Gemini 2.5 Pro vs Flash vs Nano: status & differences
Quick answer
Choose Gemini 2.5 Pro for cloud tasks where reasoning quality matters most, and Gemini 2.5 Flash when balancing response speed, cost, and capability. Gemini Nano serves a different purpose: supported on-device Android AI features. Nano is not a third Gemini 2.5 cloud pricing tier. For a new integration, also compare currently supported Gemini releases rather than assuming 2.5 is the latest.
Pro, Flash, and Nano compared
Choice | Where it runs | Useful starting point | What to check |
|---|---|---|---|
Google's cloud services | Complex reasoning, coding, and analysis | Current model lifecycle, API cost, supported inputs, and results on your task | |
Google's cloud services | Summarization, extraction, and interactive workflows with cost or latency constraints | Quality at your chosen settings, rate limits, and current alternatives | |
Supported Android devices through AICore | Local summarization, rewriting, image description, and other supported device features | Device compatibility, the specific ML Kit API, model availability, and resource limits |
Sources checked September 9, 2026: Google's Gemini model catalog, model lifecycle schedule, and Android's Gemini Nano documentation. These are workflow distinctions, not measured quality rankings.
Lifecycle note (checked September 13, 2026): Google lists the stable Gemini 2.5 Pro, Flash and Flash-Lite endpoints without an announced shutdown date. Newer Gemini 3 models are available, while preview endpoints follow separate deprecation dates. Check the current model catalog and deprecation schedule before choosing a production endpoint.
When should you use Pro or Flash?
Start with the least expensive supported cloud model that meets your quality requirements. For a document workflow, compare whether the output preserves the original facts, cites the correct passages, and follows your required format. Try a more capable model when the cheaper option fails those requirements.
For example, evaluate a short summary, an extraction table, and a question that requires combining information from several sections of the same document. Keep the inputs and scoring criteria identical. Record errors and latency instead of assuming that one model wins every task. This is a suggested evaluation, not a report of tests performed for this article.
Multimodal input is not exclusive to Pro. Check the exact model and endpoint for supported text, image, audio, or video inputs. Accepting a video for analysis does not mean that model generates video. Google's image-generation and Veo video-generation offerings have their own model IDs and capabilities.
Need to generate video?
Gemini 2.5 Pro and Flash can analyze video, but their API outputs text. Use a video-generation model when the deliverable is a new clip.
Compare video modelsWhen does Nano make sense?
Gemini Nano is useful when an Android feature should perform supported inference on the device. Android exposes capabilities through AICore and ML Kit's GenAI APIs, including summarization, proofreading, rewriting, image description, speech recognition, and a prompt API. Availability depends on the device and API.
On-device inference can work without a network connection once the required model and setup are available. Do not assume every Android phone or watch supports it, that downloads need no connection, or that every task completes instantly. Check compatibility and measure performance on the devices your users actually have.
How should you compare cost?
A consumer Gemini subscription, a cloud API bill, and an on-device feature are different purchases. For cloud use, calculate input and output tokens, thinking or caching charges where applicable, retries, and other endpoint-specific costs using Google's API pricing. Do not use a consumer subscription price as an API quote.
Nano does not map to a Pro-versus-Flash cloud token price. Its practical constraints include device support, memory, model availability, and feature limits. For any option, confirm the current lifecycle before committing a production integration.
For the current endpoints and retirement timeline, read the Gemini 3 model migration guide.
Frequently asked questions
Start with the least expensive supported cloud model that meets your quality requirements. For a document workflow, compare whether the output preserves the original facts, cites the correct passages, and follows your required format. Try a more capable model when the cheaper option fails those requirements.
For example, evaluate a short summary, an extraction table, and a question that requires combining information from several sections of the same document. Keep the inputs and scoring criteria identical. Record errors and latency instead of assuming that one model wins every task. This is a suggested evaluation, not a report of tests performed for this article.
Gemini Nano is useful when an Android feature should perform supported inference on the device. Android exposes capabilities through AICore and ML Kit's GenAI APIs, including summarization, proofreading, rewriting, image description, speech recognition, and a prompt API. Availability depends on the device and API.
On-device inference can work without a network connection once the required model and setup are available. Do not assume every Android phone or watch supports it, that downloads need no connection, or that every task completes instantly. Check compatibility and measure performance on the devices your users actually have.
A consumer Gemini subscription, a cloud API bill, and an on-device feature are different purchases. For cloud use, calculate input and output tokens, thinking or caching charges where applicable, retries, and other endpoint-specific costs using Google's API pricing. Do not use a consumer subscription price as an API quote.
Nano does not map to a Pro-versus-Flash cloud token price. Its practical constraints include device support, memory, model availability, and feature limits. For any option, confirm the current lifecycle before committing a production integration.
