GLM-4.6 is explicitly documented as a Mixture-of-Experts (MoE) transformer model, evolving from the GLM-4.5 architecture. Key architectural details are public, including the use of Grouped-Query Attention (GQA) with 96 heads, QK-Norm for stability, and a partial Rotary Position Embedding (RoPE). The model's transition to a single-stage Reinforcement Learning (RL) pipeline and the 'SLIME' framework for agentic training are also disclosed in technical presentations. However, while the high-level methodology is clear, the specific layer-by-layer configuration and the exact 'partial' nature of the RoPE implementation lack granular technical specifications in the primary model card.

Information regarding the training data is limited to high-level marketing descriptions. The provider mentions a '15 trillion token' pre-training corpus and highlights the inclusion of 'repo-level code contexts' and 'agentic reasoning data.' However, there is no public breakdown of the dataset's composition (e.g., specific percentages of web, code, or books), no disclosure of specific data sources, and no detailed documentation on the filtering or cleaning methodologies used to curate the 15T tokens.

The tokenizer is publicly accessible via the official Hugging Face repository and GitHub. It supports a 200K context window and is optimized for bilingual (English/Chinese) tasks. Technical documentation notes a 15% improvement in token efficiency over its predecessor, GLM-4.5. The vocabulary and tokenization approach are verifiable through the provided source code, though detailed alignment studies between the tokenizer and the specific 15T token training set are not fully documented.

The model provides exemplary transparency regarding its MoE structure, clearly distinguishing between the 357 billion total parameters and the 32 billion active parameters utilized during a forward pass. This prevents the common 'parameter inflation' seen in MoE marketing. The architectural breakdown (MoE with 32B active) is consistent across official documentation and third-party technical reviews.

There is almost no verifiable information regarding the specific compute resources used for training GLM-4.6. While the provider mentions the 'SLIME' framework for efficient RL, they do not disclose GPU/TPU hours, hardware cluster specifications, training duration, or the carbon footprint. Claims of 'efficiency' are made without the underlying compute data necessary for verification.

Z.ai provides a significant amount of evaluation data, including the public release of 'CC-Bench' trajectories (prompts, tool calls, and multi-turn logs) on Hugging Face to allow for scrutiny of agentic performance. They also report scores on standard benchmarks like AIME 25 and LiveCodeBench v6. However, the scoring is penalized due to significant performance discrepancies reported by third-party testers on long-context tasks compared to official claims, and the lack of a unified, one-click reproduction script for all cited benchmarks.

The model demonstrates high identity consistency, correctly identifying itself as GLM-4.6 and maintaining version awareness. It does not attempt to mimic competitor identities (like GPT-4) in its weights or system prompts. Documentation clearly distinguishes between the base model and its multimodal (4.6V) or reasoning-specific variants, ensuring users know exactly which version they are interacting with.

The model weights and source code are released under the highly permissive MIT license, which is clearly stated on Hugging Face and GitHub. This license explicitly allows for commercial use, modification, and distribution with minimal restrictions. There are no known conflicting terms between the weight license and the inference code.

Hardware requirements are well-documented for various use cases. Official guides specify that standard inference in FP8 requires 8x H100 or 4x H200 GPUs, while the full 200K context requires 16x H100s. Third-party quantization (GGUF/Ollama) provides additional data on VRAM needs for Q2 through Q8 precision levels. The only gap is the lack of official documentation on the specific accuracy-performance trade-offs for these different quantization levels.

The model follows a clear versioning path (4.5 to 4.6 to 4.7), and changelogs highlight major improvements like context expansion and token efficiency. However, there is a lack of detailed documentation regarding 'silent' updates to the safety filters or alignment layers, which users have noted can affect behavior over time. There is no formal system for accessing specific sub-versions or 'snapshots' once a new iteration is pushed to the main API.