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toksum

A comprehensive Python library for counting tokens across 300+ Large Language Models (LLMs) from 34+ providers.

PyPI version Python Support License: MIT

Features

  • 🎯 Production Ready v1.0.1: Comprehensive support for 300+ models across 34+ providers including OpenAI, Anthropic, Google, Meta, Mistral, Microsoft, Amazon, Nvidia, IBM, Salesforce, BigCode, Databricks, Voyage AI, and many more
  • Comprehensive Multi-LLM Support: Count tokens for 300+ models across 34 providers including OpenAI, Anthropic, Google, Meta, Mistral, Microsoft, Amazon, Nvidia, IBM, Salesforce, BigCode, Databricks, Voyage AI, and many more
  • Accurate Tokenization: Uses official tokenizers (tiktoken for OpenAI) and optimized approximations for all other providers
  • Chat Message Support: Count tokens in chat/conversation format with proper message overhead calculation
  • Cost Estimation: Estimate API costs based on token counts and current pricing
  • Easy to Use: Simple API with both functional and object-oriented interfaces
  • Well Tested: Comprehensive test suite with high coverage
  • Type Hints: Full type annotation support for better IDE experience
  • Global Model Coverage: Support for models optimized for Chinese, Russian, and other languages
  • Enterprise & Code Models: Specialized support for enterprise AI models and code generation models

Supported Models

OpenAI Models (49 models)

  • GPT-4 (all variants including gpt-4, gpt-4-32k, gpt-4-turbo, gpt-4o, gpt-4o-mini, etc.)
  • O1 Models (o1-preview, o1-mini, o1-preview-2024-09-12, o1-mini-2024-09-12)
  • NEW: Vision Models (gpt-4-vision, gpt-4-vision-preview-0409, gpt-4-vision-preview-1106)
  • GPT-3.5 Turbo (all variants including instruct)
  • Legacy models (text-davinci-003, text-davinci-002, gpt-3, etc.)
  • Embedding models (text-embedding-ada-002, text-embedding-3-small, text-embedding-3-large)

Anthropic Models (27 models)

  • Claude-3 (Opus, Sonnet, Haiku with full and short names)
  • Claude-3.5 (Sonnet, Haiku 3.5, Computer Use models)
  • Claude-2 (2.1, 2.0, NEW: 2.1-200k, 2.1-100k)
  • Claude-1 (legacy models including 1.3, 1.3-100k)
  • Claude Instant (all variants including NEW: instant-2, instant-2.0)

Google Models (16 models)

  • Gemini Pro, Gemini Pro Vision
  • Gemini 1.5 Pro, Gemini 1.5 Flash (including latest variants)
  • Gemini 2.0 (gemini-2.0-flash-exp, gemini-2.0-flash, gemini-exp-1206, gemini-exp-1121)
  • Gemini 1.0 Pro, Gemini 1.0 Pro Vision
  • Gemini Ultra
  • NEW: PaLM Models (palm-2, palm-2-chat, palm-2-codechat)

Meta Models (12 models)

  • LLaMA-2 (7B, 13B, 70B)
  • LLaMA-3 (8B, 70B)
  • LLaMA-3.1 (8B, 70B, 405B)
  • LLaMA-3.2 (1B, 3B)
  • LLaMA-3.3 (70B, 70B-instruct)

Mistral Models (10 models)

  • Mistral (7B, Large, Medium, Small, Tiny)
  • Mistral Large 2 (mistral-large-2, mistral-large-2407)
  • Mixtral (8x7B, 8x22B)
  • Legacy Mistral 8x7B

Cohere Models (9 models)

  • Command (standard, light, nightly)
  • Command-R (standard, plus, NEW: with 2024 variants)

xAI Models (4 models)

  • Grok (1, 1.5, 2, beta)

Alibaba Models (20 models)

  • Qwen-1.5 series (0.5B to 110B parameters)
  • Qwen-2 series (0.5B to 72B parameters)
  • NEW: Qwen-2.5 (qwen-2.5-72b, qwen-2.5-32b, qwen-2.5-14b, qwen-2.5-7b)
  • Qwen-VL (vision-language variants)

Baidu Models (8 models)

  • ERNIE (4.0, 3.5, 3.0, Speed, Lite, Tiny)
  • ERNIE Bot (standard and 4.0)

Huawei Models (5 models)

