GLM-5

GLM-5 ranks #42 out of 230 models on the public BenchAlign leaderboard, with a score of 65.64/100. Its evidence status is Supported, and this profile shows 36 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.

GLM-5 ranks #19 out of 57 eligible models for knowledge and understanding, with a public category score of 77.1/100. 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 ranks #38 out of 148 eligible models for coding and programming, with a public category score of 61.1/100. 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 ranks #7 out of 7 eligible models for mathematics, with a public category score of 56.9/100. 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 has source-displayable benchmark coverage for reasoning and logic, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

GLM-5 ranks #42 out of 143 eligible models for agentic tool use and computer tasks, with a public category score of 55.3/100. 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 ranks #18 out of 43 eligible models for instruction following, with a public category score of 87.4/100. 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 ranks #6 out of 12 eligible models for multilingual tasks, with a public category score of 48.7/100. 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 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 belongs to the GLM-5 family. Related tracked variants include GLM-5.2, GLM-5.1, GLM-5.3, 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 currently has 49 source-displayable rows across 416 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 has a reported context window of 200K in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.