GLM-5.2: built for long-horizon tasks

This archived GLM article retains its original title and publication date. Specific release, architecture and benchmark claims from the earlier version require primary-source verification before reuse. This revision focuses on the evaluation problem behind the headline: completing multi-step work reliably.

A long context window is capacity, not a guarantee that the agent will keep its goal. Give the model a task with intermediate acceptance checks, an explicit stop condition and a bounded budget. Record where it loses state or repeats failed actions.

Compare a complete workflow, not only a single answer. For a code task, inspect the diff, run the tests and verify that unrelated files did not change. A persuasive final summary does not compensate for a broken build.

Sparse attention and other efficiency techniques can reduce some costs, but they do not establish that a large model fits consumer hardware. Check the exact checkpoint, runtime and memory requirements before planning local deployment.

Xavani's replaceable provider layer makes controlled comparisons possible. Keep tools and acceptance criteria stable, pin the model configuration and retain a rollback. Select on measured completion and boundary respect rather than a benchmark superlative.