Kimi K3: Inside the First 3-Trillion-Class Open-Weight AI Model

Kimi K3 is the world's first open-weight AI model in the 3-trillion-parameter class. It delivers frontier-scale performance with an architecture designed for complex reasoning and long-running tasks.

Unlike most models of its size, Kimi K3 can be downloaded, self-hosted, and fine-tuned for your own applications, giving teams direct control over how they deploy and adapt it. Here's a closer look at what it's all about.

What Is Kimi K3?

Introduced by Moonshot AI in July 2026, Kimi K3 is a 2.8-trillion-parameter large language model.

Despite its enormous size, the model's Mixture-of-Experts (MoE) architecture activates just 16 of 896 experts for each token, meaning only about 104 billion parameters are active at any given time. This dramatically reduces computational overhead during inference.

Kimi K3 was designed for demanding reasoning workloads, including long-horizon coding, complex document analysis, deep research, and multimodal problem solving. Built-in thinking modes with low, high, and max effort settings trade off speed and reasoning depth depending on the task.

The model supports native text, image, and video understanding alongside a one-million-token context window, allowing it to analyze large volumes of information in a single session. Moonshot AI reports roughly 2.5x better overall scaling efficiency than its predecessor, Kimi K2.

Kimi K3 has also drawn attention for narrowing the performance gap between open-weight and proprietary models. In Moonshot AI's published evaluation, Kimi K3 was competitive with frontier proprietary models across coding, reasoning, agentic, and multimodal benchmarks, with results varying by task and evaluation harness.

Here's a quick snapshot of Kimi K3's vital stats:

How Does Kimi K3 Work?

Kimi K3 combines several architectural innovations to improve both efficiency and reasoning performance.

Kimi Delta Attention (KDA) is a linear attention mechanism that continuously updates the model's memory as it processes new tokens. Rather than treating every piece of information equally, KDA helps Kimi K3 decide what to remember or forget.

Attention Residuals (AttnRes) address a challenge that can arise as models become deeper: information from earlier layers can be diluted over time. In Kimi K3, instead of each layer being limited to the output of the layer immediately before it, AttnRes allows each layer to selectively retrieve useful information from earlier layers when needed. This creates a shorter information path through the network and improves training efficiency.

Stable LatentMoE organizes Kimi K3 into hundreds of specialized experts and activates only the subset needed for each token - roughly 1.8% of the pool. This provides 56x sparsity, allowing the model to make more efficient use of computational resources.

Pushing MoE sparsity this far can cause training instability. Kimi K3 addresses this with techniques designed to maintain stable training and distribute work more effectively across its experts.

Kimi K3 also differs from many multimodal models in how it handles visual inputs. Moonshot AI trained its MoonViT-V2 vision encoder from scratch alongside Kimi K3. By learning text and visual information together, the model improved training stability while maintaining competitive performance on vision benchmarks.

What Can Kimi K3 Do?

Kimi K3 was built with complex, multi-step workflows in mind. Some of its standout capabilities include:

• Long-horizon coding - It can understand large codebases, sustain long-running programming tasks, coordinate terminal-based tools with minimal human supervision, and maintain context across extended development sessions.
• Knowledge work - Kimi K3 can process large volumes of information, connect insights across multiple sources, and produce detailed outputs that require sustained reasoning.
• Multimodal understanding - Its native visual understanding allows it to reason across text and visual inputs, including screenshots, diagrams, images, and video.

Kimi K3 still has limitations. Moonshot AI notes that the model may act without seeking clarification in ambiguous scenarios and that some agent frameworks can reduce performance if they alter its preserved thinking history. Results will also depend on the task, inference engine, quantization, and agent harness.

Nonetheless, Kimi K3's capabilities make it a powerful foundation for advanced AI applications without tying developers to a proprietary model.

But before developers can build with it, they need a way to deploy and run it.

Getting Started with Kimi K3

At the 3-trillion-class scale, Kimi K3 requires a massive amount of compute. Its MXFP4 weights alone represent roughly 1.4 TB of data before runtime overhead. An eight-GPU enterprise node of NVIDIA B300s can provide the memory and compute needed to run the flagship model, but owning that kind of infrastructure is cost-prohibitive for many teams.

For developers who want to experiment with Kimi K3, Vast.ai provides a flexible alternative through GPU rental. You gain access to the same class of hardware on demand without the expense of purchasing it outright, and you pay only for the compute you use.

With Vast.ai's Kimi K3 Model page, you can launch ready-to-run vLLM or llama.cpp templates on compatible GPU configurations. Select a model variant, choose your hardware, and start running AI workloads in minutes.

Ready to build? Launch Kimi K3 on Vast.ai today.