GLM 4.6: 10 Tricks to Migrate

With additional contributions from Golara Azar, Jan Feddersen, Michael Pfaffenberger, and Joyce Er.

So you want speed, intelligence, and low cost? Introducing the first model to do it all... GLM 4.6

The era of proprietary models dictating developer workflows is ending. With the launch of GLM 4.6, developers finally have an open-source and deeply intelligent alternative, that is cheaper and faster than closed-source giants like Claude Sonnet 4. And, with the right prompting strategies, GLM4.6 can rival Sonnet 4.5 intelligence on certain tasks.

This guide provides targeted strategies and technical insights to help you rapidly optimize GLM 4.6's performance for your use case, allowing you to confidently replace expensive, closed-box APIs.

What is GLM 4.6?

GLM 4.6 is the latest foundation model from Zhipu AI, purpose-built to excel in coding and agentic workflows.

• Architecture: It is built on the "GLM-4.x" foundation , leveraging a Mixture-of-Experts (MoE) Transformer architecture.
• Efficiency: The model boasts an impressive 355 billion parameters in total. However, thanks to MoE sparsity, only about 32 billion parameters are active on any given forward pass, yielding considerable efficiency gains.
• Open Source: GLM 4.6 is released under an MIT License, giving you the flexibility to fine-tune, self-host, or deploy however you choose.
• Data Privacy: Unlike running closed source models, when you run GLM 4.6 on providers like Cerebras Inference, your data is never used to train new models or retained after processing.

Why developers are switching

At Cerebras, we’ve seen overwhelming demand from developers for GLM 4.6. The migration to GLM 4.6 is driven by three key factors: cost, speed, and intelligence.

• Cost: GLM 4.6 is more affordable than models like Claude Sonnet 4.5, achieving high-proficiency intelligence at a fraction of the cost.
• Speed: On Cerebras, GLM 4.6 achieves output speeds of up to 1000+ tokens per second, making it 20x faster than closed-source competitors like Sonnet 4.5. This significantly reduces latency in agentic workflows, allowing for rapid iteration and execution in development environments.
• Intelligence: GLM 4.6 is among the strongest open-source coding models available today. It’s remarkably skilled at tool use, achieving 71% on 𝜏²-Bench Telecom, which makes it suitable for use within coding harnesses and general agents. Additionally, GLM 4.6 is excellent at reasoning and knowledge tasks, scoring 83% on the MMLU-Pro benchmark as measured by Artificial Analysis.

Every model has different personality quirks. When migrating to GLM 4.6, a common mistake is reusing old prompts without adjusting them for its unique behavior, which can lead to suboptimal performance. To fully leverage GLM 4.6's strengths, it's essential to refine your prompts, architecture, and sampling parameters accordingly.

Below are 10 rules to help you get the most from GLM 4.6.

Rule #1: Front load your instructions

On Cerebras, GLM 4.6 supports up to 131K context length. However, like most other large models, the output quality for GLM4.6 is most accurate at shorter lengths and can degrade at extreme lengths.

GLM 4.6 in particular has been observed to have a strong bias towards the beginning of the prompt, even more so than other models. This is especially noticeable when using think tags in conversations—it reinforces earlier instructions.

Accordingly, to ensure proper instruction following, place all mandatory instructions and behavioral directives at the absolute start of your system prompt to leverage the model's beginning bias. This is more effective than placing them later in the prompt.

Rule #2: Provide clear and direct instructions

Different models follow instructions differently. GLM 4.6 responds best to firm, direct language that removes ambiguity. Establish rules immediately using strong, explicit directives like MUST and STRICTLY. Avoid soft, suggestive language that the model may treat as optional.

For example:

• Do write: “Before writing any code, you MUST first read and fully comprehend the architecture.md file. All code you generate must strictly conform…”
• Don’t write: “Please read and follow my architecture.md...”

Rule #3: Specify a default language

Because GLM 4.6 is multilingual, we’ve found that it can sometimes switch languages in its responses.

If you’re migrating from a language model that defaults to English, it’s helpful to include a directive such as:"Always respond in English" (or your preferred language) in your system prompt to prevent unexpected outputs.

Occasionally, we’ve observed that the model may output reasoning traces in Chinese on the first turn. Explicit language control prevents this behavior.

Rule #4: Leverage role-play

One of GLM 4.6’s biggest strengths is its ability to effectively maintain and follow roles and personas. Its internal “thinking blocks” mirror role prompts closely, allowing precise control over tone and domain knowledge.

To take advantage of the models’ ability to role play: give the model an explicit persona, or create multi-agent systems each with their own personas.

For example:

• Do write: “You’re acting as an analyst preparing an executive summary; make sure to review the following sources in detail and then give a structured and professional response…”

Rule #5: Break up the task

GLM 4.6 performs a single reasoning pass per prompt before acting and does not continuously re-evaluate mid-task. This is sometimes referred to as 'interleaved thinking', which is supported by models like the Sonnet/OAI models.

