GLM-5.1: Towards Long-Horizon Tasks (z.ai)

Article summary

The article about GLM-5.1, a long-horizon task model, could not be fetched. However, the discussion reveals issues with the model's coherence over longer contexts and potential limitations imposed by its hosting platform. Some users have reported significant improvements in the model's performance after switching to alternative hosting services or implementing workarounds. The model's capabilities and limitations are being compared to other similar models, such as Claude and Codex.

Main themes

• GLM-5.1 performance issues
• long-horizon task modeling
• alternative hosting services
• local inference vs external providers
• quantization and technical limitations
• economic feasibility of large models
• guaranteed performance and stability
• batching and compute efficiency

What commenters say

• Some users are experiencing significant issues with GLM-5.1's coherence over longer contexts, which may be related to the hosting platform rather than the model itself.
• The model's performance can be improved by switching to alternative hosting services or implementing workarounds, such as dynamic context pruning.
• The limitations of GLM-5.1 may be due to quantization issues or other technical problems, rather than inherent flaws in the model.
• Running large models like GLM-5.1 locally can be economically unfeasible due to the high cost of required hardware, but may provide guaranteed performance and stability.
• Dependence on external AI inference providers can be risky due to unpredictable performance degradation or price changes.
• Some users believe that local inference using open weight models can provide better performance and stability in the long run, despite potential upfront costs.
• The trade-off between opex and capex is a significant consideration when deciding whether to run large models locally or rely on external providers.
• Batching multiple tasks together can improve compute efficiency, but may also increase the complexity of managing KV-cache and other resources.