Muse Glimmer vs. Muse Spark 1.1: Evaluating Meta’s Latest Models

What the benchmarks show

When comparing the performance metrics of Muse Glimmer and Muse Spark 1.1, the disparity in their intended use cases becomes clear. Muse Spark 1.1, released on July 9, 2026, demonstrates a significant lead in core intelligence and technical aptitude. With an intelligence index of 53.2 and a coding index of 71.3, it consistently outperforms Muse Glimmer, which holds an intelligence index of 35.1 and a coding index of 49.

This performance gap is reflected across standardized benchmarks. Muse Spark 1.1 achieves a GPQA score of 0.898 compared to Glimmer’s 0.835, and shows a more pronounced lead in HLE (0.462 vs. 0.22) and SciCode (0.582 vs. 0.436). The LCR scores remain relatively close, with Spark 1.1 at 0.813 and Glimmer at 0.8. While both models lack published math index data, the available benchmarks suggest that Muse Spark 1.1 is better equipped for complex, multi-step reasoning and software development tasks, whereas Muse Glimmer is positioned as a more generalized, lower-intensity model.

Speed and cost

Economic considerations are perhaps the most defining factor in this comparison. Muse Glimmer is currently offered with no input or output costs, making it an attractive option for developers looking to scale applications without incurring per-token fees. In contrast, Muse Spark 1.1 is Meta’s first paid frontier model API, carrying a blended cost of $2.00 per million tokens. This includes an input price of $1.25 and an output price of $4.25 per million tokens.

Regarding raw performance speed, both models currently lack public data concerning output tokens per second or time-to-first-token. Users should account for the fact that while Glimmer is free, it may not offer the same latency optimization or throughput consistency as the paid Spark 1.1 tier, which Meta has specifically engineered for agentic tasks.

Which model fits which workflow

Muse Spark 1.1 is explicitly designed for agentic workflows—tasks where the AI must interact with tools, manage complex logic, and execute multi-step reasoning. Its higher intelligence and coding indices make it the superior choice for backend development, automated research, and technical analysis where accuracy is paramount. The investment in the $2.00/1M token blended rate is justified when the model’s reasoning capabilities reduce the need for human oversight or iterative corrections.

Conversely, Muse Glimmer is best suited for high-volume, low-complexity tasks where the cost of the model could otherwise become a bottleneck. Its zero-cost structure makes it ideal for experimental projects, internal prototyping, or large-scale data processing where the higher reasoning capabilities of Spark 1.1 are not strictly required. By choosing Glimmer, organizations can manage high-frequency requests without the financial pressure of a paid API.

Decision takeaway

Choosing between these two models requires a clear assessment of your project's technical requirements versus your operational budget. Muse Spark 1.1 is a high-performance tool built for precision, while Muse Glimmer is a cost-effective utility built for accessibility. If your application relies on high-fidelity reasoning or complex coding, the performance gains of Spark 1.1 are likely to outweigh its costs. If your application is cost-sensitive and the tasks are routine, Glimmer provides a robust, zero-cost foundation.