In interleaved thinking, the model will alternate between:

• Reasoning steps (analysis, hypothesis generation)
• Action steps (retrieval, tool use, code execution, or environment interaction)

This allows the model to pause, reflect on intermediate results, and adjust its approach dynamically throughout the task execution.

Without interleaved thinking, it is encouraged to prompt better task completion in GLM 4.6. You can do this by breaking tasks into small, well-defined sub-steps. For example:

1. List dependencies.
2. Propose the new structure.
3. Generate and verify migrations.

This incremental approach produces cleaner results and closely matches GLM’s execution-first tendencies.

Rule #6: Disable or minimize reasoning when not needed

GLM 4.6 often includes internal thought/reasoning blocks in its output. For many straightforward tasks, this reasoning overhead is unnecessary and slows down responses.

To minimize reasoning:

1. Disable Reasoning: Use the nonstandard parameter disable_reasoning: True in your request parameters for the Cerebras API. Note that this is different from Z.ai who uses the parameter thinking in the Z.ai API.
2. Set max_completion_tokens: GLM 4.6's verbosity can be effectively controlled by setting appropriate token limits. For focused responses, consider using lower values.
3. Prompt for Less: Include instructions in the system prompt to minimize reasoning. For example, add to the system prompt: "Reason only when necessary" or "Skip reasoning for straightforward tasks."
4. Set Output Constraints: Use structured output formats (JSON, lists, bullets) that naturally discourage verbose reasoning blocks.

Rule #7: Enable enhanced reasoning for complex tasks

While GLM 4.6's reasoning can be excessive for simple tasks, it becomes valuable for complex problem-solving that requires step-by-step thinking.

To enhance reasoning:

1. Enable Reasoning: Ensure disable_reasoning is set to False (or omitted) in your API request when tackling complex problems.
2. Prompt for Depth: Add explicit reasoning instructions to your system prompt: "For any given task you must think step by step" or "Break down your reasoning into clear logical steps."
3. Chain-of-Thought Prompting: Include examples that demonstrate the reasoning process you want, showing the model how to work through problems methodically.

Rule #8: When in doubt, use critics!

Following from rule #4, one of the most powerful patterns when working with GLM 4.6 (or any LLM) is to employ specialized critic agents to review and validate outputs before allowing the main agentic flow to advance in its plan. Rather than relying on a single agent to both generate and validate code, create dedicated sub-agents with specific expertise:

• Code Review Agent: A sub-agent configured to rigorously check for code quality, adherence to SOLID/DRY/YAGNI principles, and maintainability issues.
• QA Expert Agent: Potentially bound with agentic browser capabilities to test user flows, edge cases, and integration points.
• Security Review Agent: Specialized in identifying vulnerabilities, unsafe patterns, and compliance issues.
• Performance Audit Agent: Focused on detecting performance bottlenecks, inefficient algorithms, or resource leaks.

By splitting responsibilities across multiple agents, each with a focused persona (see Rule #4), you create a robust pipeline where generation and validation are decoupled.

This approach dramatically improves output quality and catches issues that a single generalist agent might miss. This is feasible in Code Puppy, Kilo Code/Roo Code (both offer multi agentic workflows), Llxprt-code, and limited with Claude Code (via Claude Code Router) and others. Tools are ordered in the amount of configuration needed to get these patterns implemented.

Rule #9: Pair GLM with a frontier model

GLM 4.6 excels at reasoning compared to other open source models. However, if your use case relies on frontier reasoning capabilities, you may find that GLM 4.6 falls short on the toughest 10% of use cases.

Here are three architectural patterns you can employ to effectively utilize GLM 4.6 in your applications:

1. Route to GLM 4.6 for simpler tasks and fall back to slower models for the most complex queries.
2. Use GLM 4.6 as a fast backbone agent that loops in slower, more intelligent models only when needed.
3. Use Sonnet or GPT to first create a plan, then execute it rapidly using GLM 4.6—allowing you to handle high volumes of tasks at a fraction of the cost while maintaining quality on complex reasoning steps.

By leveraging GLM 4.6's 17x faster output speed and lower costs for the majority of tasks, you can realize significant speed and cost savings without sacrificing overall quality.

Rule #10: Tune parameters for creativity vs. stability

Proper parameter tuning has a significant impact on output quality and latency. Recommended defaults from Zhipu AI and Cerebras are as follows:

For Cerebras, the temperature and top_p can be adjusted via the API, for example:

How to get started?

GLM 4.6 offers an open, high-performance alternative for developers who prioritize speed, cost-efficiency, and data privacy.

You can start building today by accessing GLM 4.6 through the free Cerebras API, together with our Cerebras Code plans. Start iterating, and let the efficiency gains speak for themselves.

Additionally, the above list is a WIP doc, and we would love to hear any contributions, feedback or observations on GLM 4.6 from the community, as we work together to get the most from open weight models.