Merge code snippets from an LLM into your existing codebase.
relace-apply-3, which is more performant on accuracy, speed, and long context requests.
| Model | Speed | Max Input | Max Output |
|---|---|---|---|
relace-apply-3 | ~10k tokens/s | 128k tokens | 128k tokens |
400 error code when input exceeds context limits (see table above). For these cases, GPT-4o-mini’s 1M token context window is a reliable fallback option.
However, even frontier LLMs struggle with long context. We recommend proactive refactoring of files >32k tokens to improve output quality.Relace API key Authorization header using the Bearer scheme.
Initial code and edits to apply
The original code that needs to be modified
The code changes to be applied to the initial code
Choice of apply model to use
relace-apply-3 Optional single line instruction for to disambiguate the edit snippet. e.g. Remove the captcha from the login page
Whether to stream the response back
Optional metadata for logging and tracking purposes.