| Name | tokonomics JSON |
| Version |
0.5.0
JSON |
| download |
| home_page | None |
| Summary | Calcuate costs for LLM Usage based on token count |
| upload_time | 2025-10-06 20:27:44 |
| maintainer | None |
| docs_url | None |
| author | Philipp Temminghoff |
| requires_python | >=3.12 |
| license | MIT License
Copyright (c) 2024, Philipp Temminghoff
Permission is hereby granted, free of charge, to any person obtaining a copy
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# Tokonomics
[](https://pypi.org/project/tokonomics/)
[](https://pypi.org/project/tokonomics/)
[](https://pypi.org/project/tokonomics/)
[](https://pypi.org/project/tokonomics/)
[](https://pypi.org/project/tokonomics/)
[](https://pypi.org/project/tokonomics/)
[](https://pypi.org/project/tokonomics/)
[](https://github.com/phil65/tokonomics/releases)
[](https://github.com/phil65/tokonomics/graphs/contributors)
[](https://github.com/phil65/tokonomics/discussions)
[](https://github.com/phil65/tokonomics/forks)
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[](https://github.com/phil65/tokonomics)
[](https://github.com/phil65/tokonomics/commits)
[](https://github.com/phil65/tokonomics/releases)
[](https://github.com/phil65/tokonomics)
[](https://github.com/phil65/tokonomics)
[](https://codecov.io/gh/phil65/tokonomics/)
[](https://pyup.io/repos/github/phil65/tokonomics/)
[Read the documentation!](https://phil65.github.io/tokonomics/)
Calculate costs for LLM usage based on token counts using LiteLLM's pricing data.
## Installation
```bash
pip install tokonomics
```
## Features
- Automatic cost calculation for various LLM models
- Detailed cost breakdown (prompt, completion, and total costs)
- Caches pricing data locally (24-hour default cache duration)
- Supports multiple model name formats (e.g., "gpt-4", "openai:gpt-4")
- Asynchronous API
- Fully typed with runtime type checking
- Zero configuration required
## Usage
```python
import asyncio
from tokonomics import calculate_token_cost
async def main():
# Calculate cost with token counts
costs = await calculate_token_cost(
model="gpt-4",
input_tokens=100, # tokens used in the prompt
output_tokens=50, # tokens used in the completion
)
if costs:
print(f"Prompt cost: ${costs.input_cost:.6f}")
print(f"Completion cost: ${costs.output_cost:.6f}")
print(f"Total cost: ${costs.total_cost:.6f}")
else:
print("Could not determine cost for model")
asyncio.run(main())
```
You can customize the cache timeout:
```python
from tokonomics import get_model_costs, clear_cache
# Get model costs with custom cache duration (e.g., 1 hour)
costs = await get_model_costs("gpt-4", cache_timeout=3600)
if costs:
print(f"Input cost per token: ${costs['input_cost_per_token']}")
print(f"Output cost per token: ${costs['output_cost_per_token']}")
clear_cache()
```
### Getting Model Token Limits
You can retrieve the token limits for a model using `get_model_limits`:
```python
from tokonomics import get_model_limits
async def main():
# Get token limit information for a model
limits = await get_model_limits("gpt-4")
if limits:
print(f"Maximum total tokens: {limits.total_tokens}")
print(f"Maximum input tokens: {limits.input_tokens}")
print(f"Maximum output tokens: {limits.output_tokens}")
else:
print("Could not find limit data for model")
```
The function returns a `TokenLimits` object with three fields:
- `total_tokens`: Maximum combined tokens (input + output) the model supports
- `input_tokens`: Maximum number of input/prompt tokens
- `output_tokens`: Maximum number of output/completion tokens
### Pydantic-AI Integration
If you're using pydantic-ai, you can directly calculate costs from its Usage objects:
```python
from tokonomics import calculate_pydantic_cost
# Assuming you have a pydantic-ai Usage object
costs = await calculate_pydantic_cost(
model="gpt-4",
usage=usage_object,
)
if costs:
print(f"Prompt cost: ${costs.input_cost:.6f}")
print(f"Completion cost: ${costs.output_cost:.6f}")
print(f"Total cost: ${costs.total_cost:.6f}")
```
## Model Name Support
The library supports multiple formats for model names:
- Direct model names: `"gpt-4"`
- Provider-prefixed: `"openai:gpt-4"`
- Provider-path style: `"openai/gpt-4"`
Names are matched case-insensitively.
## Data Source
Pricing data is sourced from [LiteLLM's pricing repository](https://github.com/BerriAI/litellm) and is automatically cached locally using `hishel`. The cache is updated when pricing data is not found or has expired.
## Requirements
- Python 3.12+
- `httpx`
- `platformdirs`
- `upath`
- `pydantic` (≥ 2.0)
## License
This project is licensed under the MIT License - see the LICENSE file for details.
