GLM-5.3

GLM-5.3 ranks #27 out of 201 models on the public BenchAlign leaderboard, with a score of 65.44/100. Its evidence status is Estimated, and this profile shows 25 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.

GLM-5.3 ranks #33 out of 160 eligible models for knowledge and understanding, with a public category score of 61.9/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

GLM-5.3 ranks #22 out of 136 eligible models for coding and programming, with a public category score of 56.7/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

GLM-5.3 ranks #9 out of 111 eligible models for agentic tool use and computer tasks, with a public category score of 67.3/100. That places it in the current top ten for this category. Check the underlying rows before treating the aggregate as a workload guarantee.

GLM-5.3 is an open-weight model from Z.AI. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.

GLM-5.3 belongs to the GLM-5 family. Related tracked variants include GLM-5, GLM-5.2, GLM-5.1, plus 4 more. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.

No. GLM-5.3 currently has 48 source-displayable rows across 486 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.

GLM-5.3 has a documented context window of 1M. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.