llm-costs — Multi-Provider LLM Cost Calculator (open source) by Jason Tanllm-costs — Multi-Provider LLM Cost Calculator (open source) by Jason Tan

llm-costs — Multi-Provider LLM Cost Calculator (open source)

Jason Tan

AI Engineer

Backend Engineer

ML Engineer

Google Gemini

OpenAI

Python

Computer Software

llm-costs

LLM cost calculator for major providers. Calculates API costs from token usage data.

Installation

pip install llm-costs

Or with uv:

uv add llm-costs

Usage

from llm_costs import calculate_cost, get_model_pricing

# Calculate cost from LangChain UsageMetadata structure
result = calculate_cost(
    provider="anthropic",
    model="claude-sonnet-4-20250514",
    usage={
        "input_tokens": 1000,
        "output_tokens": 500,
        "total_tokens": 1500,
    },
)

print(f"Cost: ${result['cost']:.6f}")  # Cost: $0.010500
print(f"Input: ${result['breakdown']['input_cost']:.6f}")
print(f"Output: ${result['breakdown']['output_cost']:.6f}")

With Prompt Caching

result = calculate_cost(
    provider="anthropic",
    model="claude-sonnet-4-20250514",
    usage={
        "input_tokens": 1000,
        "output_tokens": 500,
        "total_tokens": 1500,
        "input_token_details": {
            "cache_read": 5000,
            "cache_creation": 0,
        },
    },
)

Batch Pricing

result = calculate_cost(
    provider="anthropic",
    model="claude-sonnet-4-20250514",
    usage={"input_tokens": 1000, "output_tokens": 500, "total_tokens": 1500},
    batch=True,  # 50% discount
)

Get Model Pricing Info

pricing = get_model_pricing("anthropic", "claude-sonnet-4-20250514")
print(f"Input: ${pricing['input']}/MTok")   # Input: $3.0/MTok
print(f"Output: ${pricing['output']}/MTok") # Output: $15.0/MTok

List Available Models

from llm_costs.calculator import list_models, list_providers

providers = list_providers()  # ['anthropic', 'openai', 'google']
models = list_models("anthropic")  # ['claude-opus-4-5-20251101', ...]

Supported Providers

Anthropic

Model Input Output Cache Read claude-opus-4-5-20251101 $5.00 $25.00 $0.50 claude-opus-4-1-20250414 $15.00 $75.00 $1.50 claude-opus-4-20250514 $15.00 $75.00 $1.50 claude-sonnet-4-5-20250514 $3.00 $15.00 $0.30 claude-sonnet-4-20250514 $3.00 $15.00 $0.30 claude-3-7-sonnet-20250219 $3.00 $15.00 $0.30 claude-haiku-4-5-20250514 $1.00 $5.00 $0.10 claude-3-5-haiku-20241022 $0.80 $4.00 $0.08 claude-3-opus-20240229 $15.00 $75.00 $1.50 claude-3-haiku-20240307 $0.25 $1.25 $0.03

Prices per million tokens. Long context pricing (>200K tokens) applies to Sonnet models.

OpenAI

Model Input Output Cache Read gpt-5.2 $1.75 $14.00 $0.175 gpt-5.1 $1.25 $10.00 $0.125 gpt-5 $1.25 $10.00 $0.125 gpt-5-mini $0.25 $2.00 $0.025 gpt-5-nano $0.05 $0.40 $0.005 gpt-5.2-pro $21.00 $168.00 - gpt-5-pro $15.00 $120.00 - gpt-4.1 $2.00 $8.00 $0.50 gpt-4.1-mini $0.40 $1.60 $0.10 gpt-4.1-nano $0.10 $0.40 $0.025 gpt-4o $2.50 $10.00 $1.25 gpt-4o-mini $0.15 $0.60 $0.075 o1 $15.00 $60.00 $7.50 o1-pro $150.00 $600.00 - o1-mini $1.10 $4.40 $0.55 o3 $2.00 $8.00 $0.50 o3-pro $20.00 $80.00 - o3-mini $1.10 $4.40 $0.55 o3-deep-research $10.00 $40.00 $2.50 o4-mini $1.10 $4.40 $0.275 o4-mini-deep-research $2.00 $8.00 $0.50 computer-use-preview $3.00 $12.00 -

Prices per million tokens (Standard tier).

Google

Model Input Output Cache Read gemini-3-pro-preview $2.00 $12.00 $0.20 gemini-2.5-pro $1.25 $10.00 $0.125 gemini-2.5-flash $0.30 $2.50 $0.03 gemini-2.5-flash-lite $0.10 $0.40 $0.01 gemini-2.0-flash $0.10 $0.40 $0.025 gemini-2.0-flash-lite $0.075 $0.30 -

Prices per million tokens (Paid tier). Long context pricing (>200K tokens) applies to Pro models.

Usage Schema

The library accepts token usage in LangChain's UsageMetadata format:

{
    "input_tokens": int,
    "output_tokens": int,
    "total_tokens": int,
    "input_token_details": {
        "cache_read": int,      # Cached tokens read
        "cache_creation": int,  # Tokens written to cache
    },
    "output_token_details": {
        "reasoning": int,       # Reasoning tokens (o1/thinking models)
    },
}

Return Value

calculate_cost() returns a CostResult:

{
    "cost": 0.0105,           # Total cost in USD
    "currency": "USD",
    "breakdown": {
        "input_cost": 0.003,
        "output_cost": 0.0075,
        "cache_read_cost": 0.0,      # If applicable
        "cache_creation_cost": 0.0,  # If applicable
    },
    "pricing_used": {
        "input_per_mtok": 3.0,
        "output_per_mtok": 15.0,
        "batch_applied": False,
        "long_context_applied": False,
    },
}

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Posted Sep 26, 2026

Open-source Python lib for accurate LLM cost across Anthropic, OpenAI & Google — cache, batch & long-context aware. On PyPI, used in production.

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