Meta's Muse Spark 1.1 outperforms GLM-5.2 in coding and costs slightly less

2026-07-11 · Source: The Decoder · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Intermediate, quick

Summary

Meta's new Muse Spark 1.1 model, released on July 11, 2026, has demonstrated superior performance in coding tasks and offers a more competitive price point compared to rivals like GLM 5.2 and GPT-5.4. The model achieved an Intelligence Index score of 51, matching GLM 5.2, GPT-5.4, and GPT-5.6 Luna. Notably, Muse Spark 1.1 improved by eight points in three months, primarily in coding and agent-based knowledge work, scoring 71.3 on the Coding Index, surpassing GLM 5.2 (68.8) and nearly matching GPT-5.6 Luna (71.4). Priced at an estimated \$0.26 per task, it is significantly cheaper than GLM-5.2 (\$0.37) and GPT-5.4 (\$0.89), while using fewer output tokens (94 million vs. 141 million for GLM-5.2). Furthermore, its hallucination rate decreased from 73% to 38%, and its context window expanded to one million tokens. It is currently available exclusively via Meta's API.

Key takeaway

For AI Engineers evaluating large language models for coding and agent-based knowledge work, you should consider Muse Spark 1.1. Its strong Coding Index score of 71.3 and significantly lower cost (\$0.26 per task) versus GLM-5.2 (\$0.37) offer compelling value. The improved hallucination rate and one million token context window further enhance its utility for robust application development. Integrate it via Meta's API to optimize your project budgets and performance.

Key insights

Meta's Muse Spark 1.1 offers competitive coding performance and significantly lower costs, alongside improved reliability and context window.

In practice

• Evaluate Muse Spark 1.1 for coding tasks.
• Consider cost savings for high-volume API use.
• Utilize expanded context for complex prompts.

Topics

• Muse Spark 1.1
• Large Language Models
• Coding Benchmarks
• Model Performance
• API Pricing
• Hallucination Rate
• Context Window

Best for: CTO, VP of Engineering/Data, AI Architect, AI Engineer, Machine Learning Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Decoder.