ai-token-counter 1.0.0

Accurate token counting for multiple LLM providers

AI Token Counter

Accurate token counting for multiple LLM providers.

Features

• Multi-Provider Support: OpenAI (GPT-4, GPT-3.5, GPT-4o, o1), Anthropic (Claude 3, 3.5, 3.7), Google (Gemini), Meta (Llama 2, 3, 3.1, 3.2, 3.3), Mistral, DeepSeek, Cohere
• Cost Estimation: Built-in pricing for accurate cost calculations
• Message Support: Count tokens in chat messages with proper overhead
• Simple API: Easy-to-use functions for quick counting

Installation

pip install ai-token-counter

Quick Start

from ai_token_counter import count_tokens, estimate_cost

# Count tokens in text
count = count_tokens("Hello, world!", model="gpt-4o")
print(f"Tokens: {count}")

# Estimate cost
cost = estimate_cost(input_tokens=1000, output_tokens=500, model="gpt-4o")
print(f"Cost: ${cost.total_cost_usd:.6f}")

# Count chat messages
messages = [
    {"role": "user", "content": "Hello!"},
    {"role": "assistant", "content": "Hi there!"}
]
from ai_token_counter import count_message_tokens
result = count_message_tokens(messages, model="claude-3-5-sonnet")
print(f"Message tokens: {result.count}")

Supported Models

OpenAI

• gpt-4o, gpt-4o-mini, gpt-4-turbo, gpt-4, gpt-3.5-turbo
• o1-preview, o1-mini

Anthropic

• claude-3-7-sonnet, claude-3-5-sonnet, claude-3-5-haiku
• claude-3-opus, claude-3-sonnet, claude-3-haiku

Google

• gemini-2.0-flash-exp, gemini-1.5-pro, gemini-1.5-flash
• gemini-1.0-pro

Meta

• llama-3.3-70b, llama-3.1-405b, llama-3.1-70b
• llama-3-70b, llama-3-8b, llama-2-70b

Mistral

• mistral-large, mistral-medium, mistral-small
• mixtral-8x7b, mixtral-8x22b, codestral

DeepSeek

• deepseek-chat, deepseek-coder, deepseek-reasoner

Cohere

• command-r-plus, command-r, command

API Reference

TokenCounter Class

from ai_token_counter import TokenCounter

counter = TokenCounter(model="gpt-4o")

# Count text
result = counter.count("Your text here")
print(result.count)  # Token count
print(result.provider)  # Provider
print(result.estimated)  # Whether estimated

# Count messages
messages = [{"role": "user", "content": "Hello"}]
result = counter.count(messages=messages)

# Estimate cost
cost = counter.estimate_cost(input_tokens=1000, output_tokens=500)
print(cost.total_cost_usd)

# Get pricing info
pricing = counter.get_pricing_info("gpt-4o")

Convenience Functions

from ai_token_counter import (
    count_tokens,
    count_string_tokens,
    count_message_tokens,
    estimate_cost,
    count_conversation_tokens
)

# Quick count
count = count_tokens("text", model="gpt-4o")

# Full result
result = count_string_tokens("text", model="claude-3-opus")

# Messages
result = count_message_tokens(messages, model="gemini-1.5-pro")

# Cost
cost = estimate_cost(1000, 500, model="llama-3-70b")

# Conversation breakdown
breakdown = count_conversation_tokens(messages)
for msg in breakdown["messages"]:
    print(f"Message {msg['index']} ({msg['role']}): {msg['tokens']} tokens")
print(f"Total: {breakdown['total_tokens']} tokens")

License

MIT License - see LICENSE file for details.