openai_cost_calculator_with_langchain

This is a package forked of the original openai_cost_calculator, which integrates support for Langchain.

Instant, accurate USD cost estimates for OpenAI & Azure OpenAI API calls. Works with Chat Completions and the Responses API, streaming or not. It also works with Langchain invoke responses. Offers a typed Decimal-based API for finance-safe math and a legacy string API for drop-ins.

Docs: https://orkunkinay.github.io/openai_cost_calculator/

Installation

pip install openai-cost-calculator-with-langchain

Import name uses underscores: import openai_cost_calculator-with-langchain

Quickstart

Typed (recommended)

from openai import OpenAI
from openai_cost_calculator import estimate_cost_typed

client = OpenAI()
resp = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Hi there!"}],
)

cost = estimate_cost_typed(resp)  # -> CostBreakdown (Decimal fields)
print(cost.total_cost)            # Decimal('0.00000750')
print(cost.as_dict(stringify=True))  # 8-dp strings if you prefer

Legacy (string output)

from openai_cost_calculator import estimate_cost
print(estimate_cost(resp))  # dict of 8-dp strings

Responses API

resp = client.responses.create(model="gpt-4.1-mini", input=[{"role":"user","content":"Hi"}])
from openai_cost_calculator import estimate_cost_typed
print(estimate_cost_typed(resp))

Streaming

stream = client.chat.completions.create(
  model="gpt-4o-mini",
  messages=[{"role":"user","content":"Hi"}],
  stream=True,
  stream_options={"include_usage": True},
)
from openai_cost_calculator import estimate_cost_typed
print(estimate_cost_typed(stream))

Langchain

import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from openai_cost_calculator import estimate_cost_typed

load_dotenv()

model = ChatOpenAI(
    api_key=os.getenv("OPENAI_API_KEY"),
    model="gpt-4o-mini",
    temperature=0,
    max_tokens=None,
    timeout=None,
    max_retries=2,
)

messages = [
    (
        "system",
        "You are a helpful translator. Translate the user sentence to French.",
    ),
    ("human", "I love programming."),
]

response = model.invoke(messages)
print(estimate_cost_typed(response, is_langchain_response=True))

Highlights

• Typed API: CostBreakdown dataclass with Decimal precision
• Drop-in legacy API: 8-decimal strings (backward compatible)
• Handles edge cases: cached tokens, undated model strings, streaming generators, Azure deployment names
• Pricing sources: Remote CSV (24h cache) + local overrides and offline mode

Pricing utilities

from openai_cost_calculator import (
  refresh_pricing, set_offline_mode,
  add_pricing_entry, add_pricing_entries, clear_local_pricing
)

# Force refresh (bypasses 24h cache)
refresh_pricing()

# Run fully offline (no network calls)
set_offline_mode(True)

# Teach custom prices (per 1M tokens)
add_pricing_entry(
  "ollama/qwen3:30b", "2025-08-01",
  input_price=0.20, output_price=0.60, cached_input_price=0.04
)

Remote CSV (auto-fetched, cached 24h):
https://raw.githubusercontent.com/orkunkinay/openai_cost_calculator/refs/heads/main/data/gpt_pricing_data.csv

Errors

Recoverable issues raise CostEstimateError with a clear message (missing pricing row, unexpected input shape, etc.).

Troubleshooting

• “Pricing not found” → confirm row exists in the CSV; call refresh_pricing().
• cached_tokens = 0 → ensure include_usage_details=True (classic) or stream_options={"include_usage": True} (streaming).
• Model string has no date → the latest row with date ≤ today is used.

Links

• Docs & examples: https://orkunkinay.github.io/openai_cost_calculator/
• Source: https://github.com/orkunkinay/openai_cost_calculator
• Issues: https://github.com/orkunkinay/openai_cost_calculator/issues

License

MIT © 2025 Orkun Kınay & Murat Barkın Kınay