GLM-5

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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.

GLM-5 is purpose-built for complex systems engineering and long-horizon agentic tasks. On our internal evaluation suite CC-Bench-V2, GLM-5 significantly outperforms GLM-4.7 across frontend, backend, and long-horizon tasks, narrowing the gap to Claude Opus 4.5.

On Vending Bench 2, a benchmark that measures long-term operational capability, GLM-5 ranks #1 among open-source models. Vending Bench 2 requires the model to run a simulated vending machine business over a one-year horizon; GLM-5 finishes with a final account balance of $4,432, approaching Claude Opus 4.5 and demonstrating strong long-term planning and resource management.

Download Model

Serve GLM-5 Locally

Prepare environment

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

• vLLM

  Using Docker as:

    docker pull vllm/vllm-openai:nightly 

  or using pip:

    pip install -U vllm --pre --index-url https://pypi.org/simple --extra-index-url https://wheels.vllm.ai/nightly

  then upgrade transformers:

    pip install git+https://github.com/huggingface/transformers.git

• SGLang

  Using Docker as:

    docker pull lmsysorg/sglang:glm5-hopper # For Hopper GPU
    docker pull lmsysorg/sglang:glm5-blackwell # For Blackwell GPU

Deploy

• vLLM

    vllm serve zai-org/GLM-5-FP8 \
         --tensor-parallel-size 8 \
         --gpu-memory-utilization 0.85 \
         --speculative-config.method mtp \
         --speculative-config.num_speculative_tokens 1 \
         --tool-call-parser glm47 \
         --reasoning-parser glm45 \
         --enable-auto-tool-choice \
         --served-model-name glm-5-fp8

  Check the recipes for more details.

• SGLang

    python3 -m sglang.launch_server \
      --model-path zai-org/GLM-5-FP8 \
      --tp-size 8 \
      --tool-call-parser glm47  \
      --reasoning-parser glm45 \
      --speculative-algorithm EAGLE \
      --speculative-num-steps 3 \
      --speculative-eagle-topk 1 \
      --speculative-num-draft-tokens 4 \
      --mem-fraction-static 0.85 \
      --served-model-name glm-5-fp8

  Check the sglang cookbook for more details.

• xLLM and other Ascend NPU

  Please check the deployment guide here.

Citation

Our technical report is coming soon.