  • PanGu-α (2.6B, 13B, 200B)
  • PanGu-Coder (15B and base)

Yandex Models (4 models)

  • YaLM (100B, 200B)
  • YaGPT (1, 2)

Stability AI Models (7 models)

  • StableLM Alpha (3B, 7B base and tuned)
  • StableLM Zephyr (3B)

TII Models (6 models)

  • Falcon (7B, 40B, 180B with instruct and chat variants)

EleutherAI Models (12 models)

  • GPT-Neo (125M, 1.3B, 2.7B)
  • GPT-NeoX (20B)
  • Pythia (70M to 12B)

MosaicML/Databricks Models (8 models)

  • MPT (7B, 30B with chat and instruct variants)
  • DBRX (base and instruct)

Replit Models (3 models)

  • Replit Code (v1, v1.5, v2 - 3B parameters)

MiniMax Models (5 models)

  • ABAB (5.5 to 6.5 chat variants)

Aleph Alpha Models (4 models)

  • Luminous (Base, Extended, Supreme, Supreme Control)

DeepSeek Models (10 models)

  • DeepSeek-Coder (1.3B to 33B, instruct)
  • DeepSeek-VL (1.3B, 7B)
  • DeepSeek-LLM (7B, 67B)
  • NEW: DeepSeek V3 (deepseek-v3, deepseek-v3-base)

Tsinghua KEG Lab Models (5 models)

  • ChatGLM (6B variants: ChatGLM, ChatGLM2, ChatGLM3)
  • GLM-4 (standard and vision)

RWKV Models (7 models)

  • RWKV-4 (169M to 14B parameters)
  • RWKV-5 World

Community Fine-tuned Models (13 models)

  • Vicuna (7B, 13B, 33B)
  • Alpaca (7B, 13B)
  • WizardLM (7B, 13B, 30B)
  • Orca Mini (3B, 7B, 13B)
  • Zephyr (7B Alpha, Beta)

Perplexity Models (5 models)

  • PPLX (7B, 70B online and chat variants)
  • CodeLlama 34B Instruct

Hugging Face Models (5 models)

  • Microsoft DialoGPT (medium, large)
  • Facebook BlenderBot (400M, 1B, 3B variants)

AI21 Models (4 models)

  • Jurassic-2 (Light, Mid, Ultra, Jumbo Instruct)

Together AI Models (3 models)

  • RedPajama INCITE Chat (3B, 7B)
  • Nous Hermes LLaMA2 13B

Microsoft Models (4 models)

  • NEW: Phi Models (phi-3-mini, phi-3-small, phi-3-medium, phi-3.5-mini)
  • Optimized for coding and reasoning tasks
  • Enterprise-ready AI models

Amazon Models (3 models)

  • NEW: Titan Models (titan-text-express, titan-text-lite, titan-embed-text)
  • Enterprise-focused text generation and embedding
  • AWS Bedrock integration

Nvidia Models (2 models)

  • NEW: Nemotron Models (nemotron-4-340b, nemotron-3-8b)
  • Technical and scientific content optimization
  • GPU-accelerated training

IBM Models (3 models)

  • NEW: Granite Models (granite-13b-chat, granite-13b-instruct, granite-20b-code)
  • Enterprise AI with security and compliance focus
  • Code generation and business applications

Salesforce Models (3 models)

  • NEW: CodeGen Models (codegen-16b, codegen-6b, codegen-2b)
  • Specialized for code generation across multiple programming languages
  • Open-source code understanding

BigCode Models (3 models)

  • NEW: StarCoder Models (starcoder, starcoder2-15b, starcoderbase)
  • Multi-language code generation and understanding
  • Trained on diverse programming languages

Databricks Models (5 models)

  • NEW: Databricks Models (dbrx-instruct, dbrx-base, dolly-v2-12b, dolly-v2-7b, dolly-v2-3b)
  • High-quality instruction-following and base models

Voyage AI Models (6 models)

  • NEW: Voyage AI Models (voyage-2, voyage-large-2, voyage-code-2, voyage-finance-2, voyage-law-2, voyage-multilingual-2)
  • State-of-the-art embedding models for various domains

Total: 300+ models across 34+ providers

Installation

pip install toksum

Optional Dependencies

For OpenAI models, you'll need tiktoken:

pip install tiktoken

For Anthropic models, the library uses built-in approximation (no additional dependencies required).

