Gemini URGENT TRIVIAL 24 days left

gemini-2.0-flash — Deprecated

Deprecated
Shutdown
2026-06-01
Status
deprecated
Replacement
gemini-2.5-flash

Action required

Production code calling gemini-2.0-flash will return errors after 2026-06-01.

Quick fix — copy & paste

Choose your language. The "before" block matches the deprecated call; the "after" block is the drop-in replacement.

Breaks on 2026-06-01
model = genai.GenerativeModel("gemini-2.0-flash")
response = model.generate_content("Hello")
Use this instead
model = genai.GenerativeModel("gemini-2.5-flash")
response = model.generate_content("Hello")

Error messages

Seeing one of these? You're in the right place.

  • model_not_found: gemini-2.0-flash
  • Gemini 2.0 Flash is deprecated

Replacement options

Other Gemini deprecations

What this means for your code

gemini-2.0-flash is a general-purpose chat model used through the standard messages or chat completions endpoint. Most production traffic on chat models comes from streaming responses, function calling, and tool use. After the shutdown date, every API call returns an error and your app breaks for end users until the model id is updated.

Gemini has scheduled gemini-2.0-flash for shutdown on 2026-06-01. That gives you 24 days to migrate. Until then the model still works, but every API call after that date will return a model_not_found error.

Find every call in your codebase

Before you change anything, locate every place the deprecated model id is referenced. Search source files, environment files, feature flags, and config repos. Use these commands from your project root:

Python projects

grep -rn '"gemini-2.0-flash"' --include="*.py" .

JavaScript / TypeScript projects

grep -rn '"gemini-2.0-flash"' --include="*.{js,ts,tsx,jsx}" .

Anywhere (configs, scripts, infra)

grep -rn "gemini-2.0-flash" .

Migration checklist

Steps in order. Skip any that don't apply, but read the whole list — for chat models, the non-obvious steps are usually the ones that break in production.

  1. 1. Search for the deprecated model id in your application code, environment variables, and feature flags
  2. 2. Update the model id in your API client configuration
  3. 3. Re-run integration tests that exercise streaming, function calling, and structured outputs
  4. 4. If you use prompt caching, verify the new model supports the same cache scopes
  5. 5. Compare token costs on a representative sample of prompts before deploying

Will this migration cost more?

Switching from gemini-2.0-flash to gemini-2.5-flash could change your costs significantly. Calculate the exact difference for your prompts.

Open the cost calculator →