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GLM-5.3-Flash Intelligence, Performance and Price Analysis
GLM-5.3-Flash is amongst the leading models in intelligence and well priced when comparing to other models of similar price. The model supports text input, outputs text, and has a 400k tokens context window.
GLM-5.3-Flash scores 57 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 18). When evaluating the Intelligence Index, it generated 150M tokens, which is very verbose in comparison to the median of 64M.
Pricing for GLM-5.3-Flash is $0.15 per 1M input tokens (competitively priced, median: $0.25) and $0.50 per 1M output tokens (competitively priced, median: $0.90). In total, it cost $138.02 to evaluate GLM-5.3-Flash on the Intelligence Index.
This page shows the reasoning version of this model.
Metrics are compared against models of the same class:
Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.
Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.
Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.
While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.
Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.