) | 66.7%(10/15) |
| Document Understanding | 88.9%(8/9) | 66.7%(6/9) |
| Object Counting | 0%(0/10) | 10%(1/10) |
| Object Understanding | 71.4%(10/14) | 71.4%(10/14) |
| Spatial Understanding | 52.6%(10/19) | 47.4%(9/19) |
| OCR | ||
| Overall Score | 79.04% | 77.73% |
| Avg Response Time | 2.39s | 7.45s |
| Median input tokensincl. image tokens | 290 | 290 |
| Median output tokens | 81 | 12 |
| Est. cost / taskon this benchmark | $0.0003 | <$0.0001 |
| Focused Scene OCR | 79.8%(79/99) | 75.8%(75/99) |
| Handwritten Math | 80%(8/10) | 70%(7/10) |
| License Plate Recognition | 90%(27/30) | 90%(27/30) |
| Text Recognition | 80%(24/30) | 80%(24/30) |
| VQA & Extraction | 71.7%(43/60) | 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