Gemini 2.5 Flash
| Introduction: | Gemini 2.5 Flash is Google's latest large language model, now in preview, offering enhanced reasoning capabilities while prioritizing speed and cost efficiency for developers. |
| Recorded in: | 6/18/2025 |
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What is Gemini 2.5 Flash?
Gemini 2.5 Flash is an advanced large language model (LLM) developed by Google, designed for developers. It is a "thinking model" that can perform a reasoning process before generating a response, allowing it to better understand complex prompts, break down tasks, and plan more accurate and comprehensive answers. It builds upon the 2.0 Flash foundation, significantly upgrading reasoning while maintaining speed and cost efficiency, making it Google's most cost-efficient thinking model with a strong price-to-performance ratio.
How to use Gemini 2.5 Flash
Developers can start building with Gemini 2.5 Flash by accessing it in preview through the Gemini API, Google AI Studio, and Vertex AI. Users can control the model's reasoning process by setting a "thinking budget" via API parameters or sliders in Google AI Studio and Vertex AI, ranging from 0 to 24576 tokens, allowing them to balance quality, cost, and latency according to their specific use case. The model automatically adjusts its thinking duration based on perceived task complexity.
Gemini 2.5 Flash's core features
Enhanced reasoning capabilities for complex tasks
Hybrid reasoning model with ability to turn "thinking" on or off
Fine-grained control over thinking budget (0 to 24576 tokens)
Optimized for speed and cost efficiency, offering a strong price-to-performance ratio
Automatic adjustment of thinking duration based on perceived prompt complexity
Strong performance on complex reasoning benchmarks like Hard Prompts in LMArena
Accessible via Gemini API, Google AI Studio, and Vertex AI
Use cases of Gemini 2.5 Flash
Solving multi-step mathematical problems
Analyzing complex research questions
Creating detailed schedules with multiple constraints
Developing functions that require dependency resolution and operator precedence (e.g., spreadsheet cell evaluation)
Generating accurate and comprehensive answers for prompts requiring deep understanding
Optimizing AI model performance for specific quality, cost, and latency tradeoffs