Quick Start

from toksum import count_tokens, TokenCounter

# Quick token counting
tokens = count_tokens("Hello, world!", "gpt-4")
print(f"Token count: {tokens}")

# Using TokenCounter class
counter = TokenCounter("gpt-4")
tokens = counter.count("Hello, world!")
print(f"Token count: {tokens}")

Batch Token Counting

# Count tokens for multiple texts at once — useful for documents, datasets, etc.
import toksum

texts = ["Hello", "This is a test","count the words"]

text_counts = [toksum.count_tokens(text, model="gpt-3.5-turbo") for text in texts]
print("Batch Token Counting",text_counts)

Usage Examples

Basic Token Counting

from toksum import count_tokens

# Count tokens for different models
text = "The quick brown fox jumps over the lazy dog."

gpt4_tokens = count_tokens(text, "gpt-4")
gpt35_tokens = count_tokens(text, "gpt-3.5-turbo")
claude_tokens = count_tokens(text, "claude-3-opus-20240229")

print(f"GPT-4: {gpt4_tokens} tokens")
print(f"GPT-3.5: {gpt35_tokens} tokens") 
print(f"Claude-3 Opus: {claude_tokens} tokens")

Using TokenCounter Class

from toksum import TokenCounter

# Create a counter for a specific model
counter = TokenCounter("gpt-4")

# Count tokens for multiple texts
texts = [
    "Short text",
    "This is a longer text with more words and complexity.",
    "Very long text..." * 100
]

for text in texts:
    tokens = counter.count(text)
    print(f"'{text[:30]}...': {tokens} tokens")

💰 Token Cost Estimation (USD / INR)

from toksum.core import estimate_cost

# Estimate cost in USD
usd_cost = estimate_cost(1000, "gpt-3.5-turbo")
print(f"Cost in USD: ${usd_cost:.4f}")  # ➝ $0.0010

# Estimate cost in INR
inr_cost = estimate_cost(1000, "gpt-3.5-turbo", currency="INR")
print(f"Cost in INR: ₹{inr_cost:.2f}")  # ➝ ₹0.08

Chat Message Token Counting

from toksum import TokenCounter

counter = TokenCounter("gpt-4")

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is the capital of France?"},
    {"role": "assistant", "content": "The capital of France is Paris."}
]

total_tokens = counter.count_messages(messages)
print(f"Total conversation tokens: {total_tokens}")

💬 Token Counting + 💰 Cost Estimation (USD / INR)

from toksum import TokenCounter
from toksum.core import estimate_cost

# Initialize counter for a specific model
counter = TokenCounter("gpt-4")

# Define chat messages
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is the capital of France?"},
    {"role": "assistant", "content": "The capital of France is Paris."}
]

# Count total tokens in the conversation
total_tokens = counter.count_messages(messages)
print(f"Total conversation tokens: {total_tokens}")

# Estimate cost in USD
usd_cost = estimate_cost(total_tokens, "gpt-4", input_tokens=True)
print(f"Estimated cost in USD: ${usd_cost:.4f}")

# Estimate cost in INR
inr_cost = estimate_cost(total_tokens, "gpt-4", input_tokens=True, currency="INR")
print(f"Estimated cost in INR: ₹{inr_cost:.2f}")

Cost Estimation

from toksum import count_tokens, estimate_cost

text = "Your text here..." * 1000  # Large text
model = "gpt-4"

tokens = count_tokens(text, model)
input_cost = estimate_cost(tokens, model, input_tokens=True)
output_cost = estimate_cost(tokens, model, input_tokens=False)

print(f"Tokens: {tokens}")
print(f"Estimated input cost: ${input_cost:.4f}")
print(f"Estimated output cost: ${output_cost:.4f}")

🔢 Token & Cost Analyzer (USD / INR)

from toksum import count_tokens, estimate_cost

# Sample input
text = "Hiii my name is meeran" * 1  # Simulate large input for testing
model = "gpt-4"

# Count tokens
tokens = count_tokens(text, model)

# Estimate input/output costs in USD
input_cost_usd = estimate_cost(tokens, model, input_tokens=True)
output_cost_usd = estimate_cost(tokens, model, input_tokens=False)

# Estimate input/output costs in INR
input_cost_inr = estimate_cost(tokens, model, input_tokens=True, currency="INR")
output_cost_inr = estimate_cost(tokens, model, input_tokens=False, currency="INR")

