Add GLM-4.5 release blog (#179)

Author: lifuhuang

blog/2025-07-31-glm4-5.md

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+---
+title: "GLM-4.5 Meets SGLang: Reasoning, Coding, and Agentic Abilities"
+author: "GLM Team"
+date: "July 31, 2025"
+previewImg: /images/blog/glm_4_5/GLM-4-5-preview.png
+---
+
+Today, we are excited to introduce our latest flagship models [GLM-4.5](https://huggingface.co/zai-org/GLM-4.5) and [GLM-4.5-Air](https://huggingface.co/zai-org/GLM-4.5-Air), along with their FP8 variants. All models are now available with day-one support on SGLang.
+GLM-4.5 and GLM-4.5-Air are both powerful models designed to unify reasoning, coding, and agentic capabilities, with **355B** total parameters (**32B** active) and **106B** total parameters (**12B** active) respectively. 
+
+
+## Deploying GLM-4.5 with SGLang 
+
+We recommend deploying the **GLM-4.5 series** of models using SGLang for optimal performance. Through close collaboration with the SGLang community, all GLM-4.5 models are fully supported on SGLang starting from day one.
+
+### Basic Usage
+
+
+**Install SGLang**
+```
+pip install --upgrade pip
+pip install "sglang[all]>=0.4.9.post6"
+```
+
+**355B Model**
+```
+python3 -m sglang.launch_server --model zai-org/GLM-4.5 --tp 8
+```
+**106B Model**
+```
+python3 -m sglang.launch_server --model zai-org/GLM-4.5-Air --tp 8
+```
+**355B FP8 Quantized Model**
+```
+python3 -m sglang.launch_server --model zai-org/GLM-4.5-FP8 --tp 8
+```
+**106B FP8 Quantized Model**
+```
+python3 -m sglang.launch_server --model zai-org/GLM-4.5-Air-FP8 --tp 4
+```
+
+### Tool Call 
+Append the following parameter to the command:
+```
+--tool-call-parser glm45 
+```
+### Reasoning Parser
+Append the following parameter to the command:
+```
+--reasoning-parser glm45
+```
+
+### Speculative Decoding with MTP
+Append the following parameters to the command:
+```
+--speculative-algorithm EAGLE \
+--speculative-num-steps [number of steps] \
+--speculative-eagle-topk [top k] \
+--speculative-num-draft-tokens [number of draft tokens]
+```
+
+## GLM 4.5 Model Architecture and Highlights
+**GLM-4.5** adopts a MoE architecture with loss-free balance routing and sigmoid gates, enhancing compute efficiency. Compared to models like DeepSeek-V3 and Kimi K2, we prioritize depth over width—fewer experts and smaller hidden dimensions, but more layers—resulting in better reasoning performance.
+
+**Key architectural designs and highlights:**
+* Grouped-Query Attention with partial RoPE
+* 96 attention heads for 5120 hidden size (2.5× more than typical), improving reasoning on MMLU/BBH despite similar training loss
+* QK-Norm for stabilized attention logits
+* Muon optimizer, enabling faster convergence and larger batch sizes
+* MTP (Multi-Token Prediction) head for speculative decoding
+* RL training is powered by the open-source framework [slime](https://github.com/THUDM/slime), which was earlier open-sourced by [THUDM](https://github.com/thudm).
+
+
+### Performance
+We compare **GLM-4.5** with various models from OpenAI, Anthropic, Google DeepMind, xAI, Alibaba, Moonshot, and DeepSeek on 12 benchmarks covering agentic (3), reasoning (7), and Coding (2). Overall, **GLM-4.5** is ranked at the 3rd place and **GLM-4.5 Air** is ranked at the 6th.
+
+![benchmark overview](/images/blog/glm_4_5/benchmark-overview.png)
+
+
+**Agentic Abilities**
+GLM-4.5 supports 128k context and native function calling. On both $\tau$-bench and BFCL-v3, it matches Claude 4 Sonnet, and on the BrowseComp web browsing benchmark, it surpasses Claude 4 Opus (26.4% vs. 18.8%) and approaches GPT o4-mini-high (28.3%). Its high tool-calling success rate (90.6%) highlights its reliability in agent-based workflows.
+
+**Reasoning**
+GLM-4.5 excels in mathematical and logical reasoning. It scores competitively on MMLU Pro (84.6), AIME-24 (91.0), and MATH500 (98.2), and demonstrates strong generalization across benchmarks like GPQA, LCB, and AA-Index.
+
+**Coding**
+GLM-4.5 shows comprehensive full-stack development ability and ranks among the top models on SWE-bench Verified (64.2) and Terminal-Bench (37.5). In head-to-head evaluations, it achieves a 53.9% win rate over Kimi K2 and 80.8% over Qwen3-Coder. Its high agentic reliability, multi-round coding task performance, and visual interface quality demonstrate its strength as an autonomous coding assistant.
+
+## Conclusion
+The **GLM-4.5 series** represents a new wave of large language models, excelling in long-context reasoning, agentic workflows, and coding tasks. Its hybrid MoE architecture—enhanced by techniques like grouped-query attention, MTP, and RL training—offers both efficiency and strong capability.
+
+**SGLang** provides a production-ready, high-performance inference stack, enabling seamless deployment through advanced memory management and request batching.
+
+Together, **GLM-4.5** and **SGLang** form a robust foundation for next-generation AI—powering intelligent, scalable solutions across code, documents, and agents.
+
+## Acknowledgement
+We would like to express our heartfelt gratitude to the following teams and collaborators in this [PR](https://github.com/sgl-project/sglang/pull/8224):
+
+- **[GLM Team](https://github.com/THUDM/GLM)**: Yuxuan Zhang, Chenhui Zhang, Xin Lv, Zilin Zhu and colleagues.
+- **[SGLang Team and community](https://docs.sglang.ai/index.html)**: Biao He, Lifu Huang, Binyao Jiang, Minglei Zhu, Cheng Wan, Chang Su and many others.