Introduction
The true strength of AI today is in its capacity to maintain its deeper-level logic even during multi-turn conversations, where the underlying architectural choices in early projects are preserved in line with changing demands. The stateful system is an incredibly powerful tool in itself, much needed by technical managers working on long-term projects. Just as important is working not merely in disconnected results; instead, one also has to be capable of dealing with everything in between, right from frontend as well as backend integration as a function of one overall aim, to being able to generate high-quality deliverables in the form of presentation slides as well as Web UIs.
These capabilities are no longer on the horizon. Successful in this changeover is the example model GLM-4.7, which has shown itself to be a small model that is completely controllable, designed from the ground up to perform self-contained tasks. It brings to bear both stateful thinking, as in having the ability to maintain the complete logic of an undertaking in working memory, as well as unmatched reliability.
What is GLM-4.7?
GLM-4.7 is an active-agency Mixture-of-Experts (MoE) large language model created by Z.ai (Zhipu AI). It has been designed to go beyond answering questions and work towards task completion, which involves more than one step. Unlike other language models, GLM-4.7 has also been created for an execution-oriented AI system, which can work towards comprehending requirements, breaking down solutions, as well as integrating technologies.
Key Features of GLM-4.7
GLM-4.7 presents many industry-first features that make it differ from traditional LLMs
- Preserved Thinking: This is a major leap forward in the GLM line, and it enables the model to preserve logic trees in multi-turn conversations without having to do anything extra. This saves information by remembering logic applied in a previous meeting, instead of having to reapply logic associated with every message in a long-horizon process.
- Vibe Coding (UI/UX Excellence): This feature transcends the province of functional coding, aiming for aesthetic stability. GLM-4.7 has done a tremendous feat in churning out professional-grade graphics, thereby improving the PPT compatibility of 16:9 layouts to a whopping 91% (compared to the predecessor's 52% compatibility rate). Aesthetic output is flawless, to a point that the web page and ready-to-use slides require very few.
- Interleaved Thinking: Unlike models which could think impulsively, GLM-4.7 will think before every response and tool call. This will ensure high compliance with complex instructions and will lower the level of errors that could occur in the orchestration of multiple external tools.
- Turn-level Thinking Control: This provides detailed control over turn-level latency and depth. You can turn off thinking for short queries if needed for faster responses or turn it on for complex problem-solving in the same turn.
Use Cases of GLM-4.7
- Single-Objective Software Delivery through to End Game: GLM-4.7 can be very helpful in environments where translating one targeted description into an entire, functional result is something that needs to be done. In particular, because this model generates individual bits of code, it can break down needs, harmonize interfaces, and integrate both frontend and backend aspects.
- Evolution of Long-Horizon Projects with Stable Constraints: Different For projects that are worked on over a number of iterations, GLM-4.7 is capable of retaining architecture constraints as well as design decisions defined in the initial phases as active context in subsequent phases. This is effective in projects whereby requirements are defined in a number of iterations.
- High Reliability Tool and API Orchestration: GLM-4.7 can be used under conditions that include frequent interaction with several tools or APIs. It can work well with uncertain or incomplete tool results for multi-step workflows and reach a correct final state using minimal human involvement.
- Agentic Development and Maintenance Workflows: It comes with native support for agent frameworks like Claude Code, Cline, or Roo Code, making it capable of performing high-frequency iterations, or repeat work, related to auto-refactor, test, or documentation routines.
How Does GLM-4.7 Work?
The GLM-4.7 model retains the same general architecture for execution and training from previous models in the GLM-4 series, specifically from the GLM-4.5 model and the GLM-4.6 model. The model architecture is based on Mixture-of-Experts, with 355B total parameters and 32B active per token, designed to have large capacity for reasoning without using dense activation. The model adheres to the hybrid model for reasoning, with modes that include thinking, non-thinking, interleaved reasoning, planned before response, planned before tool call. These are made possible by architectural stabilizers that have attention logit normalization through QK-Norm, along with the Muon optimizer for faster optimization during large-scale training. Pre-training includes 15 trillion general, 7 trillion general/.reasoning.code-focused, which is a pipeline that the previous GLM-4 models have already employed in previous architecture for the capability to perform large context reasoning, tool usage, or agent-like workflows.
source - https://github.com/zai-org/GLM-4.5/
Specifically unique to GLM-4.7 is how it extends these inherited capabilities into a more stateful and execution-focused system. Specifically, this model includes Preserved Thinking, so internal reason thinking blocks are preserved across multiple-turn dialogue systems as opposed to being recalculated or lost in favor of more short-run logical evaluations. These capabilities are combined with turn-level thinking controls that allow for adjusting levels of thinking or reason logic within a given dialogue session. These processes are further encouraged through slime reinforcement learning systems that allow for separate agentic rollout computation from model training and optimize complex task learning performance across high levels of GPU utilization levels within model training itself. For inference purposes within GLM-4.7, a Multi-Token Prediction (MTP) layer is used for supporting speculative decoding capabilities and improving performance levels within GLM-4.7 systems by preserving reason integrity upon inference processes being applied. All of these elements further refine GLM-4.7 from being purely a logical model capable of reason into one that preserves and leverages reason within its performance capabilities across its operational lifespan for its primary point of technical divergence from its forgoing models.
