Gemini 2.5 Pro
State-of-the-art thinking model, capable of reasoning over complex problems in code, math, and STEM, as well as analyzing large datasets, codebases, and documents using long context.
Google Deep reasoning across code, math, and documents
Gemini 2.5 Pro is a thinking model developed by Google DeepMind, designed to reason through complex problems rather than simply predict outputs. It is built to analyze information, draw logical conclusions, and incorporate contextual nuance across tasks in code, mathematics, and STEM. The model supports native multimodality, meaning it can process text, images, audio, video, and code repositories within a single context.
The model features a 1,048,576-token context window, making it suited for tasks that require processing large documents, entire codebases, or extended conversations. It scored 63.8% on the SWE-Bench Verified coding evaluation and is available through the Gemini API and Google AI Studio. It is best suited for developers and researchers working on complex reasoning tasks, long-document analysis, and advanced code generation.
What Gemini 2.5 Pro supports
Extended Context Window
Processes up to 1,048,576 tokens in a single context, enabling analysis of large documents, codebases, or long conversation histories without truncation.
Structured Reasoning
Uses a thinking approach to work through multi-step problems, drawing logical conclusions before producing a final response rather than generating output directly.
Multimodal Input
Accepts text, images, audio, video, and code as input within the same request, allowing mixed-media tasks to be handled in a single call.
Tool Use
Supports function calling and external tool integration, allowing the model to invoke defined tools and return structured results as part of a response.
Code Generation
Generates and debugs code across languages, achieving a 63.8% score on the SWE-Bench Verified benchmark for real-world software engineering tasks.
Math and STEM Analysis
Handles complex mathematical reasoning and science problems, with benchmark performance cited across math and science evaluation suites.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MMLU-Pro | Expert knowledge across 14 academic disciplines | 86.2% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 84.4% |
| MATH-500 | Undergraduate and competition-level math problems | 96.7% |
| AIME 2024 | American math olympiad problems | 88.7% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 80.1% |
| HLE | Questions that challenge frontier models across many domains | 21.1% |
| SciCode | Scientific research coding and numerical methods | 42.8% |
Common questions about Gemini 2.5 Pro
What is the context window size for Gemini 2.5 Pro?
Gemini 2.5 Pro has a context window of 1,048,576 tokens, which allows it to process large documents, long codebases, or extended conversations in a single request.
What is the knowledge cutoff date for Gemini 2.5 Pro?
Based on the model metadata, the training data cutoff is June 2025.
What input types does Gemini 2.5 Pro support?
Gemini 2.5 Pro supports multimodal inputs including text, images, audio, video, and code. On MindStudio, it also accepts select, number, and tools input types.
What tasks is Gemini 2.5 Pro best suited for?
The model is designed for complex reasoning tasks including advanced code generation, mathematical problem solving, STEM analysis, and processing large documents or codebases using its long context window.
Does Gemini 2.5 Pro support function calling and tool use?
Yes, Gemini 2.5 Pro supports tool use and function calling, allowing it to integrate with external tools and return structured outputs as part of its responses.
What people think about Gemini 2.5 Pro
Community sentiment around Gemini 2.5 Pro is largely positive, with users frequently highlighting its coding capabilities, including examples of it generating a functional Minecraft clone from a simple prompt. Threads on r/singularity received hundreds of upvotes, reflecting broad interest in its practical creative and engineering applications.
Some discussion centers on token efficiency, with a later benchmark noting that a subsequent model completed an agentic task using 50% fewer tokens than Gemini 2.5 Pro. Users also noted its availability to free-tier users as a notable accessibility development.
Google just dropped a new Agentic Benchmark: Gemini 3 Pro beat Pokémon Crystal (defeating Red) using 50% fewer tokens than Gemini 2.5 Pro.
Google's latest model, Gemini 2.5 Pro is Amazing! It created this Awesome Minecraft clone!
Google is surprisingly rolling out Gemini 2.5 Pro (exp) to free users
Parameters & options
Must be less than Max Response Size
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