Gemini 2.5 Pro

Gemini 2.5 Pro

Quick answer: Gemini 2.5 Pro is Google's flagship multimodal model, released March 25, 2025. It scores 21.64% on Humanity's Last Exam (among the highest at launch), 73.6% on LiveCodeBench, and 79.0% on Arena Hard v2, with a 1M-token context window — the largest available in its class at launch. Priced at $1.25 input / $10 output per 1M tokens (up to 200K input), with long-context surcharge above 200K.

At a Glance

Where Gemini 2.5 Pro leads

• 1M-token context window — 5× larger than o3/o4-mini, enabling whole-codebase and long-document analysis
• 21.64% Humanity's Last Exam — top-tier expert knowledge and reasoning at launch
• 73.6% LiveCodeBench — competitive coding performance
• 79.0% Arena Hard v2 — strong instruction following
• Full multimodal capability: text, image, audio, and video input
• Thinking mode for enhanced reasoning on hard tasks

Where it lags

• Long-context surcharge: $2.50/$15 per 1M above 200K input tokens (2× standard rate)
• Closed API model with no open weights
• SHADE-Arena score of 14.8% — lower than comparable models on this safety evaluation
• AetherCode score of 32.7% — below the frontier on agentic web task performance

Best for: Long-document analysis, whole-codebase reasoning, multimodal workflows (video, audio, image), and tasks where the 1M context window is a significant advantage over 200K alternatives.

What Gemini 2.5 Pro Is

Gemini 2.5 Pro is Google's most capable model at its March 2025 launch, sitting at the top of the Gemini 2.5 family (alongside Gemini 2.5 Flash). It is a natively multimodal model, designed from the ground up to process text, images, audio, and video in a single unified model — not through separate vision encoders bolted onto a language model.

The model's 1M-token context window was a defining feature at launch, enabling workflows that were impractical with 200K-context models: analysing entire software repositories, processing multi-hour video transcripts, or synthesising hundreds of research papers in a single request. A native "thinking" mode, similar in concept to Claude's extended thinking and OpenAI's o-series, enables stronger performance on hard reasoning tasks at the cost of additional output tokens.

Gemini 2.5 Pro is accessed via Google AI Studio and the Gemini API (also available via Vertex AI for enterprise deployments). Its pricing model has a tiered structure: standard rates apply up to 200K input tokens per request, with a 2× surcharge for inputs above that threshold.

Specifications

Pricing

Pricing per Google AI pricing page as of July 2026 — verify current rates before production use.

Context Window

Gemini 2.5 Pro has a 1,000,000-token context window — roughly 750 pages of text in a single request. This is the primary capability advantage over 200K models like o3 and Claude 3.7 Sonnet. At standard pricing this applies to the first 200K input tokens; requests above 200K input are billed at 2× the standard input rate.

Public Benchmark Scores

Scores from Benchgen evaluations or attributed third-party sources. Figures depend on harness, effort setting, and tools — see the source for methodology.

Gemini 2.5 Pro vs Alternatives

Gemini 2.5 Pro's strongest case: highest HLE score (21.64%) among this group, 5× larger context window than o3/o4-mini, and strong multimodal coverage — at 8× lower cost than o3. Its main trade-off is the long-context pricing surcharge above 200K tokens, which makes it more expensive than its headline rate for very large inputs.

How Gemini 2.5 Pro Performs on Real Agent Tasks

Gemini 2.5 Pro's AetherCode score of 32.7% (agentic web task performance) and SHADE-Arena score of 14.8% reveal its relative weaknesses vs its general benchmark profile. It is a strong general-purpose model but underperforms on specialised agentic and cybersecurity tasks compared to models like GPT-5.6 Sol.

For agent builders, Gemini 2.5 Pro is the recommended choice when the 1M context window is a hard requirement — processing large codebases, legal documents, or multi-session conversation histories where 200K models need chunking. For coding-focused agents that fit within 200K context, o4-mini is competitive in performance at a lower headline price.

Use Gemini 2.5 Pro via API

import google.generativeai as genai

genai.configure(api_key="YOUR_API_KEY")

model = genai.GenerativeModel("gemini-2.5-pro")
response = model.generate_content(
    "Analyse this 500-page contract and summarise the key obligations...",
)
print(response.text)

Related Models & Benchmarks

• Compare: Gemini 2.5 Flash · o4-mini · Claude 3.7 Sonnet · Gemini 3 Pro
• Benchmarks: Humanity's Last Exam · LiveCodeBench · Arena Hard v2 · AetherCode

Frequently Asked Questions

Specs and scores sourced from Google's official Gemini 2.5 Pro announcement (March 25, 2025) and Benchgen evaluations; third-party benchmark scores attributed inline. Pricing cited to the Google AI pricing page. Last updated 2026-07-23.