Try Bifrost Enterprise free for 14 days.
Request access
[ MODEL COMPARISON ]

Compare gemini-2.5-pro with other models

Select another model to compare pricing, limits, and capabilities with gemini-2.5-pro.

Google Gemini logo
VS
Models
Google Gemini logogemini-2.5-pro
gemini
Context Length
1049K
Max Output
66K
Input Cost
$1.25/M
Output Cost
$10.00/M
Mode
Chat
Max Input Tokens
1049K
Max Tokens
66K
Supported Endpoints
/v1/chat/completions, /v1/completions
Provider
Google Gemini
Tool Choice
Yes
Response Schema
Yes
Prompt Caching
Yes
System Messages
Yes
[ WE'RE OPEN SOURCE ]

Scale with the Fastest LLM Gateway

Built for enterprise-grade reliability, governance, and scale. Deploy in seconds.

Comparison Insights

Comprehensive analysis based on the latest model metadata from the comparison table above.

What should I know about gemini-2.5-pro?

Overview

  • gemini-2.5-pro is a chat model provided by Google Gemini.
  • This model offers an exceptional context window of 1049K tokens, making it ideal for processing extensive documents, long conversations, or large codebases.

Pricing

  • Input processing costs $1.25 per million tokens.
  • Output generation costs $10.00 per million tokens.

Output Capabilities

  • The model can generate up to 66K tokens in a single response.

Availability

  • Available through the following endpoints: /v1/chat/completions, /v1/completions.
What capabilities does gemini-2.5-pro support?
  • Supports function calling, enabling integration with external tools and APIs for extended functionality.
  • Includes vision capabilities to process and analyze images alongside text inputs.
  • Features advanced reasoning capabilities for complex problem-solving and multi-step logical tasks.
  • Provides web search integration for accessing real-time information and current data.
  • Accepts audio input, allowing for voice-based interactions and audio processing.
  • Allows explicit tool selection, giving developers fine-grained control over function execution.
  • Supports structured response schemas for consistent, predictable output formatting.
  • Implements prompt caching to reduce costs and latency for repeated or similar queries.
  • Supports system messages for customizing model behavior and setting operational parameters.