How to find when to download new models for open source enthusiasts in 2026

Learn how open source model enthusiasts can find when to download new models and compare ways to track release timing in 2026.

By SupaKeeper · 2026-10-28

SupaKeeper is published by LunarWerx, which makes some of the tools compared here.

To find when to download new models, list the model families that matter, choose a source that states which models it follows and how it makes its estimate, and check that the figure stays current as launches are announced.

Option	What it can do	What to check
Model Odds	Ranks every Anthropic and OpenAI model family by its estimated chance of a release within 30 days. ^[1]	Check whether the models you plan to download are among the Anthropic and OpenAI families it follows.
Existing open source model workflow	Use the release pages and channels already in your workflow to follow announcements.	Check whether this approach gives the timing you need for each model family.
Another model tracker	A separate tracking page or service may give release timing for the models you follow.	Check the companies it covers, how current its figures are, and how each estimate is made.

What you need before you start

Before finding when to download new models, decide which model families matter for the work you plan to do. Each company manages its own releases, so the timing you follow should match the models you expect to download.

Understand that release timing is given as an estimate, not a fixed date. A forecast changes as new launches appear, so check how current the figure is before relying on it.

Separate a release chance from a record of past launches. A verified history of earlier releases helps you judge how believable a forecast is.

Decide how you want to be told about changes. Some people watch pages, some want a list of every past launch, and others want an alert when an estimate moves.

Also check whether the companies behind the models you plan to download are followed by the source you pick. A tracker that covers only certain companies may not reach the open source models you care about.

Step by step

List the model families you expect to download. Write down the company and the current model in each family so the timing you follow stays on target.

Decide what timing you need. If the main question is whether a new release is likely within the next few weeks, look for a source that gives release odds on a time window rather than a single date.

Choose where to read those odds. Look for a page or service that states which model families it follows and how it makes each estimate.

Check how the estimate was built. A forecast that uses recent gaps between launches and a verified record of past releases is easier to trust than one that does not say where its numbers came from.

Check how current the figure is. Release timing can change as new launches happen, so prefer a source that refreshes as announcements arrive.

Look at more than one estimate when a source shows it. The spread between the high and low numbers gives a sense of how much the forecast could move.

Set up an alert if timing matters. Confirm the contact method you want to use and check that a message reaches you when an estimate changes.

Recheck the choice when the release picture changes. The companies you follow may change over time, and a source that covers more of them becomes more useful.

Keep the chosen page or API within reach. Rechecking the odds before you plan a download keeps the timing as current as possible. The goal is a repeatable routine you can run each time you want a new model.

Tools that can do it

Model Odds is made by the team that publishes this site.

Model Odds ranks every Anthropic and OpenAI model family by its estimated chance of a release within 30 days. ^[1]

Anthropic and OpenAI each get their own page that lists every past release. ^[1] Each past launch the model uses has to be confirmed by an official post and an independent timeline, and it is listed with its date. ^[1]

It runs three estimators side by side and publishes the spread between them as the error bar. ^[1] The estimate is based on the timing of recent launches and takes into account how long the current wait has already lasted. ^[1] Signals read automatically from company SDKs and docs, and from third-party model catalogues when two of them agree, can only raise the odds. ^[1]

The estimator can replay any past date without using data from after that date. ^[1]

An API provides the current odds, the launch record and service health. ^[1] Model Odds is a website with a public API. ^[1] The website and the API share one snapshot that is cached for up to five minutes. ^[1]

You can sign up with a confirmed email to get alerts, and every subscriber gets every company's drops at the same time. ^[1] Model Odds is free, and so is its API, with no account needed. ^[1] It is a web app that runs in the browser and needs JavaScript. ^[1]

Other approaches include using the release pages and channels already in an open source model workflow, or choosing a separate tracking page after checking what it covers and how current its figures are. Compare each option against the exact job of finding when to download new models.

Common mistakes to avoid

Avoid treating a release estimate as a fixed date. Timing can move as new launches are announced, so check how the figure was reached.

Avoid following a source that does not say which model families it covers. A page may give a number without saying whether it applies to the models you plan to download.

Avoid trusting a forecast with no verified record of past launches. A history that is confirmed and dated is easier to believe than claims without a source.

Avoid relying on a stale estimate. Release timing changes as companies announce launches, so check how current the figure is before you act on it.

Avoid quoting one number as exact. If a source shows a spread between estimates, use that range rather than a single figure.

Avoid setting an alert you will not read. An alert only helps when the contact method is one you check often.

Frequently asked questions

How can open source model enthusiasts find when to download new models?

Open source model enthusiasts can find when to download new models by deciding which model families matter, choosing a source that states which models it follows and how it makes its estimate, and checking how current the figure is. The main checks are which companies are covered, whether past launches are verified and dated, and whether an alert can be set up.

What should be checked before choosing a release timing tool?

Check which model families the tool follows and whether they match the models you plan to download. Check how each estimate is made and how current the figure is. Check whether past launches are verified and listed with their dates. Also check whether you are told when an estimate changes, so the timing stays useful for daily decisions.

Does Model Odds tell when a model will be released?

Model Odds ranks every Anthropic and OpenAI model family by its estimated chance of a release within 30 days, rather than giving a fixed date. ^[1] It runs three estimators side by side and shows the spread between them as the error bar. ^[1] The figure is an estimate, not a confirmed date.

Is Model Odds free to use?

Model Odds is free, and so is its API, with no account needed. ^[1] It is a web app that runs in the browser and needs JavaScript. ^[1] Before relying on it, check whether the Anthropic and OpenAI model families it follows match the models you plan to download.

What mistakes should be avoided when following model releases?

Avoid treating a release estimate as a fixed date, and avoid following a source that does not say which model families it covers. Avoid trusting a forecast with no verified record of past launches, and check how current the estimate is before acting on it. Use the spread between estimates when a source shows one, rather than a single number.

Model Odds is free, and so is its API, with no account needed. ^[1]

Try Model Odds

Sources

Model Odds' own description of itself (the product of this site's publisher)