modelpricing-ai 2026.2.17

Python client for ModelPricing.ai cost estimates and tracking

modelpricing-ai

Python client for the ModelPricing.ai API — estimate LLM usage costs and track spending with a single call.

Installation

pip install modelpricing-ai

For async support (requires aiohttp):

pip install modelpricing-ai[async]

Quick Start

from modelpricing_ai import ModelPricingClient

with ModelPricingClient(api_key="YOUR_API_KEY") as client:
    estimate = client.estimate(
        model="gpt-4o-mini",
        tokens_in=1000,
        tokens_out=500,
        trace_id={"requestId": "abc-123"},
    )
    print(f"Cost: ${estimate.total:.6f}")

Async Usage

Install the async extra, then use AsyncModelPricingClient as an async context manager:

import asyncio
from modelpricing_ai import AsyncModelPricingClient

async def main():
    async with AsyncModelPricingClient(api_key="YOUR_API_KEY") as client:
        estimate = await client.estimate(
            model="gpt-4o-mini",
            tokens_in=1000,
            tokens_out=500,
            trace_id={"requestId": "abc-123"},
        )
        print(f"Cost: ${estimate.total:.6f}")

asyncio.run(main())

Response Structure

Both estimate() and await estimate() return an EstimateResponse object:

estimate.total        # float — total USD cost
estimate.model        # str   — canonical model name
estimate.traceId      # dict | None — your pass-through trace ID
estimate.breakdown    # EstimateBreakdownGroup
  .input              # EstimateBreakdown
    .unit             #   str   — e.g. "token"
    .branch           #   str   — pricing tier that matched
    .qty              #   int   — number of input tokens
    .rate             #   float — per-unit rate
    .subtotal         #   float — input cost
  .output             # EstimateBreakdown (same fields for output tokens)

Configuration

Parameter	Default	Description
api_key	required	Your ModelPricing.ai API key (also reads MODELPRICING_API_KEY env var)
base_url	"https://api.modelpricing.ai"	API base URL (also reads MODELPRICING_BASE_URL env var)
timeout	30.0	Request timeout in seconds
max_retries	3	Maximum retry attempts for transient errors
session	None	Optional requests.Session (sync) or aiohttp.ClientSession (async)

Parameters are resolved in order: constructor argument > environment variable > default.

client = ModelPricingClient(
    api_key="YOUR_API_KEY",
    base_url="https://api.modelpricing.ai",
    timeout=30.0,
    max_retries=3,
)

Error Handling

The client raises typed exceptions for different failure modes:

Exception	HTTP Status	When
Unauthorized	401	Invalid or missing API key
ValidationError	422	Invalid model name or metrics
NotFound	404	Unknown endpoint
ServerError	5xx	Server-side failures

All exceptions inherit from ModelPricingError and include a status_code attribute.

from modelpricing_ai.errors import Unauthorized, ValidationError, ServerError

try:
    estimate = client.estimate(model="gpt-4o-mini", tokens_in=1000, tokens_out=500)
except Unauthorized:
    print("Check your API key")
except ValidationError as e:
    print(f"Bad request: {e}")
except ServerError:
    print("Server error — will be retried automatically")

Retry Behavior

The client automatically retries on transient errors with exponential backoff:

• Retries: 5xx server errors and network/connection errors
• No retry: 4xx client errors (401, 404, 422)
• Default: 3 retries with exponential backoff (0.1 s initial, 2 s max)

# Increase retries for unreliable networks
client = ModelPricingClient(api_key="YOUR_API_KEY", max_retries=5)

# Disable retries (no retry attempts)
client = ModelPricingClient(api_key="YOUR_API_KEY", max_retries=0)

License

MIT