Google: Gemini 2.5 Flash Preview
google/gemini-2.5-flash-preview
Access Gemini 2.5 Flash Preview from Google using the Puter.js AI API.
Get Started// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "google/gemini-2.5-flash-preview"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "google/gemini-2.5-flash-preview"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
Model Card
Gemini 2.5 Flash Preview is the April 2025 preview release (04-17) of Google's first fully hybrid reasoning model. Developers can turn thinking on or off per request and set a thinking budget in tokens to trade off quality, cost, and latency. With the budget at zero it runs at cost and latency comparable to Gemini 2.0 Flash while scoring higher on benchmarks.
In Google's reported results it scored 12.1% on Humanity's Last Exam, ahead of Claude 3.7 Sonnet (8.9%) and DeepSeek R1 (8.6%) but behind o4-mini (14.3%), plus 78.3% on GPQA Diamond and 78.0% on AIME 2025.
It accepts text, image, audio, and video input with a 1M token context window. It suits high-volume work like chat, summarization, and data extraction where reasoning depth needs to be controlled. As a preview endpoint it has since been superseded by the stable Gemini 2.5 Flash release.
Context Window 1M
tokens
Max Output 66K
tokens
Input Cost $0.23
per million tokens
Output Cost $1.88
per million tokens
Input text, image, audio, video
modalities
Tool Use Yes
Release Date Apr 17, 2025
Model Playground
Try Gemini 2.5 Flash Preview instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
More AI Models From Google
Gemini 3.8 Flash
Gemini 3.8 Flash is Google's workhorse Flash-tier model, released September 2, 2026, three weeks after Gemini 3.7 Flash. It's built for long-horizon software engineering, agentic workflows, and multi-step reasoning in professional domains. Google reports it outperforms 3.7 Flash and other frontier models on DeepSWE v1.1 for autonomous engineering tasks, on Vals Finance Agent V2, and on Harvey's Legal Agent Benchmark. It scores 54.9% on HLE-Verified, and completes more than three times as many tasks as 3.7 Flash in Google's long-running, document-heavy workflow evaluations. It accepts text, image, video, audio, and PDF input with a 1M token context window and 64K token output limit, supports function calling and iterative tool use, and has a March 2026 knowledge cutoff. Priced at $0.75 per million input tokens and $3.75 per million output tokens through 2026, it targets teams running coding agents or document-heavy enterprise workflows.
ChatGemini 3.7 Flash
Gemini 3.7 Flash is Google's workhorse Flash-tier model, released August 13, 2026, three weeks after Gemini 3.6 Flash. It's built for coding and agentic workflows, targeting software engineering, web development, and knowledge-dense domains like finance and law. Google reports gains over Gemini 3.6 Flash on several benchmarks. DeepSWE v1.1 rose from 49.0% to 65.3%, FrontierCode 1.1 from 34.4% to 43.6%, and AutomationBench from 17.0% to 30.4%. On FrontierCode 1.1 it scores above Claude Sonnet 5 (42.7%) and GPT-5.6 Terra (41.3%), though GPT-5.6 Terra edges it out on Terminal-bench 2.1 (87.4% vs 85.8%). It accepts text, image, video, audio, and PDF input with a 1M token context window and 64K token output limit. It supports function calling, search as a tool, and computer use, and has a March 2026 knowledge cutoff. It's priced at roughly half of Gemini 3.6 Flash's rate, fitting teams running coding agents or high-volume document processing.
ChatGemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite is Google's fastest and most cost-efficient model in the Gemini 3.5 series, built for high-throughput, low-latency workloads. It scores 54% on Terminal-Bench 2.1 and 72.2% on GDM-MRCR v2, up from 31% and 60.1% for Gemini 3.1 Flash-Lite. It also outperforms the larger Gemini 3 Flash on SWE-Bench Pro (54.2% vs. 49.6%) and OSWorld-Verified (74.0% vs. 65.1%), while generating output at roughly 350 tokens per second. It supports text, image, video, audio, and PDF input with a 1M token context window, configurable thinking levels, and function calling, including computer use as a built-in tool. It's suited for agentic search, document processing, and other high-volume tasks where throughput and cost matter more than maximum reasoning depth.
Frequently Asked Questions
You can access Gemini 2.5 Flash Preview by Google through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Gemini 2.5 Flash Preview is free to integrate using the Puter.js AI API. With the User-Pays Model, you can add AI to your app for $0, since users cover their own AI usage through their Puter account.
| Price per 1M tokens | |
|---|---|
| Input | $0.23 |
| Output | $1.88 |
Gemini 2.5 Flash Preview was created by Google and released on Apr 17, 2025.
Gemini 2.5 Flash Preview supports a context window of 1M tokens. For reference, that is roughly equivalent to 2,097 pages of text.
Gemini 2.5 Flash Preview can generate up to 66K tokens in a single response.
Gemini 2.5 Flash Preview accepts the following input types: text, image, audio, video. It produces: text.
Yes, Gemini 2.5 Flash Preview supports tool use (function calling), allowing it to interact with external tools, APIs, and data sources as part of its response flow.
Yes — the Gemini 2.5 Flash Preview API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Add Gemini 2.5 Flash Preview to your app for free
Developers can integrate Gemini 2.5 Flash Preview for free using the Puter.js AI API.
With the User-Pays Model, each user covers their own AI usage instead of the developer.