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Google: Gemini 2.5 FlashSpeed meets intelligence

Try Google: Gemini 2.5 FlashAPI Documentation

Efficient reasoning. Massive context.

Dynamic Reasoning

Controllable thinking budget

Automatically adjusts processing time based on query complexity for optimal speed-accuracy balance.

Massive Context

1M token window

Process 3,000 images, 8.5 hours of audio, entire codebases, and long documents in single requests.

Cost Efficient

20-30% fewer tokens

Reduced verbosity and optimized output generation lower inference costs without sacrificing quality.

Examples

See what Google: Gemini 2.5 Flash can create

Copy any prompt below and try it yourself in the playground.

Code analysis

“Analyze this Python repository for performance bottlenecks. Review the main modules, identify inefficient patterns, and suggest optimizations with code examples.”

Document summarization

“Summarize the key findings, methodology, and conclusions from this 50-page research paper in 500 words.”

Multi-image reasoning

“Compare these three architectural photographs. Identify design patterns, materials, and stylistic differences across the images.”

Audio transcription

“Transcribe this 2-hour business meeting audio, extract action items, and identify key decisions made.”

For Developers

A few lines of code.
Fast reasoning. One API.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
API Documentation 
import requests


response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about Google: Gemini 2.5 Flash

Read the docs

Google Gemini 2.5 Flash is a multimodal LLM optimized for speed and cost-efficiency, featuring a 1M token context window and dynamic thinking capabilities. It handles text, code, images, audio, and video inputs while maintaining competitive pricing and low latency.

The model automatically adjusts its reasoning budget based on query complexity, enabling faster answers for simple requests and deeper analysis for complex problems. You can also manually control the thinking budget for fine-grained speed-accuracy tuning.

Maximum 3,000 images per prompt (up to 7 MB each via console, 30 MB from Cloud Storage), 8.5 hours of audio, and video files. Text and code inputs scale within the 1M token context window.

Flash offers better agentic tool use, improved multimodal capabilities, and 20-30% lower token costs than previous versions. It balances speed and intelligence better than Flash-Lite while remaining significantly cheaper than Pro models.

The model's training data extends through January 2025, ensuring recent information for most queries. For real-time data, use grounding with Google Search integration.

Yes, Gemini 2.5 Flash supports function calling, structured output, system instructions, and code execution. It also supports context caching for optimized performance on repeated requests.

Ready to create?

Start generating with Google: Gemini 2.5 Flash on ModelsLab.

Try Google: Gemini 2.5 FlashAPI Documentation