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GLM 5.2 Performance Benchmarks

GLM-5.2 (max) - Intelligence, Performance & Price Analysis

artificialanalysis.ai

June 17, 2026

5 min read

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55/100

Summary

GLM-5.2 (max) is a leading model in intelligence with a score of 51 on the Artificial Analysis Intelligence Index. It offers a 1 million token context window, supports text input and output, is faster than average, but is considered expensive compared to other open weight models of similar size.

Key Takeaways

GLM-5.2 (max) scores 51 on the Artificial Analysis Intelligence Index, significantly above the average score of 24 for comparable models.
The pricing for GLM-5.2 (max) is $1.40 per 1M input tokens and $4.40 per 1M output tokens, making it more expensive than the average prices of $0.42 and $1.25, respectively.
GLM-5.2 (max) generates output at a speed of 108 tokens per second, which is faster than the average speed of 60 tokens per second for similar models.
The model has a context window of 1 million tokens, allowing for extensive text input and output capabilities.

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Community Sentiment

Mixed

Positives

GLM 5.2 excels in the 'AA-Omniscience Non-Hallucination Rate', outperforming models like DeepSeek and GPT 5.5, which highlights its reliability in uncertain scenarios.
The model's performance improvements, being 30% faster than its predecessor, indicate significant advancements in efficiency that could enhance user experience.
Local models are becoming increasingly useful, suggesting a shift towards more accessible AI solutions that could democratize technology for a wider audience.

Concerns

Concerns arise over the validity of the 'AA-Omniscience Non-Hallucination Rate' benchmark, as it seems to favor models that avoid complex reasoning, potentially misleading users about true performance.
Skepticism about the benchmarks is prevalent, especially when models like Muse Spark are rated higher than GPT-5.5, raising doubts about the accuracy of the evaluations.
The high costs associated with deploying these models on local hardware may hinder accessibility, limiting their practical use for many developers and organizations.