Meta Introduces Muse Wonder, A New AI Model for Code Editing, Scoring 46.1% on CursorBench

Introduction

Meta Superintelligence Labs has a new model in one of the most competitive rooms in AI: the code editor. Muse Wonder is now available inside Cursor, and it posted a score of 46.1% on CursorBench. That is a respectable debut, not a coronation. The current leader on the same benchmark sits more than 11 points higher, so Meta’s entry joins the race somewhere in the middle of the pack.

What Muse Wonder Brings to Cursor

Cursor is the AI-powered code editor built by Anysphere. It lets developers choose which underlying model does the heavy lifting, which makes it a kind of open audition stage for AI labs. Meta Superintelligence Labs released Muse Wonder on October 12, 2026, alongside a more capable sibling called Muse Wonder Full. Both are pitched at agentic work, meaning tasks where the model has to keep a goal in mind across many steps instead of answering one question and stopping.

Benchmarking Performance

The 46.1% figure comes from CursorBench 4.0, using the model’s ‘High’ configuration. Turned up to its ‘Max’ setting, Muse Wonder can reach as much as 50.4%. For comparison, Claude Opus 5.5 leads at 57.8% in its maximum effort mode. Even at full throttle, Meta’s model trails that mark by 7.4 points. The research summary describes the result as competitive among mid-tier models and favorable against several other frontier systems. CursorBench itself is fairly new. Version 4.0 was introduced on September 10, 2026, roughly a month before Muse Wonder launched, and it focuses on long-horizon coding tasks drawn from real-world software development scenarios. Meta’s own internal evaluations point to strengths in long-horizon coding and agent workflows.

Pricing and Context Window

Pricing through the Meta Model API is set at $4 per million input tokens and $20 per million output tokens. To support that, Muse Wonder ships with a 1M-token context window. Tokens are the chunks of text a model reads, so a window that large lets it hold a sizable codebase or document set in working memory at once. The model also accepts multimodal input, including text, images, video and audio.

Conclusion

The Muse Wonder models are built on Meta’s earlier Muse Spark series. They arrive as part of a broader effort to sharpen the company’s AI offerings following a significant restructuring of its AI division in mid-2025.