Moonshot AI's K3 matches GPT-5.5 and Claude Opus 4.8 on independent benchmarks, but its pricing signals the end of ultra-cheap Chinese frontier AI.
Moonshot AI has released Kimi K3, a 2.8-trillion-parameter multimodal model that independent testing places on par with OpenAI’s GPT-5.5 and Anthropic’s Claude Opus 4.8 — while pricing it well above its own predecessor and other Chinese open-weight rivals [1].
The launch matters for developers and AI buyers because K3 is the first open model in the roughly 3-trillion-parameter range, according to Kimi, and its full weights are scheduled for public release by July 27 [1]. At the same time, its token prices represent a sharp break from the rock-bottom rates that have defined Chinese AI offerings over the past year [1].
What the benchmarks show
Independent testing lab Artificial Analysis scored K3 at 57 on its Intelligence Index, putting it level with Opus 4.8 and GPT-5.5 but behind Claude Fable 5 and GPT-5.6 Sol [1]. That result broadly matches Kimi’s own benchmark claims, though Kimi’s internal tests were run at maximum or high thinking intensity and used different agent systems — KimiCode, Claude Code, or Codex — depending on the task, meaning conditions were not identical across all 35 tests [1].
On agentic tasks, Artificial Analysis gives K3 an Elo rating of 1,668 on GDPval v2, up from 1,190 for its predecessor K2.6 [1]. That puts it ahead of GLM-5.2 (1,514), GPT-5.5 (1,494), and Claude Opus 4.8 (1,600), though Claude Fable 5 still leads at 1,760 [1]. K3 also tops AutomationBench-AA, Artificial Analysis’s agentic software-as-a-service workflow evaluation, with a score of 53 percent [1].
One area of concern: K3’s hallucination rate climbed from 39 percent to 51 percent compared with K2.6, even as its accuracy on the AA-Omniscience Index improved from 33 percent to 46 percent [1]. The model gets more questions right, but it also fabricates more answers.
Architecture and capabilities
K3 uses a mixture-of-experts (MoE) architecture — a design that routes each input through only a subset of the model’s total parameters — activating 16 of 896 experts at a time [1]. It supports a one-million-token context window and processes images and video natively [1].
A new attention mechanism Kimi calls “Kimi Delta Attention” enables up to 6.3x faster decoding for million-token contexts, the company said, while “attention residuals” improve training efficiency by roughly 25 percent at less than 2 percent additional compute overhead [1].
Kimi positions K3’s primary use case as long-running software development with minimal human oversight, including analyzing large codebases and coordinating terminal tools across many work steps [1]. A feature the company calls “Vision in the Loop” lets the model examine screen captures, modify code, and verify visible output — targeting game development, UI design, and computer-aided design workflows [1].
Pricing and availability
Through the Kimi application programming interface (API), one million input tokens cost $0.30 with a cache hit and $3.00 without; one million output tokens cost $15.00 [1]. Those rates are a significant step up from K2.6, which is priced at $0.16 per million input tokens with a cache hit, $0.95 without, and $4.00 for output [1].
For context, Anthropic’s Sonnet 5 carries the same $3.00 input and $15.00 output pricing but scores lower on Artificial Analysis’s benchmarks, according to The Decoder’s reporting [1]. On a per-task basis, Artificial Analysis estimates K3 costs $0.94 on average, close to GPT-5.6 Sol at $1.04 and roughly half the $1.80 per task for Opus 4.8 — but far above open-weight alternatives like GLM-5.2 at $0.32 and DeepSeek V4 Pro at $0.04 [1].
K3 is already live on Kimi.com, iOS, Android, and HarmonyOS apps, the Kimi Work desktop client (version 3.1.0 and later), and Kimi Code [1]. It is also listed on OpenRouter under the identifier “moonshotai/kimi-k3,” currently served only through Moonshot itself [1]. A forthcoming platform called Kimi Hosted Agent, which will offer isolated environments for long-running tasks, is accepting waitlist sign-ups now [1].
Sources
This article was drafted with AI from the cited sources and checked against them before publication. Spot an error? Let us know.
