AI Models & Companies · AI Model Releases and Versioning
Do Older AI Model Versions Get Shut Down After a New Release?
Older AI model versions are often kept available for some period after a new release rather than being shut down immediately, but most AI companies do eventually deprecate and retire older versions on a published timeline, so developers relying on a specific model version should check a provider's deprecation policy rather than assume indefinite support.
Key takeaways
- Many AI providers maintain older model versions for a transition period after a new release rather than removing them instantly.
- Eventually, most providers do retire older versions on an announced schedule, often called deprecation.
- Deprecation timelines and specific policies differ between providers and even between different models from the same provider.
- Developers building on a specific model version are generally advised to monitor deprecation announcements and plan migrations accordingly.
A Transition Period, Followed by Eventual Retirement
When an AI company releases a new model version, it doesn’t typically shut down the previous version the instant the new one becomes available. Most providers maintain older versions for some transition period, recognizing that developers and businesses have built applications relying on the specific behavior of a particular model version and need time to test and adapt before switching. However, this transition period isn’t indefinite — most AI companies do eventually retire older model versions on an announced schedule, a process generally referred to as deprecation, once they’ve determined the older version is no longer worth maintaining alongside newer, generally better-performing releases.
This pattern is fairly standard across much of the software industry, not unique to AI, but it carries particular weight in this context because AI models are being released at a notably fast pace, meaning deprecation cycles can come around more frequently than developers might be used to with more slowly evolving software categories.
Why Providers Don’t Support Every Version Forever
Maintaining multiple versions of a model simultaneously requires ongoing infrastructure, computing resources, and support effort from the provider. As newer, generally improved versions become available, the cost and complexity of continuing to support many older versions indefinitely tends to outweigh the benefit, particularly once usage of an older version has substantially declined in favor of newer ones. This is why providers generally set deprecation timelines rather than committing to indefinite support for every version they’ve ever released.
What This Means for Developers Relying on a Specific Version
For developers and businesses building applications on top of a specific AI model version through an API, deprecation policies are a practical, ongoing consideration rather than a one-time concern. Staying aware of a provider’s announced deprecation timeline for the specific model version in use, and planning to test and migrate to a newer version well ahead of the retirement date, is a standard part of maintaining an AI-powered application over time. Providers generally publish this information directly in their developer documentation, making it the most reliable source to consult rather than assuming any particular timeline.
Bottom Line
Older AI model versions are usually kept available for a transition period after a new release, but most providers do eventually deprecate and retire them on an announced schedule, making it important for developers relying on a specific version to track deprecation policies and plan migrations rather than assuming indefinite support.
Important caveats
- Specific deprecation timelines and policies change and should be checked directly against a provider's current documentation.
- Consumer-facing chat products and developer-facing APIs sometimes follow different retirement timelines even from the same company.
Frequently asked questions
How much advance notice do AI companies typically give before retiring a model version?
Practices vary by provider, but many AI companies that offer models through a developer API publish a deprecation policy specifying a notice period before an older model version is retired, giving developers time to test and migrate to a newer version; checking a specific provider's current policy is the best way to know the applicable timeline.
What happens to an application built on a model version that gets deprecated?
If a developer doesn't migrate before a deprecation date, requests to that specific model version typically stop working once it's retired, which is why monitoring deprecation announcements and testing a migration to a newer version ahead of the retirement date is an important practice for anyone building on a specific AI model.
Are consumer chat apps affected by model deprecation the same way as developer APIs?
Not necessarily in the same way — consumer-facing chat products often transition users to newer models more seamlessly as part of normal app updates, while developer-facing APIs typically require the developer to explicitly update their own code to call a newer model version, making deprecation timelines more directly actionable for developers than for typical consumer users.
Related questions
- How Should You Decide Whether to Upgrade to a New AI Model Version?
- What Does It Mean When an AI Model Is Labeled 'Preview' or 'Beta'?
- Why Do AI Companies Release New Model Versions So Frequently?
- What Is a 'Model Card' and Why Do AI Companies Publish Them?
- What Are the Advantages of Open-Source AI Models Over Closed Ones?
- How Often Should You Re-Evaluate Your AI Provider Choice?
Sources
- [1]Model deprecation and lifecycle documentation — OpenAI
- [2]Model deprecation and lifecycle documentation — Anthropic
Written by Editorial Team
Last updated July 25, 2026
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