---
id: models-dev
version: "1.0.398"
license: MIT
license_treatment: permissive
maintenance: active
---

# models-dev — Typed Python interface to models.dev API data

License: permissive · Maintenance: active · Downloads: 142.3K/mo

## What it is and what it does

models-dev is a typed Python package that bundles the models.dev catalog—a community-maintained registry of LLM models—as local data. Instead of making HTTP calls to fetch model metadata, you import and query structured Python objects for provider information, pricing, context limits, modalities, and capabilities. The package is designed to stay in sync with the upstream catalog through hourly update checks and frequent releases.

It has zero runtime dependencies and ships as a ~75 KB wheel, making it lightweight and easy to integrate into projects that need to reference LLM model details at runtime—such as prompt engineering frameworks, cost calculators, or model-selection logic. The data is pre-typed, so IDEs and type checkers can validate queries.

Use it for:

- Look up pricing and context limits for a specific LLM model before making an API call.
- Filter available models by capability (e.g., find all reasoning-enabled models across providers).
- Build a model-selection tool that compares input/output costs or modality support.
- Populate a UI dropdown or CLI menu with current provider and model names without external API calls.
- Validate model names and provider identities in a local configuration file or request handler.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Provides a typed Python interface to models.dev, a community-maintained catalog of 2000+ LLM models from 75+ providers, with pricing, context limits, and capability metadata—no HTTP calls needed.

Yes, if you need offline access to LLM model metadata. The package is lightweight, actively maintained, has no dependencies, and covers a broad catalog (2000+ models from 75+ providers). Install it when you want to avoid HTTP calls for model lookups or need typed access to pricing and capability data. Not necessary if you only work with one provider's SDK or rarely need to query model details.

## Install

pip install models-dev
uv add models-dev
poetry add models-dev

## Installing models-dev

Before you install: Low friction: zero runtime dependencies, ~75 KB installed, and actively maintained with hourly update checks. Latest release 85 days old; repository shows recent activity.

License in practice: MIT license (permissive) allows commercial and private use with minimal restrictions—suitable for most projects.

Quickstart:

pip install models-dev

from models_dev import providers, get_provider, get_model

openai = get_provider("openai")
gpt4o = get_model("openai", "gpt-4o")
print(gpt4o.cost.input, gpt4o.limit.context)

Requires Python 3.10 or later.

Verify before relying:

- How frequently the package actually publishes updates when models.dev changes (claimed hourly check, but release cadence unknown).
- Whether the 2000+ model count and 75+ provider figure remain current or are static snapshots in the package.

## Package facts

- License: MIT (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 142.3K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

llm model catalog lookup, ai model pricing data, openai anthropic google models, model context limits metadata, llm provider information, model capabilities database, ai model comparison data, llm-models, metadata-catalog, offline-data

[View on SkillFed](https://skillfed.io/packages/models-dev) · [View on PyPI](https://pypi.org/project/models-dev/)