GLM-5.1

GLM-5.1 is explicitly documented as a post-training refinement of the GLM-5 base model. The architecture, 'GlmMoeDSA', is a sophisticated hybrid combining Mixture-of-Experts (MoE) with DeepSeek Sparse Attention (DSA) and Gated DeltaNet linear attention. Technical reports and GitHub documentation detail the use of 78 layers and a specific configuration of 256 routed experts plus 1 shared expert. The transition from GLM-5's 744B to 754B parameters is noted as being driven by architectural optimizations for long-horizon agentic tasks. While the high-level methodology is clear, the specific weights of the hybrid attention blending are not fully disclosed.

Information regarding the training data is limited to high-level metrics. Official sources state the model was pre-trained on 28.5 trillion tokens, an increase from the 23T used for GLM-4.5. However, the specific breakdown of data sources (e.g., proportions of code, web, or academic data) is not publicly disclosed. Documentation explicitly lists data collection and labeling methodologies as 'Undisclosed' in technical specifications, though it mentions the use of multi-turn SFT and RL for post-training.

The model uses the 'Tekken' tokenizer, which is publicly accessible via the official GitHub repository and Hugging Face. It features a vocabulary size of 131,072 tokens and is documented to support 200K context windows with 128K max output tokens. The tokenizer is compatible with standard runtimes like vLLM and Transformers, allowing for independent verification of tokenization behavior and language support alignment.

The model's parameter density is well-documented: it features 754 billion total parameters with 40 billion active parameters per token (8 routed experts + 1 shared expert). The architectural breakdown of 256 total experts is clearly stated. However, while the total and active counts are provided, the specific parameter distribution between the Gated DeltaNet linear attention layers and standard attention layers is less granularly detailed in public documentation.

Compute transparency is low. While it is disclosed that the model was trained entirely on Huawei Ascend chips using a novel asynchronous RL infrastructure called 'slime', specific hardware hours, total GPU/TPU days, and carbon footprint data are absent. There are no public estimates of the total training cost or energy consumption provided by Z.ai.

Z.ai provides comprehensive results across major benchmarks including SWE-Bench Pro (58.4%), Terminal-Bench 2.0 (63.5%), and AIME 2026 (95.3%). While the benchmarks are named and versions are often specified, the exact evaluation code and full prompt sets used to achieve these specific scores are not fully public. Third-party verification is limited to API-based testing on platforms like OpenRouter, rather than full independent reproduction of the training-to-eval pipeline.

The model demonstrates high identity consistency, correctly identifying itself as GLM-5.1 and maintaining awareness of its versioning and specific focus on agentic engineering. It does not exhibit confusion with competitor models in official documentation or API deployments. It is transparent about its limitations, such as being a text-only model despite the existence of multimodal variants like GLM-5V.

The model is released under the MIT License, which is one of the most permissive and clear open-source licenses available. This allows for unrestricted commercial use, modification, and distribution. There are no conflicting proprietary terms found in the official weight release on Hugging Face or the source code on GitHub.

Hardware requirements are documented for the 1.51TB FP16 weights. Documentation and community resources provide guidance on running the model via quantization (GGUF, EXL2) on various hardware configurations, including multi-GPU setups. While VRAM requirements for the full model are clear (requiring enterprise-grade clusters), more detailed documentation on the accuracy-performance tradeoffs for specific quantization levels (Q4/Q8) would improve this score.

Z.ai uses a clear versioning scheme (GLM-5 to GLM-5.1) and provides changelogs highlighting the 28% coding performance improvement and the introduction of 'thinking mode'. However, the frequency of silent updates to the API endpoints and the availability of long-term support for older versions are not fully transparent, making it difficult for developers to guarantee long-term behavior stability.