GLM-5.2 Emerges as Top Open-Weight Model: Performance vs. Cost Analysis
The new GLM-5.2 model leads the Artificial Analysis Intelligence Index, offering the best intelligence score among open-weight models on the Intelligence vs. Cost per Task frontier. Here’s how to evaluate its cost-efficiency for your AI workflows.
Practical Summary
Z.ai has released GLM-5.2, an open-weight model (MIT license) that achieves the highest intelligence score (51) on the Artificial Analysis Intelligence Index among open-weight models. It is positioned on the Pareto frontier for intelligence vs. cost per task, meaning it offers the best intelligence at its cost tier. Its pricing, cost-per-task metrics, and wide availability through multiple providers make it a concrete option for teams evaluating model cost optimization.
Why It Matters
For businesses building or deploying AI, this update provides hard data to compare a top-tier open-weight model's performance against cost. Understanding its cost-per-task ($0.46) and positioning on the efficiency frontier helps in selecting models for revenue-generating applications where both capability and operational cost are critical factors.
How to Evaluate GLM-5.2 for Cost-Optimized AI Workflows
When assessing a new model like GLM-5.2, the key is to move beyond raw intelligence scores and analyze its practical value. The goal is to determine if the performance gain justifies the cost for your specific revenue-generating tasks.
Step 1: Benchmark Intelligence Against Your Needs. GLM-5.2 scores 51 on the Artificial Analysis Intelligence Index (v4.1), leading open-weight competitors like MiniMax-M3 (44) and DeepSeek V4 Pro (44). Review the specific sub-benchmarks where it improved most, such as scientific reasoning (+16 points on CritPt) and terminal operation (+16 points on TerminalBench v2.1). These gains indicate particular strength in complex, multi-step tasks.
Step 2: Calculate the True Cost per Task. The model’s intelligence comes with a cost. GLM-5.2 uses an average of 43,000 output tokens per task, which is higher than many competitors. At its pricing ($1.40 per 1M input tokens, $4.40 per 1M output tokens, $0.26 per 1M cache hit tokens), the total cost per task is ~$0.46. Compare this directly to the cost per task of your current models (e.g., GLM-5.1 at $0.25, DeepSeek V4 Pro at $0.05).