Moonshot AI Releases Kimi K3 Model Weights, Largest Open-Weight AI To Date
In Brief
Moonshot AI releases Kimi K3 open weights, the largest 2.8T-parameter model ever, topping Arena AI benchmarks in agentic and coding tasks.
Moonshot AI, the Chinese AI startup, has released the model weights and technical report for Kimi K3, its latest LLM. The release makes the system available as an open-weight download, permitting developers, enterprises, and government entities with adequate computational infrastructure to deploy, modify, and commercialize the model without seeking permission from Moonshot or maintaining dependency on its proprietary hosting infrastructure.
Kimi K3 comprises 2.8 trillion parameters, with the complete weight file totaling approximately 1.4 terabytes when compressed using MXFP4 quantization—a format designed to reduce storage requirements while preserving most of the model’s inference performance. By both total parameter count and distribution size, it represents the largest open-weight model ever released to the public.
The release arrives during a period of fast price compression across the AI industry. Moonshot’s own API pricing for Kimi K3 starts at $0.30 per million tokens for cached input, undercutting offerings such as OpenAI’s GPT-5.6 at $1 per million tokens and Meta’s Muse Spark 1.1 at $1.25 and $4.25 per million tokens for respective input and output tiers. By removing the API paywall entirely for organizations capable of self-hosting, the open-weight distribution alters the competitive calculus for commercial laboratories that currently charge for access to models of comparable scale, including those that Moonshot has been accused of using as training references.
Benchmark Performance and Technical Evaluation
According to the latest findings, published by independent evaluation platform Arena AI, Kimi K3 (Max) is positioned at the forefront of open-weight model capability. In the Agent Arena, which assesses models on millions of real-world, long-horizon agentic tasks, Kimi K3 (Max) achieved a net improvement of 9.75%, surpassing the previous leader GLM-5.2 (Max) at 7.12%. The model secured top rankings across five distinct evaluation signals.
The Agent Arena methodology grants models access to web search, filesystem, and terminal tools to execute complex workflows including code generation, presentation creation, web research, application development, and document analysis. Arena AI employs causal tracing methodology to measure net improvement, quantifying a model’s performance advantage relative to the average benchmark participant.
Beyond agentic task execution, the AI model claimed the leading position among open-weight models in specialized technical domains, scoring 1,682 points in the Frontend Code Arena and 1,485 points in the Text Arena. The results suggest that the model’s substantial architectural scale translates into measurable capability across both autonomous multi-step workflow execution and domain-specific content generation tasks, reinforcing its position within the open-weight ecosystem.
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About The Author
Alisa, a dedicated journalist at the MPost, specializes in crypto, AI, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
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Alisa, a dedicated journalist at the MPost, specializes in crypto, AI, investments, and the expansive realm of Web3. With a keen eye for emerging trends and technologies, she delivers comprehensive coverage to inform and engage readers in the ever-evolving landscape of digital finance.
