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Kimi K3.1 Pro

artificialanalysis.aiDR 85 Accessible 300 views

أدوات الذكاء الاصطناعي A detailed analysis page for Kimi K3.1 Pro on Artificial Analysis, covering its intelligence score, output speed, pricing, verbosity, and 1M-token context window alongside comparisons with other AI…

Editor's Review

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Kimi K3.1 Pro page preview

The page stands out for placing a single model under a consistent, multi-axis benchmark rather than marketing claims, letting readers see its #4 intelligence rank next to its slower-than-average speed and heavy verbosity. Cost figures, including cache discounts and cost per intelligence task, make t

Site introduction

Artificial Analysis is an independent benchmarking platform that evaluates AI models across standardized metrics, and this page applies that framework to a single model: Kimi K3.1 Pro, a proprietary release dated October 2026. Rather than a product landing page, it functions as a data sheet, positioning the model within a field of 215 ranked systems so readers can judge it against peers instead of in isolation.

The core content centers on four measured dimensions. The Artificial Analysis Intelligence Index gives the model a score of 55, ranked fourth overall and well above the median of 27. Speed is captured through output tokens per second at 73.5, ranked 112th of 186. Cost is broken into input at $2.00 and output at $10.00 per million tokens, with a 90% cache discount and a derived cost per intelligence task of $2.11. Verbosity is tracked separately, with 150M output tokens generated during the index run versus a median of 87M.

The page also records practical specifications: text and image input, text output, and a 1M-token context window. A comparison summary condenses the findings into plain language, noting strong intelligence at a reasonable price relative to similarly priced models, but slower-than-average throughput and high verbosity. Links to try the model, access API providers, and view benchmarks sit alongside the summary.

This resource suits developers selecting a model for production, technical leads comparing latency and cost budgets, and analysts tracking t

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