GoogleGemini 2.5 Pro
Gemini 2.5 Pro, developed by Google, is a chat model that excels in mathematical reasoning and coding tasks, as evidenced by its high scores in the Math Index, Coding Index, and various math and coding benchmarks. Notably, it achieves top 25% or better performance in several benchmarks, including MMLU-Pro, GPQA, and MATH-500. A key technical trait of Gemini 2.5 Pro is its large context window of 1,048,576 tokens, allowing it to process and understand extensive inputs.
Input
Output
Context
1M
Max Output
66K
Parameters
-
Technical Specifications
Input Modalities
Output Modalities
Capabilities
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Benchmarks
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Frequently asked questions about Gemini 2.5 Pro
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Gemini 2.5 Pro advertises a context window of 1,048,576 tokens, with a maximum output of 65,536 tokens in a single response. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. Gemini 2.5 Pro is an open-weight model released under the Apache 2.0 license, so the weights can be downloaded and self-hosted within that license's terms.
Gemini 2.5 Pro accepts Text, Image, Audio and Video and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
Gemini 2.5 Pro has published results from Artificial Analysis, shown by suite in the benchmarks card above exactly as the publisher reported them. Scores are not combined across suites, and a suite that has not measured Gemini 2.5 Pro is shown as not available rather than estimated.
Not on the managed pool today; Gemini 2.5 Pro is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
Google has not published a parameter count for Gemini 2.5 Pro, so the catalog cannot estimate its memory footprint. The capacity planner can size it from a parameter count you supply.
Fields collected from public registries, provider APIs and benchmark publishers, each tagged with its source.
Last updated: Nov 6, 2026
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