GLM-5-FP8

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Introduction

We are launching GLM-5, targeting complex systems engineering and long-horizon agentic tasks. Scaling is still one of the most important ways to improve the intelligence efficiency of Artificial General Intelligence (AGI). Compared to GLM-4.5, GLM-5 scales from 355B parameters (32B active) to 744B parameters (40B active), and increases pre-training data from 23T to 28.5T tokens. GLM-5 also integrates DeepSeek Sparse Attention (DSA), largely reducing deployment cost while preserving long-context capacity.

Reinforcement learning aims to bridge the gap between competence and excellence in pre-trained models. However, deploying it at scale for LLMs is a challenge due to the RL training inefficiency. To this end, we developed slime, a novel asynchronous RL infrastructure that substantially improves training throughput and efficiency, enabling more fine-grained post-training iterations. With advances in both pre-training and post-training, GLM-5 delivers significant improvement compared to GLM-4.7 across a wide range of academic benchmarks and achieves best-in-class performance among all open-source models in the world on reasoning, coding, and agentic tasks, closing the gap with frontier models.

Benchmark

| | GLM-5 | GLM-4.7 | DeepSeek-V3.2 | Kimi K2.5 | Claude Opus 4.5 | Gemini 3 Pro | GPT-5.2 (xhigh) | | -------------------------------

| ---------------------- | --------- | ------------- |-----------| --------------- | ------------ | --------------- |

| HLE | 30.5 | 24.8 | 25.1 | 31.5 | 28.4 | 37.2 | 35.4 | | HLE (w/ Tools) | 50.4 | 42.8 | 40.8 | 51.8 | 43.4* | 45.8* | 45.5* | | AIME 2026 I | 92.7 | 92.9 | 92.7 | 92.5 | 93.3 | 90.6 |

|

| HMMT Nov. 2025 | 96.9 | 93.5 | 90.2 | 91.1 | 91.7 | 93.0 | 97.1 | | IMOAnswerBench | 82.5 | 82.0 | 78.3 | 81.8 | 78.5 | 83.3 | 86.3 | | GPQA-Diamond | 86.0 | 85.7 | 82.4 | 87.6 | 87.0 | 91.9 | 92.4 | | SWE-bench Verified | 77.8 | 73.8 | 73.1 | 76.8 | 80.9 | 76.2 | 80.0 | | SWE-bench Multilingual | 73.3 | 66.7 | 70.2 | 73.0 | 77.5 | 65.0 | 72.0 | | Terminal-Bench 2.0 (Terminus 2) | 56.2 / 60.7 † | 41.0 | 39.3 | 50.8 | 59.3 | 54.2 | 54.0 | | Terminal-Bench 2.0 (Claude Code) | 56.2 / 61.1 † | 32.8 | 46.4 |

| 57.9 | - | - |

| CyberGym | 43.2 | 23.5 | 17.3 | 41.3 | 50.6 | 39.9 |

|

| BrowseComp | 62.0 | 52.0 | 51.4 | 60.6 | 37.0 | 37.8 |

|

| BrowseComp (w/ Context Manage) | 75.9 | 67.5 | 67.6 | 74.9 | 67.8 | 59.2 | 65.8 | | BrowseComp-Zh | 72.7 | 66.6 | 65.0 | 62.3 | 62.4 | 66.8 | 76.1 | | τ²-Bench | 89.7 | 87.4 | 85.3 | 80.2 | 91.6 | 90.7 | 85.5 | | MCP-Atlas (Public Set) | 67.8 | 52.0 | 62.2 | 63.8 | 65.2 | 66.6 | 68.0 | | Tool-Decathlon | 38.0 | 23.8 | 35.2 | 27.8 | 43.5 | 36.4 | 46.3 | | Vending Bench 2 | $4,432.12 | $2,376.82 | $1,034.00 | $1,198.46 | $4,967.06 | $5,478.16 | $3,591.33 |

> *: refers to their scores of full set. > > †: A verified version of Terminal-Bench 2.0 that fixes some ambiguous instructions. See footnote for more evaluation details.

Footnote

* Humanity’s Last Exam (HLE) & other reasoning tasks: We evaluate with a maximum generation length of 131,072 tokens (temperature=1.0, top_p=0.95, max_new_tokens=131072). By default, we report the text-only subset; results marked with * are from the full set. We use GPT-5.2 (medium) as the judge model. For HLE-with-tools, we use a maximum context length of 202,752 tokens. * SWE-bench & SWE-bench Multilingual: We run the SWE-bench suite with OpenHands using a tailored instruction prompt. Settings: temperature=0.7, top_p=0.95, max_new_tokens=16384, with a 200K context window. * BrowserComp: Without context management, we retain details from the most recent 5 turns. With context management, we use the same discard-all strategy as DeepSeek-v3.2 and Kimi K2.5. * Terminal-Bench 2.0 (Terminus 2): We evaluate with the Terminus framework using timeout=2h, temperature=0.7, top_p=1.0, max_new_tokens=8192, with a 128K context window. Resource limits are capped at 16 CPUs and 32 GB RAM. * Terminal-Bench 2.0 (Claude Code): We evaluate in Claude Code 2.1.14 (think mode, default effort) with temperature=1.0, top_p=0.95, max_new_tokens=65536. We remove wall-clock time limits due to generation speed, while preserving per-task CPU and memory constraints. Scores are averaged over 5 runs. We fix environment issues introduced by Claude Code and also report results on a verified Terminal-Bench 2.0 dataset that resolves ambiguous instructions (see: https://huggingface.co/datasets/zai-org/terminal-bench-2-verified). * CyberGym: We evaluate in Claude Code 2.1.18 (think mode, no web tools) with (temperature=1.0, top_p=1.0, max_new_tokens=32000) and a 250-minute timeout per task. Results are single-run Pass@1 over 1,507 tasks. * MCP-Atlas: All models are evaluated in think mode on the 500-task public subset with a 10-minute timeout per task. We use Gemini 3 Pro as the judge model. * τ²-bench: We add a small prompt adjustment in Retail and Telecom to avoid failures caused by premature user termination. For Airline, we apply the domain fixes proposed in the Claude Opus 4.5 system card. * Vending Bench 2: Runs are conducted independently by Andon Labs.

Serve GLM-5 Locally

Prepare environment

vLLM, SGLang, KTransformers, and xLLM all support local deployment of GLM-5. A simple deployment guide is provided here.

+ vLLM

Using Docker as:



or using pip:



then upgrade transformers:



+ SGLang

Using Docker as:

Deploy

+ vLLM



Check the recipes for more details.

+ SGLang

Check the sglang cookbook for more details.

+ xLLM and other Ascend NPU

Please check the deployment guide here.

+ KTransformers

Please check the deployment guide here.

Citation

Our technical report is coming soon.