Gemini 2.5 Pro
Our state-of-the-art multipurpose model, which excels at coding and complex reasoning tasks.
About Gemini 2.5 Pro
Gemini 2.5 Pro earns the top spot on LMArena's overall leaderboard for one main reason: it thinks before it answers. Built-in reasoning — active by default — makes it exceptional at math, multi-step logic, and the kind of hard problems where other models guess and move on. A study of over 21,000 users ranked it first for reasoning above ChatGPT and Claude, and developers in particular keep returning to it for web work: it holds the number-one position on WebDev Arena for converting designs into polished, functional front-end code. Its 1 million token context window — with 94.5% accuracy at 128k tokens on long-document tasks — lets you feed in entire codebases or lengthy research papers and get coherent analysis back. The tradeoff is real and worth knowing: that same reasoning overhead means latency is high, with a 21-second median time to first token, and prompts over 100k tokens can sit for minutes before a response arrives. For async, depth-first work where quality matters more than instant feedback, it's hard to beat at its price point.
Best for
- Web development and UI design — ranked #1 on WebDev Arena for generating polished, functional front-end code from design files and mockups
- Complex math and algorithmic problem-solving — 88% on AIME 2024, strong for calculus, statistics, and reasoning-heavy tasks
- Long-document and codebase analysis — 1M token context fits roughly 30,000 lines of code or 1,500 pages of text in a single pass
- Video and multimodal understanding — can interpret design walkthrough videos and convert them to working React components
- Scientific research and literature synthesis — 83% on GPQA Diamond, well-suited for domain-specific analysis and research summarization
Specs & capabilities
How Gemini 2.5 Pro stacks up — intelligence, speed, context, and modalities.
Intelligence
Medium
Speed
Medium
Context window
1,048,576 tokens
Max output
65,536 tokens
Frequently asked questions
What does it cost?
Standard pricing is $1.25 per million input tokens and $10.00 per million output tokens for prompts under 200k tokens. Batch and Flex tiers cut those rates by 50% for non-real-time workloads. Prompts over 200k tokens cost more — $2.50 input and $15.00 output per million tokens.
How large is the context window?
1,048,576 tokens — roughly 1 million. Max output is 65,536 tokens (64k). It supports text, image, video, and audio as inputs, but produces text only.
Why is the response sometimes slow?
The model reasons through problems before responding, which adds latency. Median time to first token is around 21 seconds — much higher than typical models. Prompts exceeding 100k tokens can take 2 to 10 minutes or more. Once it starts generating, output speed is fast at around 144 tokens per second.
What is it not good at?
Structured output like JSON generation can be very slow, with reported timeouts exceeding 180 seconds. It also has a tendency to make unrequested changes to surrounding code when asked for a targeted edit, which frustrates developers expecting precise, scoped responses.
How does it compare to Gemini Flash?
Gemini 2.5 Pro is the flagship reasoning model in the family — deeper thinking, larger context, higher accuracy — but slower and more expensive. Flash is optimized for speed and cost, making it a better fit when low latency or high request volume matters more than maximum reasoning depth.
Who is this model best suited for?
Developers tackling complex front-end builds, researchers processing long documents, and anyone working on math-heavy or multi-step reasoning tasks where a slower, more deliberate response is acceptable. It is less suited for real-time applications or workflows where fast turnaround is critical.