# Print results
print(f"The given text is:{text}")
print(f"Tokens: {tokens}")
print(f"Estimated input cost (USD): ${input_cost_usd:.4f}")
print(f"Estimated output cost (USD): ${output_cost_usd:.4f}")
print(f"Estimated input cost (INR): ₹{input_cost_inr:.2f}")
print(f"Estimated output cost (INR): ₹{output_cost_inr:.2f}")

List Supported Models

from toksum import get_supported_models

models = get_supported_models()
print("Supported models:")
for provider, model_list in models.items():
    print(f"\n{provider.upper()}:")
    for model in model_list:
        print(f"  - {model}")

API Reference

Functions

count_tokens(text: str, model: str) -> int

Count tokens in text for a specific model.

Parameters:

  • text: The text to count tokens for
  • model: The model name (e.g., "gpt-4", "claude-3-opus-20240229")

Returns: Number of tokens as integer

get_supported_models() -> Dict[str, List[str]]

Get dictionary of supported models by provider.

Returns: Dictionary with provider names as keys and model lists as values

estimate_cost(token_count: int, model: str, input_tokens: bool = True) -> float

Estimate cost for given token count and model.

Parameters:

  • token_count: Number of tokens
  • model: Model name
  • input_tokens: Whether tokens are input (True) or output (False)

Returns: Estimated cost in USD

Classes

TokenCounter(model: str)

Token counter for a specific model.

Methods:

  • count(text: str) -> int: Count tokens in text
  • count_messages(messages: List[Dict[str, str]]) -> int: Count tokens in chat messages

Exceptions

UnsupportedModelError

Raised when an unsupported model is specified.

TokenizationError

Raised when tokenization fails.

How It Works

OpenAI Models

Uses the official tiktoken library to get exact token counts using the same tokenizer as OpenAI's API.

Anthropic Models

Uses a smart approximation algorithm based on:

  • Character count analysis
  • Whitespace and punctuation detection
  • Anthropic's guidance of ~4 characters per token
  • Adjustments for different text patterns

The approximation is typically within 10-20% of actual token counts for English text.

Development

Setup Development Environment

git clone https://github.com/kactlabs/toksum.git
cd toksum
pip install -e ".[dev]"

Run Tests

pytest

Run Tests with Coverage

pytest --cov=toksum --cov-report=html

Code Formatting

black toksum tests examples

Type Checking

mypy toksum

Contributing

Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change.

  1. Fork the repository
  2. Create your feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add some amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Changelog

v1.0.0 - 2025-01-06

  • 🎯 MAJOR MILESTONE: Achieved production-ready v1.0.0 status with 50+ additional models
  • Total Models: Expanded to 300+ models across 32+ providers
  • New Model Categories Added:
    • GPT-4 Turbo & Embedding Series: gpt-4-turbo-preview, gpt-4-0125-preview, text-embedding-ada-002, text-embedding-3-small/large, text-similarity models
    • Claude 3 Opus & Sonnet Series: claude-3-opus-20240229, claude-3-sonnet-20240229, with latest and short-name variants
    • Gemini Pro Comprehensive Series: gemini-pro, gemini-pro-vision, gemini-1.0-pro variants with vision support
    • Llama 2 Chat & Llama 3 Instruct Series: Complete chat and instruct model families including HuggingFace variants
    • Mistral Instruct Series: mistral-7b-instruct with version variants, mixtral-8x7b/8x22b-instruct
    • Extended BigCode StarCoder: starcoder2-3b/7b, starcoder-plus, starcoderbase variants
  • Enhanced Model Support: Updated model counts - OpenAI (60), Anthropic (33), Google (22), Meta (25), Mistral (16), BigCode (9)
  • Production Quality: 1000+ comprehensive test cases covering all model types, edge cases, and error scenarios
  • Specialized Tokenization: Optimized approximations for reasoning, code, embedding, multilingual, and instruction-following models
  • Enterprise Ready: Full backward compatibility, comprehensive error handling, and extensive documentation

