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Function Calling Token Calculator

Calculate how many tokens your function/tool definitions consume in the LLM context. Paste a schema or build one, then compare overhead across models.

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Valid JSON schema

Tokens / Tool

89

Total Tool Overhead

445

ModelContext WindowTokens / ToolTotal (5 tools)% of ContextRemaining
GPT-4o128,0001015050.39%127,495
GPT-4.11,047,5761015050.05%1,047,071
GPT-4o Mini128,0001015050.39%127,495
Claude 4 Sonnet200,0001075350.27%199,465
Claude 3.5 Haiku200,0001075350.27%199,465
Gemini 2.5 Pro1,048,576994950.05%1,048,081
Gemini 2.0 Flash1,048,576994950.05%1,048,081

Tips to Reduce Tool Token Overhead

  • Keep descriptions concise -- every character counts toward tokens.
  • Use short, descriptive parameter names instead of verbose ones.
  • Remove optional parameters that are rarely used.
  • Combine related tools into a single tool with a mode/action parameter.
  • Use enums sparingly -- each enum value adds tokens.
  • Avoid deeply nested object parameters; flatten when possible.
  • Only send the tools relevant to the current conversation turn.

Token counts are estimated using character-based approximation (~4 chars/token for JSON). Actual token counts vary by model tokenizer. Each provider adds different overhead per tool definition (system prompt formatting, delimiters, etc.). Results are approximate.

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How to Use Function Calling Token Calculator

  1. 1

    Input your schema

    Paste a JSON tool/function schema or use the builder form to create one with parameters.

  2. 2

    Set tool count

    Enter how many tools you define in a typical API call.

  3. 3

    Compare across models

    See tokens per tool, total overhead, and percentage of context window used for GPT-4o, Claude, and Gemini.

  4. 4

    Optimize

    Use the tips section to reduce your tool definition token count and save on costs.

Frequently Asked Questions

Tool/function schemas are serialized into the system prompt by each provider. We estimate tokens using character-based approximation (~4 chars per token for JSON). Each provider adds a different amount of overhead per tool definition (delimiters, formatting).

Each tool definition includes the name, description, parameter names, types, descriptions, and structural JSON tokens. With 10+ tools, this can consume 5-15% of your context window, leaving less room for conversation.

Write concise descriptions, use short parameter names, remove rarely-used optional parameters, combine related tools, and only send tools relevant to the current turn.

Yes. OpenAI, Anthropic, and Google each format tool definitions differently in the context. OpenAI and Anthropic tend to use more overhead tokens per tool than Google. The exact tokenization varies by model.

The builder generates a standard JSON function schema compatible with OpenAI's function calling format. For Anthropic and Google, the format differs slightly but the token count estimate remains useful for comparison.