Open-weight models are changing the economics of building and deploying AI at scale. Rapid gains in intelligence and efficiency mean companies can match each workload with the right balance of capability, speed, and cost.
What happened
AWS is building for a future in which organizations can adopt open-weight innovation with the reliability and security required for production.
Where the sources line up
Today, Kimi K3 from Moonshot AI is available on Amazon Bedrock, giving you a powerful new option for coding and knowledge work. According to Moonshot AI, Kimi K3 is its most capable model and the first open model to reach 2. 8 trillion parameters. It combines native vision capabilities with a 1-million-token context window and delivers an approximate 2. 5x improvement in scaling efficiency over Kimi K2. These advances make Kimi K3 well suited to long-running coding and knowledge workflows that require sustained context across large repositories, documents, and images. Kimi K3 is the first open-weight model on Amazon Bedrock to support explicit prompt caching , helping you reduce latency and input costs when reusing context across model calls.
Practical impact for readers
The launch of Kimi K3 reflects the sustained investment by AWS in open-weight models on Amazon Bedrock. Since 2025, Bedrock has added dozens of open-weight models from providers including DeepSeek, Google, MiniMax, Mistral AI, Moonshot AI, NVIDIA, OpenAI, and Qwen. Supporting this expanding selection is continued advancement of the inference technology that serves these models at scale. In 2026, Bedrock added support for tool calling, structured output, reasoning, response streaming, and the Responses and Chat Completions APIs. Because these are platform capabilities rather than per-model integrations, new open-weight models can benefit from them as they become available on Amazon Bedrock.
Who should pay attention now
As with all open-weight models on Amazon Bedrock, you can adopt Kimi K3 without changing your security posture. Your data is processed within the AWS data boundary, is not shared with the model provider, and is not used to train the underlying model. Zero data retention is always enabled for inference requests, while zero operator access prevents even AWS operators from accessing your prompts and completions during inference. Together, these protections let you use open-weight models with confidence while maintaining control of your data.
What is still unclear
To try Kimi K3, open the Amazon Bedrock console , go to Test > Playground , and select Kimi K3 as the model. From there, you can test your first prompt.
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