Agentic AI Comparison:
GLM‑4.5 vs TEN Framework
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Introduction
This report provides a detailed comparison between the TEN Framework and GLM-4.5 agents across five key metrics: autonomy, ease of use, flexibility, cost, and popularity. Both represent advanced open-source technology, but differ in target applications and design priorities.
Overview
GLM‑4.5
GLM-4.5 by Zhipu AI is a flagship open-source language model optimized for agentic tasks, reasoning, coding, and vision. It features Mixture-of-Experts architecture for efficiency, supports multi-language and multi-modal use cases, and includes native tooling for agentic workflows. It is recognized for competitive performance in benchmark evaluations.
TEN Framework
TEN Framework is an open-source agentic software stack focused on enabling end-to-end autonomous agents. It offers a modular design for building, orchestrating, and deploying multi-agent workflows that integrate language models, tools, and external systems. It is intended for teams and developers seeking highly customizable AI solutions.
Metrics Comparison
autonomy
GLM‑4.5: 9
GLM-4.5 is purpose-built for agentic autonomy, with chain-of-thought reasoning, native function calling, and tool-use capabilities. Independent benchmarking places it among the top three in agentic performance, matching leading models in autonomous reasoning and adaptive behavior.
TEN Framework: 8
TEN Framework is designed for building highly autonomous multi-agent systems. Its architecture supports independent decision-making, orchestration, and complex workflows. While autonomy depends on developer configuration, it provides advanced primitives for autonomous operations.
GLM-4.5 generally demonstrates higher direct autonomy out of the box, while TEN Framework delivers greater autonomy in customized orchestration scenarios.
ease of use
GLM‑4.5: 8
GLM-4.5 provides ready-to-use models with Python libraries, standardized tool calling formats, and out-of-the-box support for agentic and coding tasks. Its documentation and ecosystem lower the entry barrier for practical agent deployment.
TEN Framework: 6
TEN Framework prioritizes extensibility and customization, which can make initial setup and use complex. Effective use requires software engineering skills and detailed configuration of agent workflows.
GLM-4.5 is easier for most users to deploy and experiment with, while TEN Framework demanding more specialized setup and domain knowledge.
flexibility
GLM‑4.5: 7
GLM-4.5 supports multi-modal tasks, tool calling, and works well with coding and vision applications. Its Mixture-of-Experts architecture aids adaptive task handling, but flexibility is constrained by its design as a model, not a workflow stack.
TEN Framework: 9
TEN Framework's modular components allow for high flexibility in agent design, orchestration, and extension. It can integrate diverse models and tools and supports custom workflows tailored to enterprise or research needs.
TEN Framework is better suited for organizations demanding bespoke agent workflows; GLM-4.5 is flexible within the model's supported tasks but less so for custom orchestration.
cost
GLM‑4.5: 9
GLM-4.5 is open-source and budget-friendly, with competitive pricing when used via API: $0.11 per million input tokens and $0.28 per million output tokens, cheaper than DeepSeek and comparable to Google's best offerings.
TEN Framework: 10
TEN Framework is free and open-source, with no licensing or pay-per-use fees. Operational costs are limited to infrastructure and optional integrations, typically determined by deployment scale.
Both are highly cost-efficient; TEN Framework has no direct costs, while GLM-4.5 is notably economical for cloud-scale inference.
popularity
GLM‑4.5: 8
GLM-4.5 receives significant attention in the AI research and developer communities as a leading Chinese LLM with strong benchmark results and a growing ecosystem. It is frequently cited and adopted for both agentic and coding tasks.
TEN Framework: 5
TEN Framework is a niche solution with modest adoption in the open-source agent development community. It has fewer GitHub stars and less visibility than prominent language models, limiting its mainstream popularity.
GLM-4.5 is substantially more popular internationally and in mainstream AI applications versus TEN Framework, which targets a specialized user base.
Conclusions
GLM-4.5 stands out for its competitive autonomy, user-friendliness, cost performance, and international popularity as an agentic language model. TEN Framework is optimal for highly flexible, custom enterprise or multi-agent applications, especially for teams prioritizing workflow control and full-stack autonomy. GLM-4.5 is recommended for most direct agentic and coding tasks; TEN Framework is preferable where orchestration or unique agent architectures are required.