Introduction

The release of GLM-5-Turbo signifies a paradigm shift in large models from Chat to Agent. Zhiyu AI has not only increased parameter scale but also enhanced the model’s ability to execute long-sequence tasks through the Slime training framework. This article deeply analyzes how agent models break the limitations of chat models and how they will reshape developers’ workflows and industry ecology.

A Rapid Evolution: Why Two Generations in One Month?

Just yesterday, I was using GLM-5 to have Lobster assist me in researching, writing articles, and organizing code. Today, Zhiyu AI has released GLM-5-Turbo. It has only been a month since the launch of GLM-5. More importantly, it is positioned as the world’s first general-purpose large model specifically optimized for the OpenClaw scenario.

This is not an “upgrade”; it is a “transformation”. As a deep user of OpenClaw, my first reaction is not about the performance improvement but rather: Why does the base model need to be specifically optimized for agent scenarios? The answer to this question holds the key to the next paradigm shift in large model development—from Chat (dialogue) to Act (execution).

From Chat to Act: A Generational Change

The Inherent Limitations of Chat Models

For the past two years, we have been accustomed to interacting with dialogue models like ChatGPT and Claude. What are their core capabilities? Answering questions. You ask it, “What is the MoE architecture?” and it explains; you ask, “How to write a quicksort algorithm?” and it provides code. This capability is based on single-turn question-answer optimization.

However, agent scenarios are entirely different. Agents do not “answer questions”; they “complete tasks”. For example, you ask OpenClaw to “refactor this project, optimize performance, and write a technical report”. This is not a “question”; it is a “task chain”:

1. Analyze project structure
2. Identify performance bottlenecks
3. Design optimization plan
4. Implement modifications
5. Test and validate
6. Write report

In this process, agents need:

• Long sequence execution: dozens of steps, thousands of messages
• Tool collaboration: invoking editors, terminals, testing frameworks
• Dynamic planning: adjusting strategies based on intermediate results
• Error recovery: automatically retrying or changing plans upon failure

These capabilities are fundamentally absent in chat models.

• Chat models are like “encyclopedic consultants”—knowledgeable but lacking practical experience.
• Agent models are like “experienced executors”—capable of completing tasks in complex environments.

The Solution of GLM-5-Turbo

Zhiyu AI’s solution is the Slime training framework. The core innovation is the asynchronous agent reinforcement learning algorithm. In simple terms, it trains the model on real or simulated agent tasks instead of static question-answer data.

A key figure: the limit of 3000-6000 messages per single run for long sequence training. What does this mean? It means the model learns not just to “answer questions” but to “manage the complete lifecycle of a long task”—planning, executing, monitoring, adjusting, and summarizing. This is a true paradigm shift from a “static knowledge base” to a “dynamic executor”.

Technical Breakdown: It’s Not About Parameters, It’s About Architecture

MoE: Precision Over Size

Technical parameters of GLM-5-Turbo:

• 744 billion total parameters, activating 40B-44B
• Pre-training data: 28.5T Tokens
• 80 layers, 256 experts
• Context window: 202K-204.8K Tokens

While the parameter count appears large, Zhiyu AI emphasizes not “maximum” but “optimal”. The MoE (Mixture of Experts) architecture has unique advantages in agent scenarios:

• Different experts handle different types of tasks
• More efficient long-sequence reasoning
• Better generalization capabilities
• Agent scenarios test “execution ability”, not “memory capacity”.

It’s better to change architecture than to stack parameters.

Long Context: Remembering More, Not Just Reading More

A context window of 202K-204.8K may sound like a “larger input box”, but its actual significance is entirely different. As an OpenClaw user, I have experienced this firsthand: Previously, when I asked Lobster to help me refactor a project, it would “forget” the initial plan midway. For instance, if I said at step 50, “Remember, all APIs must follow RESTful specifications,” by step 150, it might start writing GraphQL instead.

This isn’t because it “lacks intelligence”; it’s because the context window is insufficient, and earlier instructions get pushed out of memory. Now, it can complete modifications across hundreds of files while always remembering the initial requirements. This is the effect of DeepSeek sparse attention (DSA) and multi-head latent attention (MLA)—not just “reading more” but “remembering more”.

Evaluation Data: Demonstrating Capability, Not Just Scores

According to public evaluation data:

• SWE-bench Verified: 77.8 points → code generation ability close to that of a mid-level engineer
• Terminal-Bench 2.0 Terminus 2: 60.7 points → command line operation ability
• BrowseComp: 75.9 points → browser automation ability
• τ²-Bench Agent Task: 89.7 points → comprehensive task execution ability

These are not “exam scores”; they are “proof of work capability”. In the agent era, evaluation standards shift from “knowledge Q&A” to “task completion”. Whether a model can help you fix bugs, automate testing, or browse the web and extract information—these are the true indicators of capability.

Ecological Perspective: Hardware Needs Software, Models Need Ecology

AutoClaw: A Ready-to-Use Productivity Partner

No matter how powerful the technology, it is useless if users cannot access it. Zhiyu AI has simultaneously launched AutoClaw—a desktop application preloaded with 50+ Skills.

What are Skills? They are “predefined task templates”. For example, “Help me write a public account article”, “Help me analyze this code repository”, or “Help me organize meeting minutes”.