Future Horizons: Adaptive Logic and Collaborative Agency
The future of adaptive logic decision making will be transformative and ambitious. Transitioning from the historical idea of a stateful reasoning, What will Adaptive Logic Life Cycles look like in the future? Can future iterations of Adaptive Logic have the ability to Identify critical architectural decisions that should be held long term from lessen architectural decisions that should be allowed to automatically retire? If we can develop a way to differentiate the two types of architectural decisions and allow for the elimination of lessen architectural choices, we will have a greater capacity to self-scale for larger projects and balance the speed at which we build context with the cost of operating responsibilities. Further, imagine if we could also apply this thinking to Cross-Session Continuity, where all aspects of project logic remain safe across various environments, provided that there are clearly established boundaries. Thus, we will progress beyond thinking of a single session worker model to a collaborative working environment to permit facilitation of engineering collaboration in a cohesive manner with multiple engineers benefiting from having a common reasoning state throughout long-duration work.
Future improvements to execution may include more closely linking the reasoning process with Artifact Validation. For example, could we build into our systems a way to automatically check the interface or integration produced against constraints of the structure or against pre-stated acceptance criteria before being approved for finalization? If so, this would reduce the amount of rework necessary later in the development cycle. A vision of Multi-Agent Collaboration under a unified Reasoning framework further supports this progression, as it envisions the collaboration of highly specialized agents—created specifically for Design, Implementation, and Verification—with appropriate control and oversight of the operation of all agents. The outcome of this evolution may be autonomous completion of project tasks that more closely reflect the behavior of engineers in the real world, thus creating a system of AI that not only takes action but develops and regulates itself in conjunction with increasingly complex Development Cycles.
Performance Evaluation with Other Models
GLM-4.7’s strength challenges and at times outperforms both open-weighted models and the best proprietary models. At the high-level reasoning level, GLM-4.7 scored an astonishing 42.8% on Humanity’s Last Exam (HLE). The new model shows a remarkable improvement of 41% over its previous version, GLM-4.6, which scored only 17.2%. More significantly, GLM-4.7 outperforms GPT-5.1 High (42.7%) and DeepSeek-V3.2 (40.8%) on HLE. Its superior performance.
source - https://z.ai/blog/glm-4.7
On the level of programming proficiency, the model attained 73.8% accuracy on SWE-bench Verified, which is a very essential task for assessing real programming proficiency. It also improved from a 5.8% gain in GLM-4.6, placing it better than DeepSeek-V3.2 (73.1%). Additionally, in the SWE-bench Multilingual dataset, it increased to 66.7% accuracy, registering a gigantic 12.9% gain from the past model.
source - https://docs.z.ai/guides/llm/glm-4.7
Aside from those headlines, GLM-4.7 is the best in utilizing interactive tools. On τ²-Bench, it got a total score of 87.4, beating both Claude Sonnet 4.5 (87.2) and GPT-5.1-High (82.7). It also topped the list for open-source models in the Code Arena for professionals and got a total score of 84.9 on LiveCodeBench-v6, proving to be more than a code generation tool but an elite coding.
How to Access and Use GLM-4.7?
The GLM-4.7 model is designed to be easily accessible. The model weights, which have BF16 and FP8 precisions, can be downloaded from Hugging Face and ModelScope to be used in local deployment using industry-standard frameworks such as vLLM and SGLang.
For anyone considering managed services, this model is also fully accessible through the Z.ai API, providing an interface compatible with OpenAI. It is available commercially through GLM Coding Plan, designed to have cost-effective pricing, 1/7th that of Claude, making it competitively priced. You can find it from this GitHub link, which has all the information necessary to install it. I have provided you with this information in your sources section.
Limitations
Although the GLM-4.7 model exhibits good agentic capabilities, the MoE strategy has to be carefully planned for optimal efficiency, even if reasoning is preserved. Furthermore, the new aspects that come with preserved reasoning involve the management of context or costs for long reasoning sessions. The next versions will likely improve compression or boundaries for the reasoning agent.
Conclusion
GLM-4.7 represents a significant paradigm shift in AI models of small to medium size—no longer systems for responding, but systems that can execute, remember, and deliver. Its retained ability to reason, task focus, and tested performance level indicate the dawn of the age of controllable systems capable of taking genuine engineering initiative in these matters, not entailing the costs of frontier-scale systems. GLM-4.7 brings efficiency as well as a new paradigm in integrating humans and AI systems.
Sources:
Blog: https://z.ai/blog/glm-4.7
Guide document: https://docs.z.ai/guides/llm/glm-4.7
Model Weight: https://huggingface.co/zai-org/GLM-4.7
GitHub Repo: https://github.com/zai-org/GLM-4.5/
Disclaimer - This article is intended purely for informational purposes. It is not sponsored or endorsed by any company or organization, nor does it serve as an advertisement or promotion for any product or service. All information presented is based on publicly available resources and is subject to change. Readers are encouraged to conduct their own research and due diligence.
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