Replicate Release Notes

How it works

Set allow_fallback_model to true when calling the API. If Nano Banana Pro hits a rate limit, it tries to generate the image with Seedream 5.0 lite instead. For certain inputs, for example if the aspect ratio isn’t supported, the original rate limit error is returned.

The fallback is off by default. If you don’t set allow_fallback_model, nothing changes — you’ll get a rate limit error when Google’s API is at capacity.

When the fallback is triggered, your logs still show a prediction to Nano Banana Pro. You can tell the fallback was used by checking the resolution field in your output — it says "fallback" instead of the actual resolution. You’re charged the cost of the fallback model, not Nano Banana Pro.

Limitations

Our current fallback model, Seedream 5.0 lite, doesn’t support all the same options as Nano Banana Pro:

• Seedream 5.0 lite doesn’t support 1K resolution. If you request 1K, the fallback generates at 2K and downscales the result.
• Seedream 5.0 lite doesn’t support 4K resolution. If you request 4K, the fallback won’t be used and the original rate limit error is returned.
• Seedream 5.0 lite doesn’t support the 4:5 and 5:4 aspect ratios. Requests with these ratios won’t fall back and will return the original rate limit error.

MCP server discovery

Replicate’s MCP server can now be discovered automatically through the official MCP Registry.
We added a /.well-known/mcp/server.json endpoint that publishes metadata about the MCP server. This follows the server.json specification from the Model Context Protocol.

How discovery works

The MCP Registry is the official metadata repository for MCP servers, backed by Anthropic, GitHub, and Microsoft. It doesn’t host code—just metadata that describes where to find servers and how to install them.
When you publish a server.json file at /.well-known/mcp/server.json, the Registry can discover your server automatically. MCP clients then use the Registry to find and install servers.

Clients with built-in discovery

A few MCP clients have built-in marketplaces or directories:

• VS Code has the best Registry integration. Enable chat.mcp.gallery.enabled in your settings, then search @mcp in the Extensions view to browse and install MCP servers.
• Claude Desktop has a curated extensions directory at Settings > Extensions > Browse extensions.
  Other clients like ChatGPT, Cursor, and LM Studio require manual configuration—you add the server URL or edit a config file yourself.

Code mode option

The metadata also exposes the --tools flag, which lets you choose between standard tools (all) or code mode (code) when installing.

Filter list predictions API by source

You can now filter the list predictions API endpoint to show only predictions created through the web interface.

Use the source query parameter with a value of web :

curl -s \
-H "Authorization: Bearer $REPLICATE_API_TOKEN" \
"https://api.replicate.com/v1/predictions?source=web"

This is useful if you want to see predictions you created using the playground or other parts of the Replicate website, separate from predictions created programmatically via the API.
Note: When filtering by source=web , results are limited to predictions from the last 14 days.

Web

• Added created and last updated dates on model pages
• Improved pricing display on model pages with clearer messaging for bring-your-own-token models and better visual consistency for single and multiple pricing tiers
• Made FLUX.2 the default model in the playground
• Added FLUX.2 models (pro, flex, and dev) to the playground
• Improved homepage hero section with clickable hero images that link to model pages, better mobile responsiveness, and reduced bundle size
• Improved mobile homepage hero legibility by adjusting gradient overlays
• Fixed model card text truncation and spacing on the explore page at smaller viewport sizes
• Updated playground to better adhere to platform rate limits
• Improved data retention banner messaging to clarify that failed predictions are not retained indefinitely
• Fixed support form reliability issues
• Fixed an issue where the navigation may have rendered incorrectly on mobile devices

Docs

• Published blog post on how to run FLUX.2 on Replicate, Black Forest Lab’s most advanced image generation model
• Published blog post on how to run Isaac 0.1 on Replicate, an open-weight vision-language model

Web

• Added approximate cost display to predictions and trainings on dashboard and predictions pages, showing how much each run costs
• Launched an updated homepage hero section and navigation
• Improved display of code snippets on the Replicate homepage
• Made Nano Banana Pro the default model in the playground
• Improved site search sorting, and improved keyboard shortcut handling

Cog

• Released Cog v0.16.9 with a fix for x-order bug

Docs

• Published blog post on how to prompt Nano Banana Pro with guidance on using its logic, text rendering, character consistency, and world knowledge capabilities

Code mode

Replicate’s local MCP server now supports an experimental “code mode” that allows language models to write and execute TypeScript code directly in a sandboxed environment.

Instead of exposing individual API operations as separate tools, Code mode provides two tools: one for searching SDK documentation, and another for executing TypeScript code using the Replicate SDK within a Deno sandbox. The model uses a built-in docs search tool to learn how to write code against the SDK. This approach is more efficient for complex workflows that involve multiple API calls, as it reduces context window usage and allows the model to write custom logic that calls multiple methods and returns only the final results.

To use code mode, start the local MCP server with the --code-mode flag:

npx -y replicate-mcp@alpha --tools=code

Here’s how to add the local Replicate MCP server in code mode as a tool in Claude Code:

claude mcp add "replicate-code-mode" --scope user --transport stdio -- npx -y replicate-mcp@alpha --tools=code

Code mode is currently experimental and subject to change. It requires Node.js and Deno to be installed locally. Remote cloud sandboxing support is planned but not yet available.

To get started with Code mode, see the Code mode documentation or visit the demo GitHub repo.

Web

• Added video support to the before/after slider, allowing side-by-side comparison of video outputs from models like video upscaling and style transfer
• Added pixelated image rendering with size controls (1x, 2x, fit) in the playground for pixel art models
• Improved support for aspect ratios in prediction outputs
• Overhauled focus states across the Replicate UI library
• Add search to Deployments
• Fixed a bug that caused model input fields to display in a different order
• Added FAQs to every collection page

Platform

• Launched deployment setup monitoring for enterprise customers with automatic email notifications when deployments fail setup and customizable setup timeouts

Docs

• Published blog post on how to run FLUX.2 on Replicate, Black Forest Lab’s most advanced image generation model
• Published blog post on how to run Isaac 0.1 on Replicate, an open-weight vision-language model

Enterprise deployment controls

Setup failure notifications

If a deployment fails during the model’s setup function, we’ll notify you via email. This helps you catch issues with your model earlier, before your users reach out.

If you use Slack, you can configure these emails to be sent to a Slack channel using their send emails to Slack feature.

Custom setup timeouts

You can customize the timeout for your deployment’s model setup function. The default timeout is 10 minutes, which works for most models. But if your model needs to download large files, load trained weights, or perform other expensive initialization operations, you can give it more time before we mark it as failed.

You can configure both of these in your deployment settings.

Read more in our deployment monitoring docs or reach out to [email protected] to learn about our enterprise plans.

SDK

• Our Python SDK public beta is out now, making it even easier to run AI models from your Python code with full support for every operation in our HTTP API

Web

• Improved playground handling of different output and streaming types
• Fixed search keyboard shortcut in docs to use Cmd+/ on Mac and Ctrl+/ on Windows for consistency across platforms

API

• Launched prediction deadlines so you can automatically cancel predictions that don’t complete within a specified duration
• Added source field to prediction API responses to indicate whether predictions were created via web or api

Docs

• Split deployment monitoring into its own docs section with detailed information about metrics, GPU memory monitoring, and performance tracking
• Published blog post about how to prompt Veo 3.1 with guidance on reference images, frame control, and image-to-video features