Can I run Gemini 2.5 Pro locally?

No. Google has not released the weights; it is API-only, so you access it through Google's infrastructure and cannot run it on your own hardware.

What does Gemini 2.5 Pro cost and how long is its context?

Input is $1.25 per million tokens and output is $10.00 per million tokens, with a context window of 1,048,576 tokens.

How is Gemini 2.5 Pro different from other Gemini models?

It is Google's top-tier model and adds a 'thinking' capability that reasons through a problem before producing a final answer, which the smaller or faster Gemini variants do not do.

Gemini 2.5 Pro

5952°

#llm #google #vision #audio #video #reasoning

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Google | 1,048,576 token context | $1.25/M input, $10.00/M output. Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy and nuanced context handling. Gemini 2.5 Pro achieves top-tier performance on multiple benchmarks, including first-place positioning on the LMArena leaderboard, reflecting superior human-preference alignment and complex problem-solving abilities.

Guides for this model

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Captain's notes

Written by Captain, the house editor

Notes from 2026-10-13

Gemini 2.5 Pro is Google's flagship reasoning model, built around a 'thinking' step that works through problems before committing to an answer. It handles a one-million-token context window and covers text, vision, audio, and video in a single model. It suits engineers, researchers, and anyone whose work demands careful multi-step reasoning rather than fast, shallow replies.

Known for

Built-in thinking step improves accuracy on complex, multi-stage problems.
One-million-token context: long codebases, documents, or transcripts fit in one call.
Strong across coding, mathematics, and scientific reasoning tasks.
Accepts text, images, audio, and video as input.
Top-ranked on the LMArena human-preference leaderboard at release.

Watch out for

Closed weights, API-only: you cannot run it locally or inspect the model.
Output costs $10 per million tokens, which adds up fast on long thinking traces.
No open licence; your usage is governed entirely by Google's API terms.

Best for

Prompts to try

Debug a tricky async race

I have a Node.js service where two async functions both write to the same database record. Occasionally the second write silently overwrites the first. Walk me through why this happens, show a minimal reproduction, then give me two different fixes with trade-offs for each.

Reason through a proof

Prove that the square root of 2 is irrational using a proof by contradiction. Then explain each logical step as if I understand algebra but have never seen a formal proof before. Finally, tell me where the argument would break down if we tried the same approach on the square root of 4.

Analyse a long document

I'm pasting in a 50-page research paper on transformer attention mechanisms. Identify the three central claims the authors make, note any assumptions they state explicitly versus ones they leave implicit, and flag any sections where the evidence feels thin or the methodology is unclear.

Compare with

Gemma 4 31B
Open-weights model
262,144 token context · $0.20/M input, $0.40/M output
Gemma 4 26B A4B
Open-weights model
262,144 token context · $0.09/M input, $0.30/M output
gemma 4 31B it turbo
Open-weights model
262,144 token context · $0.09/M input, $0.34/M output
Gemini 3.8 Flash
API-only model
1,048,576 token context · $0.83/M input, $4.12/M output

More text models from google

Questions people ask

Can I run Gemini 2.5 Pro locally?: No. Google has not released the weights; it is API-only, so you access it through Google's infrastructure and cannot run it on your own hardware.
What does Gemini 2.5 Pro cost and how long is its context?: Input is $1.25 per million tokens and output is $10.00 per million tokens, with a context window of 1,048,576 tokens.
How is Gemini 2.5 Pro different from other Gemini models?: It is Google's top-tier model and adds a 'thinking' capability that reasons through a problem before producing a final answer, which the smaller or faster Gemini variants do not do.

Captain's verdict
The million-token window and genuine thinking step make this Google's most capable model for hard reasoning work, if you can stomach the output price.

Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.

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Updated Oct 22, 2026

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How this score adds up

This model has no popularity data from any source yet, so it carries a stand-in number below 7,000° that keeps the lists complete. It never ranks above a model with data. The breakdown appears here as soon as a source reports it.

Glossary: the terms used here

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Download-equivalents: Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
Log scale: Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
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Adoption rate: How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
Measured and estimated: Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
Seeder: Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
AI Horde: A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
Generation sites: Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
OpenRouter tokens: A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
Full model (checkpoint): The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
LoRA and embedding: Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
Family and weights: The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
Age: Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
Family boost: A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
Stand-in score: A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
New! (grace period): A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
Trend: How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
Typical model: The middle of the catalog: half the models with a figure grew faster over the period, half slower.

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