Kimi K3 - Moonshot AI's 2.8 Trillion Parameter Open-Source Large Model

What is Kimi K3

Kimi K3 is a 2.8 trillion parameter open-source large model released by Moonshot AI, built on the KDA hybrid linear attention mechanism and attention residual technology, with native support for visual understanding and a 1 million token context window. As the world's first open-source 3-trillion-class model, Kimi K3 is designed for frontier intelligence scenarios such as long-horizon programming, knowledge work, and reasoning, reaching frontier levels in programming, Agent, and knowledge work evaluations.

Kimi K3's Main Features

• Long-Horizon Programming: Supports understanding and handling large codebases, continuously completing long-duration engineering tasks, and coordinating the use of terminal tools.
• Visual Reasoning: Combines software engineering with visual reasoning, using screenshots and visual feedback to optimize game development, frontend, and CAD.
• Kernel Optimization: Independently analyzes, rewrites, and verifies GPU kernel optimizations in a GPU sandbox, iterating continuously within 24 hours.
• Compiler Development: Builds a Triton-like GPU compiler (MiniTriton) from scratch, implementing a complete optimization-to-PTX code generation pipeline.
• Interactive Creation: Integrates 3D reasoning, programming, and visual capabilities to turn concepts into playable interactive experiences.
• Chip Design: Autonomously completes chip construction, optimization, and verification based on open-source EDA tools, realizing chip design in which the model serves itself.
• Scientific Research Programming: Connects scientific literature with executable code, autonomously completing the implementation, verification, and analysis of complex computational research workflows.

Kimi K3's Technical Principles

• KDA Hybrid Linear Attention Mechanism: Kimi K3 is built on Kimi Delta Attention (KDA), a hybrid linear attention mechanism that improves long-context processing by allowing information to flow more smoothly across longer sequences and deeper models.
• Attention Residuals: The model introduces Attention Residuals technology, making information transfer in deep networks more stable and alleviating the gradient decay problem that may occur as model depth increases.
• Stable LatentMoE Sparse Architecture: Adopts a Mixture of Experts (MoE) architecture combined with the Stable LatentMoE framework, efficiently activating only 16 out of 896 experts, greatly improving computational efficiency and parameter utilization.
• Native Vision and Long-Context Support: The model natively supports visual understanding and has a 1 million token context window, capable of handling ultra-long text and multimodal input simultaneously.

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How to Use Kimi K3

• Official Portal: Visit Kimi, the latest version of the Kimi mobile app or the Kimi Work desktop client to directly call the Kimi K3 model for conversation.
• Programming-Specific: Use K3 through the Kimi Code client, optimized for development scenarios such as long-horizon programming, codebase understanding, and terminal tool coordination.
• API Integration: Call the Kimi API to integrate K3 into your own applications or workflows, supporting automated tasks and third-party system integration.
• Thinking Intensity Adjustment: max (extreme) thinking mode is currently enabled by default. Future updates will support two intensity levels, low and high, allowing flexible switching based on task complexity.

Core advantages of Kimi K3

• Leading scale: the world's first open-source 3-trillion-level model, holding the upper limit of open-source model scale for 9 out of the past 12 months.
• Long-horizon autonomy: performs stably in long-duration engineering tasks, supporting 24-hour autonomous kernel optimization and 48-hour chip design Agent operation.
• Cost efficiency: in evaluations such as Kimi Code Bench and BrowseComp, it achieves performance close to top closed-source models at lower cost.
• Vision in the loop: supports seamless iteration between code and real-time screenshots, enabling true visual feedback optimization (vision in the loop).
• End-to-end scientific research: can autonomously read literature, implement numerical workflows, discover inconsistencies in published formulas, and generate interactive dashboards.

Kimi K3 project addresses

• Project official website：https://www.kimi.com/blog/kimi-k3
• GitHub repository：https://github.com/MoonshotAI/Kimi-K3
• HuggingFace model library：https://huggingface.co/collections/moonshotai/kimi-k3
• Technical paper：https://github.com/MoonshotAI/Kimi-K3/blob/main/k3_tech_report.pdf

Comparison of Kimi K3 with similar competitors

Application scenarios of Kimi K3

• Large-scale software engineering: the model can independently analyze and refactor large codebases, continuously completing complex development tasks that take hours or even days.
• GPU kernel and compiler optimization: automatically optimizes GPU kernels for high-performance computing scenarios or builds domain-specific compilers from scratch.
• Interactive 3D content development: rapidly iterates with visual feedback to turn concepts into playable 3D games, simulators, or visualizations.
• Scientific computing and data analysis: autonomously reads literature, reproduces numerical workflows, evaluates large numbers of equations of state, and generates interactive result dashboards.
• Hardware chip design: based on an open-source EDA toolchain, autonomously completes chip architecture design, optimization, and verification, realizing chip design in which the model serves itself.