# AI Model Directory

> A curated directory of AI models and the places to run them: frontier labs, open-weight families, model hubs, and inference hosts. Editorial, unpaid, and checked.

Site: https://aimodeldirectory.com/
Listings: 26
Updated: 2026-09-11

## What this site is
Picking a model is now three decisions, not one: whose model, open weights or an API, and hosted by whom. This directory keeps those separate — the labs that train frontier models, the open-weight families you can download, the hubs that distribute them, and the providers that will serve any of it for you.

## Machine-readable data
- https://aimodeldirectory.com/entries.json  — the full listing set as JSON (name, url, category, blurb, tags).
  Stable shape. Attribution appreciated; a link back to the listing page is the only ask.

## Editorial policy
Listings are editorial and unpaid. Inclusion is not an endorsement. Outbound links are
checked to resolve before publication. Advertising is labelled and never determines
editorial placement.

## Categories
- Frontier labs
- Open-weight families
- Hubs & distribution
- Hosted inference
- Self-hosting engines

## Questions this site answers directly

### What does 'open weights' actually mean?
That the trained parameters can be downloaded and run on your own hardware. It does not necessarily mean open source in the traditional sense — training data and code are usually not released, and many licences restrict commercial use or scale. Read the specific licence rather than assuming, because they differ substantially between families.

### When is a hosted API better than running weights yourself?
Almost always at the start. An API gives you the newest capability with no infrastructure and no idle cost. Self-hosting wins when you have steady high volume, a hard data-residency requirement, or a need to guarantee the model will not change under you — that last one is underrated, because hosted models are updated and your prompts were tuned against the old behaviour.

### Why do benchmark scores disagree with real-world results?
Benchmarks measure narrow, often contaminated tasks, and leaderboards reward optimising for them. They are useful for excluding clearly unsuitable models and close to useless for choosing between good ones. Build a small evaluation from your own real inputs — a few dozen examples will tell you more than any public leaderboard.

### What is a context window and how much do I need?
It is how much text a model can consider at once, prompt and reply together. Bigger is not automatically better: attention quality often degrades well before the stated limit, and long contexts cost more and run slower. Retrieval that sends only the relevant passages usually beats pasting an entire corpus into a large window.

### Does this directory take payment for listings?
No. Listings are editorial and unpaid. Advertising on the site is labelled and has no bearing on what gets listed or where it appears.

## Contact
privacy@aimodeldirectory.com · legal@aimodeldirectory.com