GLM-5: что умеет модель и где применять её в разработке
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Короткий ответ
GLM-5 стоит использовать как рабочий инструмент для задач, где важны скорость первичного черновика и понятная структура ответа. Она полезна для генерации заготовок, резюме изменений и быстрого анализа вариантов решения. Для критичных продовых правок нужен обязательный слой проверки тестами и логами.
- Сильные сценарии: черновики кода, сравнительный анализ, документация.
- Слабые сценарии: сложные бизнес-правила без формальных тестов.
- Безопасный режим: маленькие итерации + обязательная проверка результата.
The short answer
GLM-5 should be used as a working tool for tasks where the speed of the primary draft and the structure of the response are important. It is useful for generating workpieces, summarizing changes and quickly analyzing solutions. For critical food edits, a mandatory layer of verification with tests and logs is needed.
- Strong scenarios: draft code, comparative analysis, documentation.
- Weak Scenarios: Complex business rules without formal tests.
- Safe mode: small iterations + mandatory result check.
The GLM-5 is a new flagship big language model from Z.ai (also known as Zhipu AI), released on February 12, 2026. This model represents a significant step forward from the previous GLM-4.7, focusing on “agent engineering” – the transition from simple code writing to automated creation of entire projects and systems. GLM-5 is designed for complex tasks in systems engineering and long-term agent scenarios, where AI must plan and execute actions over many steps.
What is GLM-5 and why is it needed?
The GLM-5 is a transformer-based model with a Mixture-of-Experts (MoE) architecture with 744 billion overall parameters and 40 billion active parameters. It is trained on 28.5 trillion data tokens, 5.5 trillion more than GLM-4.5. A key feature is the integration of DeepSeek Sparse Attention (DSA), which reduces the cost of deployment by 6-10 times compared to analogues, preserving the context of up to 205 thousand tokens. This allows the model to handle long sequences without losing performance.
The model focuses on:
- Coding and development: GLM-5 shows the best results among open-source models in programming problems. At the SWE-bench Verified benchmark, she scored 77.8%, ahead of Claude Opus 4.5.
- Suitable for long-term planning, where AI acts as an autonomous agent, building complex systems.
- Understanding and minimizing hallucinations:** The GLM-5 has a record low hallucination rate among all models (including proprietary models), with a rating of -1 on the AA-Omniscience Index - an improvement of 35 points over its predecessor.
The GLM-5 is the first frontier model fully trained on Huawei Ascend chips, demonstrating its independence from American technology. It is released under the MIT license, making it fully open source and suitable for commercial use.
Technical innovation
The development of the GLM-5 included several breakthroughs:
- Scaling: Increase from 355B (in GLM-4.5) to 744B and data from 23T to 28.5T tokens.
- ** Asynchronous RL infrastructure "slime":** A new system based on Megatron-LM and SGLang, which accelerates learning in RL (Reinforcement Learning) and allows more iterations of post-learning. This helps the model cope better with real-world programming scenarios.
- Optimizations: Use of Muon Optimizer, QK Normalization, Partial RoPE and MTP for speculative decoding. The model supports the context of 200K-205K tokens and is compatible with tools like Claude Code and OpenClaw.
In benchmarks, GLM-5 is the leader among open-source: improvements on Humanity's Last Exam (+7.6%), BrowseComp (+8.4%) and Terminal-Bench-2.0 (+28.3%). On the Artificial Analysis Intelligence Index, it scored 77.8 points, making it one of the best in the world.
How to use GLM-5?
GLM-5 is available for free for testing on https://chat.z.ai, a chatbot where you can ask questions and experiment without a proxy, even from Russia. For developers:
- API: Through api.z.ai or BigModel.cn. Prices: from $0.2 per 1M input tokens (for Air versions).
- Local launch: Model on Hugging Face (zai-org/GLM-5) or Ollama. For Macs with M chips, use MLX; for NVIDIA, use NIM. Optimized versions (like Unsloth’s 2-bit GGUF) reduce the size to 241GB.
- Integrations: Works with Claude Code, Kilo Code, OpenClaw and other coding tools.
Users note high speed and quality, although there may be performance issues at the start (e.g., 20 minutes for a simple task). The model is 6-10 times cheaper than analogues like the Claude Opus 4.6.
Conclusion
Z.ai’s GLM-5 is a breakthrough in open-source AI, bringing us closer to Artificial General Intelligence (AGI). It combines power, efficiency and affordability, making complex tasks like system engineering and agency planning easier for everyone.
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