Glm 4 7 Migration Guide

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Jan 08 2026 This new model is smarter than Sonnet 4.5…and 20X faster? Sarah Chieng Sebastian Duerr

So, you need speed, intelligence, and great economics… introducing GLM 4.7, the first open model that delivers all three. Why developers are switching At Cerebras, we’ve seen overwhelming demand from developers for GLM 4.7. The migration to GLM 4.7 is driven by three key factors: cost, speed, and intelligence. Cost: GLM 4.7 is more affordable than models like Claude Sonnet 4.5, achieving high-proficiency intelligence at a fraction of the cost. Speed: On Cerebras, GLM 4.7 achieves output speeds of up to 1500+ 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 4.7 is the strongest open-source coding models available today. It’s remarkably skilled at tool use, achieving 96% on 𝜏²-Bench Telecom, which makes it suitable for use within coding harnesses and general agents. Additionally, GLM 4.7 is excellent at reasoning and knowledge tasks, scoring 86% on the challenging GPQA Diamond (Scientific Reasoning) as measured by Artificial Analysis.

What GLM 4.7 is (and why it matters) GLM 4.7 is Z.AIs latest model, and the first OS model to rival closed source giants like Sonnet 4.5. Some main points: Scale: ~ 358B total parameters, ~ 32B active per token (MoE routing) Built for: coding + tool use + agentic workflows Privacy on Cerebras: inputs/outputs are processed in memory and not persisted

But architecture is just theory until you see the results. ​ The Performance If you care about coding and agentic evals, GLM 4.7 lands in the “top open model” tier. On LiveCodeBench, GLM 4.7 outperforms Anthropic and OpenAI models, trailing only Gemini 3. GLM 4.7 extends that lead with substantial gains on GPQA and AIME, outperforming Claude Sonnet 4.5 on both, see AIME 2025 below. And when you compare it directly to GLM 4.6, a previous model generation the improvement in coding and general capabilities is substantial, notably +12.4 points in HLE, +16.5 points Terminal Bench 2, amongst others. Performance is one thing, but usability is another. GLM 4.7: The opportunity to be 20x faster The beauty of GLM 4.7 is that it’s open source. This means that you’re no longer obstructed by hardware bottlenecks. Now, your product can achieve token output speeds magnitudes faster than closed source models like Sonnet 4.5 or GPT 5.2 running on GPUs. For example, see the difference in speed for GLM 4.7 across different hardware providers. Every model has different personality quirks. When migrating to GLM 4.7, a common mistake is reusing old prompts without adjusting them for its unique behavior, which can lead to suboptimal performance. To fully leverage GLM 4.7'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 4.7. Rule #1: Front load your instructions On Cerebras, GLM 4.7 supports up to 131K context length. However, like most other large models, the output quality for GLM4.7 is most accurate at shorter lengths and can degrade at extreme lengths. GLM 4.7 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 4.7 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 4.7 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 4.7’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 4.7 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 4.7. You can do this by breaking tasks into small, well-defined sub-steps. For example: List dependencies. Propose the new structure. 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...