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# GitHub Copilot CLI gets a cross-model review agent called Rubber Duck

**[Collections](https://daily.dev/sources/collections)** · 2 min read · 1 upvotes · 0 comments

## Summary

GitHub Copilot CLI has added an experimental feature called Rubber Duck that uses a second AI model from a different family to review the primary agent's work before execution. When Claude Sonnet 4.6 is the primary orchestrator, GPT-5.4 acts as the reviewer. The reviewer triggers automatically after plan generation, complex multi-file implementations, and test writing. On SWE-Bench Pro, this pairing closed 74.7% of the performance gap between Sonnet and Opus, with the biggest gains on complex multi-file problems. The core insight is that self-review is ineffective because a model that made a flawed assumption will likely defend it — a different model family provides genuinely independent scrutiny at Sonnet-level pricing.

## Content

GitHub has added an experimental feature to Copilot CLI called Rubber Duck: a review agent that uses a model from a *different* AI family to critique the primary agent's plans before anything gets executed.

The setup is straightforward. When Claude Sonnet 4.6 is the primary orchestrator, GPT-5.4 acts as the reviewer. The two models don't share a lineage, which is the whole point — a model reviewing its own output tends to miss the same things it missed the first time. Bringing in a different model family means genuinely independent scrutiny.

## How it works

Rubber Duck triggers automatically at three checkpoints:

- After the primary agent produces a plan
- After complex multi-file implementations
- After writing tests

It can also be invoked manually. The reviewer's job is to look for missed details, questionable assumptions, and edge cases — not to rewrite anything, just to flag problems before they propagate.

## The benchmark numbers

On SWE-Bench Pro, pairing Claude Sonnet 4.6 with Rubber Duck running GPT-5.4 closed 74.7% of the performance gap between Sonnet and Opus. The biggest gains showed up on complex multi-file problems, which makes sense — that's exactly where a flawed premise in step one becomes expensive by step ten.

The cost argument is also worth noting: you get most of Opus-level performance at Sonnet-level pricing.

## Why this matters

AI coding agents fail in a specific, annoying way. They don't usually produce obviously broken code. They produce polished, internally consistent output built on a wrong assumption. By the time you notice, the assumption has been baked into a dozen files.

Self-review doesn't fix this. A model that made a flawed assumption in planning will likely defend that assumption when asked to review its own work. A different model, with no stake in the original plan, is more likely to push back.

The feature is available now via the `/experimental` slash command in GitHub Copilot CLI. Whether it stays useful depends on staying targeted — if it becomes a rubber-stamp ceremony that fires on every trivial change, the signal disappears fast.

## Similar posts on daily.dev

- [Rubber Duck in GitHub Copilot CLI](https://daily.dev/posts/rubber-duck-in-github-copilot-cli-h1kpggec0) · The Art of Simplicity · 1 upvotes · 1 comments
- [Rubber Duck in GitHub Copilot CLI now supports more models](https://daily.dev/posts/rubber-duck-in-github-copilot-cli-now-supports-more-models-d96bkcovt) · GitHub Changelog · 1 upvotes · 0 comments

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Tags: [#github](https://daily.dev/tags/github), [#claude](https://daily.dev/tags/claude)

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