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Gemini 2.5 Pro
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About

Gemini 2.5 Pro is Google’s most capable AI model for 2025, built to solve complex problems across extremely large inputs and multiple media types. It combines embedded multi-step reasoning with multimodal understanding — text, images, audio, video and code — and can keep coherent context across up to 2 million tokens. That makes it uniquely suited to analyze entire codebases, long research papers, multi-document legal briefs, or multimedia datasets without losing important connections. For users, Gemini 2.5 Pro delivers practical benefits: accurate multi-turn debugging and large-scale code generation, deep synthesis of research from many sources, integrated interpretation of diagrams and associated text or audio, and nuanced decision support for scientific or business workflows. Its internal selection mechanism optimizes for speed and precision, so responses are fast while retaining high reliability. Benchmarks place it at the top for reasoning and generative tasks, and it’s accessible through Google Cloud Vertex AI and the Gemini API with options for scaled throughput. Considerations: full-feature access is offered primarily via Google AI Pro plans and may incur costs and quota limits; some advanced capabilities remain in experimental phases. For routine, low-latency tasks, lighter models may be more cost-effective. Overall, Gemini 2.5 Pro is ideal when you need robust, context-aware reasoning over vast multimodal data — from end-to-end codebase understanding and debugging to long-form research synthesis, multimedia content analysis, and other high‑complexity applications.

Percs

Multi-modal
Large context
High accuracy
Fast response

Settings

Temperature-  The temperature of the model. Higher values make the model more creative and lower values make it more focused.
Top P-  Tokens are selected from the most to least probable until the sum of their probabilities equals this value. Use a lower value for less random responses and a higher value for more random responses.
Top K-  For each token selection step, the top_k tokens with the highest probabilities are sampled. Then tokens are further filtered based on top_p with the final token selected using temperature sampling. Use a lower number for less random responses and a higher number for more random responses.
Context length-  The maximum number of tokens to use as input for a model.
Response length-  The maximum number of tokens to generate in the output.
Reasoning-  undefined