Source: MarkTechPost
Google has introduced Gemini 2.5 Flash, an early-preview AI model accessible via the Gemini API through Google AI Studio and Vertex AI. This model builds upon the foundation of Gemini 2.0 Flash, offering enhanced reasoning capabilities while maintaining a focus on speed and cost-efficiency.
Hybrid Reasoning with Adjustable Thinking Budgets
A key feature of Gemini 2.5 Flash is its hybrid reasoning capability, allowing developers to enable or disable the model’s “thinking” process. This process involves the model reasoning through its thoughts before generating a response, which can be beneficial for complex tasks requiring multiple steps of reasoning, such as solving math problems or analyzing research questions.
To provide flexibility, developers can set a “thinking budget” that controls the maximum number of tokens the model can generate during its thinking phase. A higher budget permits more extensive reasoning, potentially improving the quality of responses for complex prompts. Importantly, the model does not use the full budget if the prompt does not necessitate it, ensuring efficiency for simpler tasks.
Performance and Cost Considerations
Gemini 2.5 Flash maintains the fast speeds of its predecessor, Gemini 2.0 Flash, even when the thinking process is disabled. This design allows developers to optimize for latency and cost when high-level reasoning is unnecessary. By adjusting the thinking budget, developers can find the appropriate balance between response quality, cost, and latency to suit their specific application needs.
Integration and Accessibility
The model is currently available in preview through Google AI Studio and Vertex AI. Developers can experiment with Gemini 2.5 Flash by accessing it via the Gemini API, enabling them to build and test applications that leverage the model’s hybrid reasoning capabilities.
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Sana Hassan
Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.
