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Compare gemini-2.0-flash-001 with other models

Select another model to compare pricing, limits, and capabilities with gemini-2.0-flash-001.

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Models
Vertex AI Language Models logogemini-2.0-flash-001
vertex_ai-language-models
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Context Length
1049K
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Max Output
8K
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Input Cost
$0.15/M
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Output Cost
$0.60/M
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Mode
Chat
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Max Input Tokens
1049K
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Max Tokens
8K
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Provider
Vertex AI Language Models
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Tool Choice
Yes
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Response Schema
Yes
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Parallel Function Calling
Yes
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Prompt Caching
Yes
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System Messages
Yes
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Deprecation Date
2026-06-01
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Comparison Insights

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

What should I know about gemini-2.0-flash-001?

Overview

  • gemini-2.0-flash-001 is a chat model provided by Vertex AI Language Models.
  • 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 $0.15 per million tokens.
  • Output generation costs $0.60 per million tokens.

Output Capabilities

  • The model can generate up to 8K tokens in a single response.

Availability

  • Please note: This model is scheduled for deprecation on 2026-06-01.
What capabilities does gemini-2.0-flash-001 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.
  • Provides web search integration for accessing real-time information and current data.
  • Generates audio output for text-to-speech and voice response applications.
  • Allows explicit tool selection, giving developers fine-grained control over function execution.
  • Supports structured response schemas for consistent, predictable output formatting.
  • Enables parallel function calling to execute multiple operations simultaneously for improved efficiency.
  • Implements prompt caching to reduce costs and latency for repeated or similar queries.
  • Supports system messages for customizing model behavior and setting operational parameters.