Gemini 2.0 Flash Exp vs Gemini 2.5 Flash
Compare Gemini 2.0 Flash Exp and Gemini 2.5 Flash side-by-side. See how these vision models stack up in Classification, Image Captioning, OCR, Open Prompt, and Object Detection.
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Gemini 2.0 Flash Exp is deprecated and can no longer be run. Details and evals are still available on its model page.
Models in this comparison
Gemini 2.0 Flash Exp vs Gemini 2.5 Flash: Overview
Gemini 2.0 Flash, released by Google DeepMind on February 5, 2025, is the efficiency-focused successor to Gemini 1.5 Flash. It is a multimodal model that accepts text, code, images, audio, and video as inputs, though its stable GA release outputs text only (image and audio generation remain in preview). The model supports up to 1 million tokens of input context with an output cap of ~8K tokens, making it well-suited for analyzing large documents, transcripts, or media files. Its knowledge is current through August 2024.
Flash 2.0 is optimized for speed, scalability, and agentic workflows, offering fast response times, tool use, structured outputs, and function calling. While more cost-efficient than Pro variants, its trade-offs include shorter output lengths and less depth on reasoning-intensive tasks. Available through the Gemini API, Vertex AI, AI Studio, and Gemini apps, Gemini 2.0 Flash is positioned for real-time applications, enterprise assistants, and production-scale multimodal processing where efficiency and throughput are priorities.
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 2.0 Flash Exp vs Gemini 2.5 Flash Comparison Table
| Property | Gemini 2.0 Flash Exp | Gemini 2.5 Flash |
|---|---|---|
| Organization | ||
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2025 | Jul 2025 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.300 | |
| Output $/1M | $2.50 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Classification | Demo | |
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | 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% | |
| Avg Response Time | 24.91s | |
| Median input tokensincl. image tokens | 294 | |
| Median output tokens | 171 | |
| Est. cost / taskon this benchmark | $0.0005 | |
| Defect Detection | 60%(9/15) | |
| Document Understanding | 88.9%(8/9) | |
| Object Counting | 0%(0/10) | |
| Object Understanding | 71.4%(10/14) | |
| Spatial Understanding | 52.6%(10/19) | |
| OCR | ||
| Overall Score | 79.04% | |
| Avg Response Time | 2.39s | |
| Median input tokensincl. image tokens | 290 | |
| Median output tokens | 81 | |
| Est. cost / taskon this benchmark | $0.0003 | |
| Focused Scene OCR | 79.8%(79/99) | |
| Handwritten Math | 80%(8/10) | |
| License Plate Recognition | 90%(27/30) | |
| Text Recognition | 80%(24/30) | |
| VQA & Extraction | 71.7%(43/60) | |
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