Gemini 2.5 Flash vs Gemini 2.5 Pro
Compare Gemini 2.5 Flash and Gemini 2.5 Pro 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 Pro: 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 Pro, released on June 17, 2025, is Google DeepMind’s most capable model in the Gemini 2.5 family, optimized for deep reasoning, coding, and complex multimodal tasks. It accepts text, images, audio, video, and PDFs as input and outputs text. The model supports 1 million input tokens with an output capacity of up to 65K tokens, enabling large-scale comprehension of datasets, codebases, and technical documents. Its training knowledge extends to January 2025.
Pro outperforms earlier Gemini 2.0 models across benchmarks, including agentic coding tasks where it achieved ~63.8% on SWE-Bench Verified. It supports structured outputs, function calling, code execution, search grounding, and URL context, making it well-suited for enterprise, STEM, and developer workflows. However, it does not currently support image or audio generation in its stable release, and its higher computational cost and latency make it less efficient than Flash or Flash-Lite. It is available via the Gemini API, Google AI Studio, and Vertex AI.
Gemini 2.5 Flash vs Gemini 2.5 Pro Comparison Table
| Property | Gemini 2.5 Flash | Gemini 2.5 Pro |
|---|---|---|
| Organization | ||
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jul 2025 | Jun 2025 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.300 | $1.25 |
| Output $/1M | $2.50 | $10.00 |
| 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% | 70.15% |
| Avg Response Time | 24.91s | 11.87s |
| Median input tokensincl. image tokens | 294 | 294 |
| Median output tokens | 171 | 565 |
| Est. cost / taskon this benchmark | $0.0005 | $0.0060 |
| Defect Detection | 60%(9/15) | 73.3%(11/15) |
| Document Understanding | 88.9%(8/9) | 88.9%(8/9) |
| Object Counting | 0%(0/10) | 20%(2/10) |
| Object Understanding | 71.4%(10/14) | 78.6%(11/14) |
| Spatial Understanding | 52.6%(10/19) | 78.9%(15/19) |
| OCR | ||
| Overall Score | 79.04% | 78.6% |
| Avg Response Time | 2.39s | 4.91s |
| Median input tokensincl. image tokens | 290 | 290 |
| Median output tokens | 81 | 323 |
| Est. cost / taskon this benchmark | $0.0003 | $0.0036 |
| Focused Scene OCR | 79.8%(79/99) | 78.8%(78/99) |
| Handwritten Math | 80%(8/10) | 80%(8/10) |
| License Plate Recognition | 90%(27/30) | 90%(27/30) |
| Text Recognition | 80%(24/30) | 73.3%(22/30) |
| 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