Z.ai’s GLM-5.3 is now listed among the higher-scoring models on the Artificial Analysis Intelligence Index, according to the benchmark data surfaced in the cluster. The Artificial Analysis page cited through Hacker News says GLM-5.3 (max) scored 60 on the Intelligence Index, while Techmeme’s item citing @artificialanlys says that puts it on par with Kimi K3 and below Opus 5 at 63 and Fable 5 at 62. The benchmark page describes this as the reasoning version of GLM-5.3. It says the model supports text input and text output and has a 1 million-token context window, a spec that matters for workloads involving long documents, extended agent traces, or large code and research contexts. Artificial Analysis’ Intelligence Index v4.1.1 combines several evaluations, according to the provided body text: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity’s Last Exam, GPQA Diamond, CritPt, AA-Omniscience, and AA-LCR. The page describes the index as a composite benchmark rather than a single task score, and says specific evaluations may be more relevant for particular use cases. The cost profile is one of the more concrete parts of the listing. Artificial Analysis reports GLM-5.3 (max) pricing at $1.40 per 1 million input tokens, compared with a median of $1.75, and $4.40 per 1 million output tokens, compared with a median of $10.00. It says the total cost to evaluate the model on the Intelligence Index was $1,238.50. The same data also points to operational trade-offs. Artificial Analysis says GLM-5.3 (max) generated 170 million tokens during the Intelligence Index evaluation, versus a median of 72 million, and characterizes the model as very verbose. It also reports throughput of 74 tokens per second and describes the model as slower than average. For buyers, that creates a more nuanced read than the headline score alone. The benchmark data shows a strong intelligence-index result and lower-than-median listed token prices, but also substantially higher token generation in the evaluation and slower reported speed. For production use, that means the relevant comparison is not only model score, but the combined effect of price per token, output length, latency, and task fit. The evidence in this cluster is still concentrated in one benchmarking source. Techmeme corroborates the headline score and peer comparison by citing @artificialanlys, while the fuller metrics come from the Artificial Analysis page carried in the Hacker News item. There are no material contradictions in the provided items, but there is also no independent second benchmark or deployment data in the cluster. Who benefits: Teams evaluating text-heavy reasoning models may benefit from another benchmarked option with a 1 million-token context window and lower-than-median listed token prices. Benchmark users also get a clearer side-by-side data point against Kimi K3, Opus 5, and Fable 5 as reported by Techmeme’s item. Who's exposed: Buyers who optimize only for headline intelligence scores are exposed to missing the reported verbosity and speed trade-offs. The cluster does not provide enough evidence to identify a specific vendor or model that is commercially disadvantaged.