For ordinary users, this means: No programming skills are needed to utilize agent capabilities. You don’t need to know how to write prompts or understand technical details; just select a Skill and provide your requirements.

This is a leap from “technical tools” to “productivity partners”. Let me give a specific example: I was recently organizing a quality inspection project in a multidimensional table on Feishu. Previously, I had to manually export data, check each line, mark issues, and generate reports—taking at least two hours. Now, I called a “data quality inspection” Skill and simply told it: “Check this table for all empty fields, format errors, and duplicate data, and generate an Excel report.”

Lobster completed the entire process in under 5 minutes. More importantly, if it encounters special situations—like a field “appearing empty but actually containing hidden characters”—Lobster will pause and ask me: “This field contains invisible characters; should it be marked as an exception?” This is the difference between Agent and Chat: Chat gives you an answer, while Agent helps you walk through the entire process and proactively confirms when issues arise.

Developer Ecology: From “Writing Prompts” to “Writing Skills”

For developers, the OpenClaw ecosystem means greater imaginative potential. Previously, we optimized prompts to get better outputs from models. Now, we can “develop Skills” to enable agents to complete more complex tasks.

A Skill can encompass:

This is much deeper and more valuable than simple prompt engineering. More importantly, the Skills ecosystem is forming a model of “capability reuse”. For instance, if one developer writes a “code review” Skill, another developer can directly call that Skill instead of writing a complete code review logic from scratch.

This is akin to npm for JavaScript or pip for Python—modularization and reuse of capabilities. I have already seen many interesting Skills emerging in the community:

• Someone created a “automatically generate test cases” Skill
• Someone created a “analyze code dependencies” Skill
• Another created a “translate documents into multiple languages” Skill

These Skills are not isolated; they can be used in combination. For example, the “code review” Skill can call the “generate test cases” Skill to automatically generate test code for identified bugs. This combinatorial innovation potential is the true value of the ecosystem. Zhiyu AI has also launched the Lobster Package:

• 39 yuan/month for 35 million Tokens
• 99 yuan/month for 100 million Tokens

Prices have been adjusted, but capabilities have significantly improved—this is a reasonable value reassessment for heavy users.

If OpenClaw is “Linux”, then GLM-5-Turbo is the “chip” optimized for Linux.

Hardware-software collaboration is essential to maximize value.

Reflection and Outlook: Greater Capability, Greater Responsibility

Risks and Compliance: A Warning Not to Be Ignored

Just before and after the release of GLM-5-Turbo, two notable events occurred:

1. The China Internet Finance Association issued a risk warning: Beware of AI scams.
2. The 315 Gala exposed the AI poisoning industry chain: generating false information and deep forgery.

This is not alarmism. The autonomous execution capability of agents has fundamentally changed the risk dimension—previously, AI could only “speak incorrectly”; now it might “act incorrectly”.

An AI that can “automatically execute tasks” could have far more severe consequences if maliciously exploited than one that merely “answers questions”. Let me illustrate a few specific scenarios:

Scenario 1: Automated Attacks Imagine a malicious user instructing an agent to “help me find all vulnerabilities on this website”. In chat mode, the AI would only tell him “what common types of vulnerabilities are”. But in agent mode, the AI might automatically:

The entire process could be fully automated, and the attacker wouldn’t even need to understand the technology.

Scenario 2: Large-Scale Forgery Imagine a malicious user instructing an agent to “generate 100 false reviews and post them on this e-commerce platform’s product page”. In chat mode, the AI would simply generate a piece of review text. But in agent mode, the AI might:

1. Automatically register accounts
2. Generate 100 different styled reviews
3. Bypass CAPTCHA
4. Bulk publish

This scalable forgery capability is something chat models cannot achieve.

Scenario 3: Unauthorized Operations Imagine a user instructing an agent to “help me organize all the files in this folder”. If permission control is inadequate, the agent might:

1. Delete files it shouldn’t delete
2. Modify configurations it shouldn’t modify
3. Upload sensitive data to the wrong location

This isn’t the agent being “malicious”; it’s an error caused by “misunderstanding”. The faster the agent executes, the quicker the damage caused by errors.

The principle of least privilege becomes more important than ever:

• It’s not about “granting all permissions” but “granting permissions as needed”.
• User trust must be reciprocated with security.
• Every permission granted must have clear boundaries.

The stronger the agent’s capabilities, the greater the responsibility. This is not just a technical issue; it is also an ethical one.

Industry Impact: AI’s “Linux Moment”

The industry compares OpenClaw to the “Linux moment for AI”. This analogy is quite accurate. The open-source ecology of Linux has made it the “infrastructure” for servers, cloud computing, and embedded devices. The open-source ecology of OpenClaw is making it the “infrastructure” for AI agents.

A more vivid analogy is: From the “DOS system” to the “Windows moment”. In the DOS era, you had to remember various commands and manually execute each step. In the Windows era, you click the mouse, and the system automatically completes complex workflows. In the agent era, you provide a goal, and AI automatically plans and executes the entire task chain. This is not just an “interface upgrade”; it’s a fundamental change in the “interaction paradigm”.

The Endgame of Technology: Continuous Learning