GLM-5 is extensively documented in the technical report 'GLM-5: from Vibe Coding to Agentic Engineering' (arXiv:2602.11354). The architecture is a Mixture-of-Experts (MoE) transformer with 80 layers and 256 experts. It introduces DeepSeek Sparse Attention (DSA) and Multi-Head Latent Attention (MLA) to optimize long-context performance. The post-training methodology, specifically the 'slime' asynchronous reinforcement learning infrastructure, is detailed with clear explanations of how generation and training are decoupled to improve iteration throughput.

The model was trained on a 28.5 trillion token corpus. Documentation specifies a two-stage process: general pre-training (~27T tokens) and a mid-training phase for long-context and agentic data. The technical report provides a breakdown of data sources including web (refined via DCLM classifiers), code (Software Heritage snapshots), and math/science (papers and books). While specific percentage breakdowns for every category are not provided in a single table, the filtering and cleaning methodologies (PPL, deduplication, and LLM-based scoring) are well-documented.

The tokenizer is publicly available via the official GitHub and Hugging Face repositories. It features a vocabulary size of 154,880 tokens. The tokenization approach is consistent with the GLM family's bilingual (Chinese/English) and code-heavy focus, and its integration is verified through public inference engines like vLLM and SGLang which support the model natively.

Z.ai provides precise transparency regarding parameter density. The model contains 744 billion total parameters, with exactly 40 billion parameters activated per token. The architectural breakdown is highly detailed: 80 layers total, consisting of 3 dense layers and 75 MoE layers, with 256 experts (8 routed and 1 shared per MoE layer). This level of detail exceeds industry standards for MoE disclosure.

While the technical report confirms the model was trained on Huawei Ascend AI chips (demonstrating hardware transparency), it lacks specific disclosure of total GPU/TPU hours, energy consumption, or carbon footprint calculations. The mention of 'massive resources' and 'different GPU clusters' for the slime infrastructure is descriptive but lacks the quantitative metrics required for a high score.

The model provides results for a wide array of benchmarks including SWE-bench Verified (77.8%), AIME 2026 (92.7%), and Vending Bench 2. Evaluation settings (temperature, top_p, context window) are disclosed in the technical report and model card. However, while evaluation code for some benchmarks is available in the repository, third-party verification for the most recent agentic benchmarks (like Vending Bench 2) is still emerging.

GLM-5 demonstrates high identity consistency, correctly identifying itself and its versioning in system prompts and documentation. It is transparent about its capabilities as a reasoning and agentic model and its limitations regarding long-context stability (200K tokens). There are no documented cases of the model claiming to be a competitor's product.

The model is released under the MIT License, which is explicitly stated in the official blog, the GitHub repository, and the Hugging Face model card. This is a highly permissive, standard open-source license with no conflicting commercial restrictions or 'open-ish' caveats, providing maximum clarity for derivative works and commercial use.

Hardware requirements are well-documented for various precisions. Official documentation and community guides (e.g., Unsloth, vLLM) provide VRAM requirements for BF16 (~1.5TB), FP8 (~756GB), and various quantization levels (2-bit GGUF at 241GB). The impact of context length on KV-cache memory is also detailed, including the benefits of the DSA mechanism in reducing memory overhead.

The model follows a clear versioning path from GLM-4.5 to 5.0, and the GitHub repository maintains a basic changelog. However, as a relatively new release, there is limited long-term data on silent behavior drift or a formal deprecation policy for previous iterations. Semantic versioning is used, but the frequency of 'silent' weight updates in the early release phase remains a point of skepticism.