v0.9.0

  • Added 30 new unique models across 6 new providers, bringing total to 279 models
  • New Providers (6 providers, 18 models):
    • Microsoft (4 models): phi-3-mini, phi-3-small, phi-3-medium, phi-3.5-mini
    • Amazon (3 models): titan-text-express, titan-text-lite, titan-embed-text
    • Nvidia (2 models): nemotron-4-340b, nemotron-3-8b
    • IBM (3 models): granite-13b-chat, granite-13b-instruct, granite-20b-code
    • Salesforce (3 models): codegen-16b, codegen-6b, codegen-2b
    • BigCode (3 models): starcoder, starcoder2-15b, starcoderbase
  • Extended Existing Providers (12 models):
    • Anthropic (4 models): claude-2.1-200k, claude-2.1-100k, claude-instant-2, claude-instant-2.0
    • OpenAI (3 models): gpt-4-vision, gpt-4-vision-preview-0409, gpt-4-vision-preview-1106
    • Cohere (2 models): command-r-plus-04-2024, command-r-plus-08-2024
    • Google (3 models): palm-2, palm-2-chat, palm-2-codechat
  • Comprehensive Testing: Added 500+ new test cases for all new models
  • Provider-Specific Approximations: Optimized tokenization for enterprise and code models
  • Enhanced Model Detection: Improved provider detection logic for all new model categories
  • Enterprise & Code Model Support: Specialized support for business AI and code generation models
  • Updated model counts: OpenAI (49), Anthropic (27), Google (16), Cohere (9), Microsoft (4), Amazon (3), Nvidia (2), IBM (3), Salesforce (3), BigCode (3)

v0.8.0

  • Added 22 new cutting-edge models across 8 model categories, bringing total to 249 models
  • Latest Model Releases:
    • OpenAI O1 Models (4 models): o1-preview, o1-mini, o1-preview-2024-09-12, o1-mini-2024-09-12
    • Anthropic Claude 3.5 Haiku (2 models): claude-3.5-haiku-20241022, claude-3-5-haiku-20241022
    • Anthropic Computer Use (2 models): claude-3-5-sonnet-20241022, claude-3.5-sonnet-computer-use
    • Google Gemini 2.0 (4 models): gemini-2.0-flash-exp, gemini-2.0-flash, gemini-exp-1206, gemini-exp-1121
    • Meta Llama 3.3 (2 models): llama-3.3-70b, llama-3.3-70b-instruct
    • Mistral Large 2 (2 models): mistral-large-2, mistral-large-2407
    • DeepSeek V3 (2 models): deepseek-v3, deepseek-v3-base
    • Qwen 2.5 (4 models): qwen-2.5-72b, qwen-2.5-32b, qwen-2.5-14b, qwen-2.5-7b
  • OpenAI O1 Support: Full tokenization support using cl100k_base encoding for accurate token counting
  • Advanced Model Detection: Enhanced provider detection logic to handle all new model categories
  • Comprehensive Testing: Added 200+ new test cases specifically for v0.8.0 models
  • Technical Improvements:
    • Provider-specific approximations for new model families
    • Chinese language optimization for Qwen 2.5 models
    • Code understanding for DeepSeek V3 models
    • Multimodal support for Gemini 2.0 models
    • Computer use model handling for Anthropic models
  • Updated model counts: OpenAI (46), Anthropic (23), Google (13), Meta (12), Mistral (10), Alibaba (20), DeepSeek (10)

v0.7.0

  • Added 139 new models across 16 new providers, bringing total to 212+ models
  • New Providers:
    • xAI: grok-1, grok-1.5, grok-2, grok-beta
    • Alibaba: qwen-1.5 series (0.5b to 110b), qwen-2 series, qwen-vl variants
    • Baidu: ernie-4.0, ernie-3.5, ernie-3.0, ernie-speed, ernie-lite, ernie-tiny, ernie-bot, ernie-bot-4
    • Huawei: pangu-alpha series (2.6b, 13b, 200b), pangu-coder variants
    • Yandex: yalm-100b, yalm-200b, yagpt, yagpt-2
    • Stability AI: stablelm-alpha, stablelm-base-alpha, stablelm-tuned-alpha, stablelm-zephyr variants
    • TII: falcon series (7b, 40b, 180b) with instruct and chat variants
    • EleutherAI: gpt-neo series, gpt-neox-20b, pythia series (70m to 12b)
    • MosaicML/Databricks: mpt series (7b, 30b) with chat/instruct variants, dbrx models
    • Replit: replit-code series (v1, v1.5, v2)
    • MiniMax: abab series (5.5 to 6.5) chat models
    • Aleph Alpha: luminous series (base, extended, supreme, supreme-control)
    • DeepSeek: deepseek-coder series, deepseek-vl series, deepseek-llm series
    • Tsinghua KEG Lab: chatglm series (6b variants), glm-4, glm-4v
    • RWKV: rwkv-4 series (169m to 14b), rwkv-5-world
    • Community Fine-tuned: vicuna, alpaca, wizardlm, orca-mini, zephyr variants
  • Enhanced provider-specific tokenization approximations for all new providers
  • Optimized approximations for Chinese models (Alibaba, Baidu, Huawei, MiniMax, Tsinghua)
  • Optimized approximations for Russian models (Yandex)
  • Specialized approximations for code models (Replit, DeepSeek-Coder, Huawei PanGu-Coder)
  • Expanded to 26 total providers
  • Total model support increased from 112 to 212+ models

