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Anthropic Google OpenAI

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Anthropic 29 Google 24 OpenAI 103
Google gemini-1.5-pro

Gemini 1.5 Pro (Google)

Retired

Yes — Gemini 1.5 Pro is retired. Google shut it down on June 17, 2026; API requests to it now fail. The recommended replacement is Gemini 2.5 Pro.

The model that made a million-token context window a mainstream expectation, and for a year the reason teams chose Gemini at all. Google no longer lists it on its deprecations page — rows are removed once a model is long gone — so the shutdown date here comes from the community llm-model-deprecation registry rather than a Google page, and should be treated as less firm than the rest of this site.

The facts

Provider Google
Model id gemini-1.5-pro
Also published as gemini-1.5-pro-002
Released February 15, 2024
Deprecated date not published
Shutdown June 17, 2026 · 43 days ago
Status Retired
Last verified July 30, 2026

What to use instead

Google Gemini 2.5 Pro gemini-2.5-pro Provider-recommended Shutdown scheduled

The 2.5 generation thinks before answering by default, which changes both latency and token cost. Budget with thinkingConfig rather than assuming 1.5-era numbers carry over.

What parameters gemini-2.5-pro accepts on modelparams.dev

Change the id

In most cases the migration is one string. Check the parameter differences before you ship — newer models drop older sampling knobs more often than they add them. 

- "model": "gemini-1.5-pro"
+ "model": "gemini-2.5-pro"

Frequently asked questions

When did Google Gemini 1.5 Pro shut down?
Google Gemini 1.5 Pro shut down on June 17, 2026. API requests to it fail from that date onward.
What replaces Google Gemini 1.5 Pro?
Google recommends Gemini 2.5 Pro (`gemini-2.5-pro`) as the replacement for Google Gemini 1.5 Pro. The 2.5 generation thinks before answering by default, which changes both latency and token cost. Budget with thinkingConfig rather than assuming 1.5-era numbers carry over.
Is Google Gemini 1.5 Pro still available?
No. Google Gemini 1.5 Pro is retired and no longer served — it was shut down on June 17, 2026. Calls to it return an error.
How do I migrate from Google Gemini 1.5 Pro to gemini-2.5-pro?
Swap the model id in your API call: replace "gemini-1.5-pro" with "gemini-2.5-pro". The 2.5 generation thinks before answering by default, which changes both latency and token cost. Budget with thinkingConfig rather than assuming 1.5-era numbers carry over.
Does this apply to gemini-1.5-pro-002 too?
Yes. Google Gemini 1.5 Pro is also published as `gemini-1.5-pro-002`; that id shares the same lifecycle dates as `gemini-1.5-pro`.

Sources

Found something wrong? The data is one YAML file — edit it on GitHub.

This page, for machines

JSON Markdown Badge JSON

Drop the badge in a README so it turns red the day this model is retired:

[![gemini-1.5-pro](https://img.shields.io/endpoint?url=https://modeldeprecations.dev/badge/google/gemini-1.5-pro.json)](https://modeldeprecations.dev/google/gemini-1.5-pro)

Other Google models

All Google deprecations →

How to use this site

The full guide, in Markdown. Copy it and hand it to a coding agent verbatim.

# How to use modeldeprecations.dev

[modeldeprecations.dev](https://modeldeprecations.dev) answers one question per model: is it deprecated,
when does it shut down, and what replaces it. Every date on the site is cited to the
provider's own documentation, with the date we last checked it.

Three lifecycle states are used, matching the providers' own vocabulary:

- **active** — still supported, no deprecation announced.
- **deprecated** — the provider has announced it is going away. It still answers requests.
- **retired** — API access is gone. Requests fail.

A model can be active *and* carry a shutdown date: Anthropic publishes a
"not sooner than" date and Google publishes an earliest shutdown date for models
that are current and fully supported. Those are reported as scheduled shutdowns,
not deprecations, because that is what the provider actually committed to.

## Check one model

```bash
curl https://modeldeprecations.dev/api/v1/models/openai/gpt-4-32k.json
```

Or fetch the page as Markdown, which includes the answer, the dates and the sources:

```bash
curl https://modeldeprecations.dev/openai/gpt-4-32k.md
```

## Full catalog

```bash
curl https://modeldeprecations.dev/api/v1/models.json
```

Ids are `provider/model`. Dated snapshots (`gpt-4-32k-0613`) are listed as aliases of
their canonical entry rather than as separate records.

Each entry carries `computed_status`, the lifecycle state recomputed against the build
date — so a model whose shutdown has passed reads `retired` without anyone editing data.

## Badges

A shields.io endpoint per model, for READMEs that should go red when a model dies:

```
https://img.shields.io/endpoint?url=https://modeldeprecations.dev/badge/openai/gpt-4-32k.json
```

## Calendar and changelog

- Subscribe to every shutdown date: https://modeldeprecations.dev/calendar.ics
- Changelog RSS: https://modeldeprecations.dev/changelog.xml

## JSON Schema

```bash
curl https://modeldeprecations.dev/api/v1/schema.json
```

## Contribute

Data lives in YAML under `models/{provider}/{model}.yaml` in the
[GitHub repo](https://github.com/mnfst/modeldeprecations.dev). Every lifecycle date needs a source URL; CI rejects a
date without one. Open a PR.

## For agents

- Machine-readable overview: https://modeldeprecations.dev/llms.txt
- This guide plus every model inline: https://modeldeprecations.dev/llms-full.txt
- Any page also exists as Markdown: append `.md` to a model URL.
- In a browser, this site registers a **WebMCP** tool on `navigator.modelContext`:
  `check_model_deprecation`, `list_shutdowns`, `find_replacement`, and `get_usage_guide`.
- Parameters each model accepts are catalogued on the sibling site, https://modelparams.dev.