GLM-5.2 Performance Analysis

GLM-5.2 is analyzed by Artificial Analysis to determine its intelligence, performance, and pricing efficiency. The evaluation focuses on the model's ability to handle agentic real-world work tasks, coding, and terminal use, while measuring the trade-offs between intelligence and operational cost.

Intelligence and Agentic Capabilities

GLM-5.2 is measured using the Artificial Analysis Intelligence Index v4.1, which incorporates nine distinct evaluations including GDPval-AA v2, τ³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, and AA-LCR.

Key intelligence metrics include:

  • Agentic Work Tasks: Measured via (Elo-500)/2000.
  • Agentic Coding: Evaluation of the model's ability to use terminals and perform coding tasks.
  • Agentic Tool Use: Assessment of the model'ss ability to integrate with and utilize external tools.

Performance and Latency Metrics

Artificial Analysis evaluates the speed and responsiveness of GLM-5.2 through several key performance indicators:

  • Output Speed: Measured in tokens per second, representing the generation speed after the first chunk is received.
  • Time to First Token (TTFT): The latency between the API request and the first answer token, which includes "thinking" time for reasoning models.
  • End-to-End Response Time: The total time required to output 500 tokens, combining input time, thinking time (for reasoning models), and generation speed.
  • Time per Intelligence Index Task: The weighted average wall clock time (in minutes) per task, excluding TTFT and execution time.

Cost and Pricing Structure

GLM-5.2's economic efficiency is assessed by comparing the cost per Intelligence Index task against its intelligence score. This is calculated based on a blended rate of input, cache hit, cache write, and reasoning tokens.

  • Pricing Components: The model's pricing is segmented by input tokens, output tokens, and cache hit prices (USD per million tokens).
  • Cost to Run Index: The total USD cost to execute all evaluations within the Artificial Analysis Intelligence Index.
  • Token Efficiency: The weighted average number of output tokens used to complete a single task in the Intelligence Index.

Context Window and Model Architecture

GLM-5.2 supports a context window that determines its capacity for Retrieval Augmented Generation (RAG) workflows, which require processing large amounts of data for reasoning and information retrieval.

For open-weights versions of the model, Artificial Analysis tracks both total parameters (the total number of trainable weights) and active parameters (the parameters executed during each inference forward pass), which is particularly relevant for Mixture of Experts (MoE) architectures where active parameters are fewer than total parameters.

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