How I would evaluate Muse Wonder in Cursor

Muse Wonder is Meta’s coding model in Cursor. You can select it for agent work, but adding another model raises a practical question: when would you choose it over the one you already use?

I haven’t run my own comparison yet. This guide explains how I would evaluate it, using Cursor’s model documentation for the product details checked in October 2026.

What you’re choosing

Cursor provides the editor and the tools. Muse Wonder supplies the model making decisions about those tools.

The documented context window is 300,000 tokens, or one million in Max Mode. The model accepts image input, which matters when an agent needs to inspect a screenshot.

Reasoning effort is another choice. Cursor offers levels from minimal through max, with high as the default. More reasoning can spend more tokens, so I would record the effort level alongside the model name when comparing results.

If two runs use different effort levels, they are still useful experiments. You just need to know which settings produced each result.

Read the benchmark in context

Cursor publishes CursorBench, an evaluation built from ambiguous tasks involving multiple files. At the time I checked, its current page was CursorBench 4.0, with separate rows for different reasoning levels.

That is useful evidence about performance in Cursor’s evaluation. It doesn’t tell you whether a model will handle your application’s authorization rules, or whether you will like its changes.

Before comparing a score from an announcement with today’s table, check the benchmark version and effort level. Otherwise you can end up comparing different tests.

My next step would be to give the model a small sample of work from a real project.

Choose tasks with observable results

Start with a bug you can reproduce. For example, a form that loses the user’s input after a failed save. Write down the failure and the result you expect before asking either model to fix it.

For a second task, use a small feature that crosses a boundary: adding an optional field that has to survive validation and storage. This tests whether the agent follows data through the application.

Give both models the same starting commit, instructions, and access to tools. Keep experiments separate according to your project’s working-directory rules. Don’t let one run inherit the other model’s solution.

For each result, record whether the task works and how much correction it needs. Include review time. A fast response that takes half an hour to repair can be a poor choice for your work.

Also note unnecessary changes. If the task only needs a validation fix, a broad rewrite makes the result harder to trust.

Work out what a run costs

Cursor lists Muse Wonder at $4 per million input tokens and $20 per million output tokens. Cached input is $0.20 per million tokens. These rates were checked in October 2026 in the model’s pricing section.

For a deliberately simple example, suppose a run uses 100,000 uncached input tokens and 10,000 output tokens:

This is arithmetic using hypothetical token counts, not a measured coding task. Real runs can include repeated model calls and cached input. Use the usage details from the actual run when available.

Cursor’s billing treatment also depends on your plan. Its documentation currently places Muse Wonder outside the included usage on The X Subscription, charging prepaid credit; legacy plans use the Other Models pool. Check your own account before assuming the experiment is included.

Decide where it helps

I would keep a small comparison note with the task, effort level, result, cost, and the corrections I had to make.

That makes it possible to choose a model for a particular kind of work. Perhaps one handles an unfamiliar codebase well, while another produces smaller patches for everyday fixes. A few successful runs won’t establish a universal ranking, but they can support a useful decision for your project.

If Muse Wonder fails a task, keep the failure details too. “Missed the server-side check” is something you can test again after an update. “Didn’t feel as good” gives you very little to work with.

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