llm-catalogue 0.2.1

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

Full API reference (generated from docstrings): see Documentation below.

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 directly
model = catalog.get_model("gemini-2.5-flash")

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

Estimating request cost

Every AIModel can price a request via calculate_cost(), which accounts for prompt caching, batch pricing, and context-length tiering automatically:

model = catalog.get_model("gemini-2.5-pro")

# Standard request
model.calculate_cost(input_tokens=50_000, output_tokens=2_000)

# Half the input tokens were served from a prompt cache
model.calculate_cost(input_tokens=50_000, output_tokens=2_000, cached_tokens=25_000)

# Via the batch API (uses pricing.batch_input/batch_output instead)
model.calculate_cost(input_tokens=50_000, output_tokens=2_000, is_batch=True)

# Over the model's context-length threshold -- automatically picks up
# tiered_pricing.over_threshold_rate instead of the base rate
model.calculate_cost(input_tokens=250_000, output_tokens=2_000)

Cost is returned in USD, rounded to 6 decimal places. See the AIModel API reference for exactly how each argument affects the rate used.

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.

registry.json schema

Catalog loads this file at src/llm_catalogue/data/registry.json. It's a plain JSON document, so you can also read it directly without the package:

Field	Type	Notes
updated_at	string	ISO date the registry was last rebuilt.
models	array	List of model objects, described below.

Each entry in models matches AIModel.to_dict():

Field	Type	Notes
id	string	Provider-native model id, e.g. "gpt-4o".
name	string	Human-readable display name.
vendor	string	One of "openai", "anthropic", "google".
pricing	object	TokenPricing: standard_input, output, cached_input, batch_input, batch_output (USD per 1M tokens; nulls where unknown/not applicable).
context_window	int or null	Max input tokens, where documented.
tiered_pricing	object or null	{threshold_tokens, base_rate, over_threshold_rate} for models with context-length-dependent pricing.
free_tier	object or null	{has_free_tier, rate_limit_rpm, data_used_for_training}.
status	string	"active", "deprecated", "retired", or "limited_availability".
tool_costs	object	Reserved for per-tool pricing; empty in v1.

Documentation

Full API docs are generated from the docstrings on Catalog, AIModel, TokenPricing, TieredPricing, and FreeTierPolicy via mkdocstrings. To browse them locally:

pip install -e ".[docs]"
mkdocs serve

then open http://127.0.0.1:8000. mkdocs build produces a static site under site/ you can host anywhere (e.g. GitHub Pages).

Contributing / keeping the registry up to date

Project layout:

src/llm_catalogue/
  models.py     # AIModel, TokenPricing, TieredPricing, FreeTierPolicy, Vendor, ModelStatus
  catalogue.py  # Catalog -- the main entry point
  scraper.py    # dev-only tool that rebuilds data/registry.json
  data/registry.json
tests/          # pytest
docs/           # mkdocs source

Run the test suite:

pip install -e ".[dev]"
pytest

Refresh the bundled pricing data from each provider's live docs:

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

This overwrites src/llm_catalogue/data/registry.json from:

• Gemini pricing
• Claude pricing
• OpenAI pricing

Review the diff, commit it, 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.