llm-catalogue 0.1.0

Model names, pricing, and free-tier metadata for OpenAI, Anthropic, and Google Gemini.

llm-catalogue

Model names, pricing, and free-tier metadata for OpenAI, Anthropic, and Google Gemini, in one small zero-dependency package.

pip install llm-catalogue

Usage

from llm_catalogue import Catalog

catalog = Catalog()

# All free-tier-eligible models for a provider ([] if none)
free_gemini = catalog.get_free_models("google")
free_openai = catalog.get_free_models("openai")  # -> []

# All models for a provider
claude_models = catalog.get_models("anthropic")

# UI toggle helper
if catalog.has_free_tier("google"):
    ...

# Free models across every provider
for model in catalog.find_free_models():
    print(model.id, model.vendor.value)

# Look up one model and estimate a request's cost
model = catalog.get_model("gemini-2.5-flash")
cost_usd = model.calculate_cost(input_tokens=50_000, output_tokens=2_000)

get_models/get_free_models/has_free_tier accept "openai", "anthropic" (or "claude"), and "google" (or "gemini").

Data freshness

Catalog() never makes a network call — it reads the registry.json bundled with the package (or a previously cached one under ~/.cache/llm_catalogue/), so imports stay fast and offline-safe. To pull the latest data from GitHub:

catalog = Catalog(auto_update=True)   # fetch on construction
catalog.refresh()                     # or fetch explicitly, any time
catalog.refresh(force=True)           # bypass the 24h cache TTL

refresh() never raises — on failure (offline, timeout, bad response) it leaves the currently loaded data untouched and returns False.

Keeping the bundled registry up to date

The registry is rebuilt from each provider's public pricing docs:

• Gemini pricing
• Claude pricing
• OpenAI pricing

pip install -e ".[scraper]"
python -m llm_catalogue.scraper

This overwrites src/llm_catalogue/data/registry.json. Commit the result and cut a new release so it ships in the next pip install.

Scope and known limitations (v1)

• Only "Standard" tier, text-in/text-out pricing is captured. Batch pricing is included where the source table has it; Flex/Priority tiers are not.
• Multimodal, audio, image, video, and embedding-specialist models are out of scope — this tracks general-purpose chat/text LLMs.
• cached_input is the cache-read price. Separate cache-write premiums (e.g. Anthropic's 5m/1h cache writes, OpenAI's gpt-5.6-family write cost) aren't modelled yet.
• free_tier.rate_limit_rpm isn't populated — Gemini's free-tier RPM limits live on a separate rate-limits doc this scraper doesn't fetch yet.
• Gemini's tiered (>200k token) pricing is captured via tiered_pricing; OpenAI's <272K context length models are recorded with context_window but don't have a documented over-the-limit rate, so they aren't tiered.

Data last refreshed: 2026-07-26.