Raw data
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"description": "# Tokonomics\n\n[](https://pypi.org/project/tokonomics/)\n[](https://pypi.org/project/tokonomics/)\n[](https://pypi.org/project/tokonomics/)\n[](https://pypi.org/project/tokonomics/)\n[](https://pypi.org/project/tokonomics/)\n[](https://pypi.org/project/tokonomics/)\n[](https://pypi.org/project/tokonomics/)\n[](https://github.com/phil65/tokonomics/releases)\n[](https://github.com/phil65/tokonomics/graphs/contributors)\n[](https://github.com/phil65/tokonomics/discussions)\n[](https://github.com/phil65/tokonomics/forks)\n[](https://github.com/phil65/tokonomics/issues)\n[](https://github.com/phil65/tokonomics/pulls)\n[](https://github.com/phil65/tokonomics/watchers)\n[](https://github.com/phil65/tokonomics/stars)\n[](https://github.com/phil65/tokonomics)\n[](https://github.com/phil65/tokonomics/commits)\n[](https://github.com/phil65/tokonomics/releases)\n[](https://github.com/phil65/tokonomics)\n[](https://github.com/phil65/tokonomics)\n[](https://codecov.io/gh/phil65/tokonomics/)\n[](https://pyup.io/repos/github/phil65/tokonomics/)\n\n[Read the documentation!](https://phil65.github.io/tokonomics/)\n\n\nCalculate costs for LLM usage based on token counts using LiteLLM's pricing data.\n\n## Installation\n\n```bash\npip install tokonomics\n```\n\n## Features\n\n- Automatic cost calculation for various LLM models\n- Detailed cost breakdown (prompt, completion, and total costs)\n- Caches pricing data locally (24-hour default cache duration)\n- Supports multiple model name formats (e.g., \"gpt-4\", \"openai:gpt-4\")\n- Asynchronous API\n- Fully typed with runtime type checking\n- Zero configuration required\n\n## Usage\n\n```python\nimport asyncio\nfrom tokonomics import calculate_token_cost\n\nasync def main():\n # Calculate cost with token counts\n costs = await calculate_token_cost(\n model=\"gpt-4\",\n input_tokens=100, # tokens used in the prompt\n output_tokens=50, # tokens used in the completion\n )\n\n if costs:\n print(f\"Prompt cost: ${costs.input_cost:.6f}\")\n print(f\"Completion cost: ${costs.output_cost:.6f}\")\n print(f\"Total cost: ${costs.total_cost:.6f}\")\n else:\n print(\"Could not determine cost for model\")\n\nasyncio.run(main())\n```\n\nYou can customize the cache timeout:\n\n```python\nfrom tokonomics import get_model_costs, clear_cache\n\n# Get model costs with custom cache duration (e.g., 1 hour)\ncosts = await get_model_costs(\"gpt-4\", cache_timeout=3600)\nif costs:\n print(f\"Input cost per token: ${costs['input_cost_per_token']}\")\n print(f\"Output cost per token: ${costs['output_cost_per_token']}\")\n\nclear_cache()\n```\n\n### Getting Model Token Limits\n\nYou can retrieve the token limits for a model using `get_model_limits`:\n\n```python\nfrom tokonomics import get_model_limits\n\nasync def main():\n # Get token limit information for a model\n limits = await get_model_limits(\"gpt-4\")\n\n if limits:\n print(f\"Maximum total tokens: {limits.total_tokens}\")\n print(f\"Maximum input tokens: {limits.input_tokens}\")\n print(f\"Maximum output tokens: {limits.output_tokens}\")\n else:\n print(\"Could not find limit data for model\")\n```\n\nThe function returns a `TokenLimits` object with three fields:\n- `total_tokens`: Maximum combined tokens (input + output) the model supports\n- `input_tokens`: Maximum number of input/prompt tokens\n- `output_tokens`: Maximum number of output/completion tokens\n\n\n### Pydantic-AI Integration\n\nIf you're using pydantic-ai, you can directly calculate costs from its Usage objects:\n\n```python\nfrom tokonomics import calculate_pydantic_cost\n\n# Assuming you have a pydantic-ai Usage object\ncosts = await calculate_pydantic_cost(\n model=\"gpt-4\",\n usage=usage_object,\n)\n\nif costs:\n print(f\"Prompt cost: ${costs.input_cost:.6f}\")\n print(f\"Completion cost: ${costs.output_cost:.6f}\")\n print(f\"Total cost: ${costs.total_cost:.6f}\")\n```\n\n## Model Name Support\n\nThe library supports multiple formats for model names:\n- Direct model names: `\"gpt-4\"`\n- Provider-prefixed: `\"openai:gpt-4\"`\n- Provider-path style: `\"openai/gpt-4\"`\n\nNames are matched case-insensitively.\n\n## Data Source\n\nPricing data is sourced from [LiteLLM's pricing repository](https://github.com/BerriAI/litellm) and is automatically cached locally using `hishel`. The cache is updated when pricing data is not found or has expired.\n\n## Requirements\n\n- Python 3.12+\n- `httpx`\n- `platformdirs`\n- `upath`\n- `pydantic` (\u2265 2.0)\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n",
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