Model ComparisonGoogle

Gemini 2.5 Pro

Gemini 2.5 Pro — Google's flagship model with a 2M token context window.

VisionTool usePrompt cachingReasoningStandard
Pricing (verified 2026-08-19)
TypePrice / 1M tokensNotes
Input$1.25Per 1M input tokens
Output$10.00Per 1M output tokens
Cache read$0.31Cached prefix read-back
Source: Google official pricing page · Last verified 2026-08-19
Context Window
2M tokens
1,500K words · 6 pages of text

At $1.25/M input and $10/M output, Gemini 2.5 Pro offers frontier capability with the longest standard context window of any major model.

Best for

  • Long-document analysis (entire codebases, books)
  • Video understanding
  • Tasks requiring deep contextual reasoning
  • Multimodal workflows

Not ideal for

  • Simple high-volume inference — Flash is 4× cheaper
  • Workloads where Anthropic's ecosystem is required

Real cost scenarios

ScenarioEst. monthly costBreakdown
Full codebase review — 500k token codebase$3,750$625 input + $3,125 output per review at scale
Long-form document Q&A — 10k queries/day$4,87550k context per query

Estimates with typical token distributions. Use the Prompt Cost Calculator for your exact workload.

How to cut costs on Gemini 2.5 Pro

  1. Use Gemini's context caching — charged per token-hour, effective for static documents queried multiple times.
  2. Route simple queries to Gemini 2.5 Flash — same 2M context at 15× lower price.

Calculate your Gemini 2.5 Pro costs

Prompt Cost Calculator
Exact cost for your prompt
Agent Cost Calculator
Multi-step agent loops
Prompt Caching Calculator
Caching savings
Conversation Cost
Chat history strategies

Other Google models

Gemini 3 Flash$0.50/MGemini 2.5 Flash$0.30/MGemini 2.5 Flash-Lite$0.10/M

Compare Gemini 2.5 Pro

claude opus 5 vs gemini 3 progemini 3 pro vs gpt 5 4gemini 3 flash vs gemini 3 pro

Frequently Asked Questions

What makes the 2M context window useful?

Most LLMs require chunking documents that don't fit in context, which loses cross-document relationships. With 2M tokens, you can load entire codebases (average production codebase: 200-500K tokens), full legal cases, or multi-book research corpora in a single context window without retrieval.

How does Google's context caching work vs Anthropic's?

Google charges per token-hour for cached content (ongoing storage cost), while Anthropic charges a write fee then a lower per-call read fee. For documents queried many times per hour: Google's approach can be cheaper. For documents with lower query frequency: Anthropic's model is simpler. Calculate based on your query frequency.

Pricing verified 2026-08-19 from Google official pricing. All prices in USD per 1M tokens. See methodology and price changelog.