v0.6.0

  • Type safety improvements and mypy compliance
  • Enhanced exception handling with proper type annotations
  • Improved conditional imports using TYPE_CHECKING pattern
  • Added runtime checks for tokenizer initialization
  • Full mypy compliance with better type hints throughout codebase

v0.5.0

  • Added 28 more models across 4 new providers:
    • Perplexity: pplx-7b-online, pplx-70b-online, pplx-7b-chat, pplx-70b-chat, codellama-34b-instruct
    • Hugging Face: microsoft/DialoGPT-medium, microsoft/DialoGPT-large, facebook/blenderbot variants
    • AI21: j2-light, j2-mid, j2-ultra, j2-jumbo-instruct
    • Together AI: RedPajama INCITE Chat models, Nous Hermes LLaMA2
    • Additional OpenAI legacy and embedding models
    • Additional Anthropic legacy models (Claude-1 series)
    • Additional Cohere model variants
  • Enhanced case-insensitive model matching
  • Expanded to 10 total providers
  • Total model support increased to 112 models

v0.4.0

  • Added 30 more models across all providers:
    • OpenAI: gpt-4o-2024-08-06, gpt-4o-2024-11-20, gpt-4-1106-vision-preview, gpt-3.5-turbo-instruct
    • Anthropic: claude-3-opus, claude-3-sonnet, claude-3-haiku, claude-instant (short names)
    • Google: gemini-1.5-pro-latest, gemini-1.5-flash-latest, gemini-1.0-pro, gemini-1.0-pro-vision, gemini-ultra
    • Meta: llama-3-8b, llama-3-70b, llama-3.1-8b, llama-3.1-70b, llama-3.1-405b, llama-3.2-1b, llama-3.2-3b
    • Mistral: mistral-large, mistral-medium, mistral-small, mistral-tiny, mixtral-8x7b, mixtral-8x22b
    • Cohere: command-light, command-nightly, command-r, command-r-plus
  • Enhanced provider-specific tokenization approximations
  • Total model support increased to 84 models

v0.3.0

  • Added 10 more models from new providers:
    • Google: gemini-pro, gemini-pro-vision, gemini-1.5-pro, gemini-1.5-flash
    • Meta: llama-2-7b, llama-2-13b, llama-2-70b
    • Mistral: mistral-7b, mistral-8x7b
    • Cohere: command
  • Expanded to 6 total providers (OpenAI, Anthropic, Google, Meta, Mistral, Cohere)
  • Enhanced approximation algorithms with provider-specific adjustments
  • Total model support increased to 54 models

v0.2.0

  • Added 10 new models:
    • OpenAI: gpt-4-turbo, gpt-4-turbo-2024-04-09, gpt-4o, gpt-4o-2024-05-13, gpt-4o-mini, gpt-4o-mini-2024-07-18
    • Anthropic: claude-3.5-sonnet-20240620, claude-3.5-sonnet-20241022, claude-3.5-haiku-20241022, claude-3-5-sonnet-20240620
  • Updated cost estimation for new models
  • Enhanced model support (now 43 total models)

v0.1.0

  • Initial release
  • Support for OpenAI GPT models and Anthropic Claude models
  • Token counting for text and chat messages
  • Cost estimation functionality
  • Comprehensive test suite

Acknowledgments

  • tiktoken for OpenAI tokenization
  • Anthropic for Claude model guidance
  • The open-source community for inspiration and best practices

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Release - 1.0.1 Latest
Jul 6, 2025
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