PERFORMANCE Signal 501
GLM-5.3 achieves top-tier intelligence score at below-median cost in Artificial Analysis benchmarks
GLM-5.3 ranks 8th in intelligence among 181 models while pricing input tokens 20% below median and output tokens 56% below median
Engineers selecting large language models for production systems must balance capability against cost. GLM-5.3 demonstrates that high intelligence scores need not come with premium pricing, potentially reducing operational expenses for token-heavy workloads. The model’s verbosity may however increase downstream processing requirements.
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GLM-5.3 scores 60 on the Artificial Analysis Intelligence Index, placing it in the top 5% of 181 benchmarked models
Input tokens cost $1.40 per million (20% below median) and output tokens cost $4.40 per million (56% below median)
The model generated 170 million output tokens during intelligence evaluation, more than twice the median of 72 million
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What the cluster adds up to.
GLM-5.3 achieves an intelligence score of 60 on the Artificial Analysis Intelligence Index, ranking 8th out of 181 models. This places it among the top-tier proprietary models, outperforming the median score of 35 by a substantial margin. The model’s intelligence performance is comparable to leading offerings from established providers, yet its pricing structure diverges significantly from industry norms.
Pricing for GLM-5.3 is set at $1.40 per million input tokens and $4.40 per million output tokens. These rates are 20% and 56% below the respective medians of $1.75 and $10.00, positioning the model as a cost-effective option for high-volume inference tasks. The total evaluation cost of $1238.50 reflects this pricing advantage, though the model’s verbosity may offset savings by increasing token consumption.
The model’s 170 million output tokens during intelligence evaluation exceed the median of 72 million by more than 130%. This verbosity, while potentially beneficial for detailed responses, may increase processing overhead for applications requiring concise outputs. Engineers must weigh this trade-off against the model’s intelligence and cost advantages when integrating it into production systems.
GLM-5.3 supports a 1 million token context window, enabling processing of large documents or multi-turn conversations without truncation. This capability is particularly valuable for applications involving extensive context retention, such as legal analysis or code generation. However, the model’s speed metrics remain unreported, leaving latency considerations unaddressed in the current benchmarks.
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