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

1. What is Kimi K3

Kimi K3 is a 2.8 trillion-parameter open-source large model officially released by Moonshot AI on July 16, 2026. It is built upon the KDA hybrid linear attention mechanism and attention residual technology, natively supports visual understanding, and features a 1 million token context window. As the first open-source model to reach the 3 trillion parameter level globally, Kimi K3 is designed for cutting-edge intelligent scenarios such as long-range programming, knowledge work, and reasoning, achieving state-of-the-art performance in programming, Agent, and knowledge work evaluations. The model employs a Stable LatentMoE sparse architecture, efficiently activating only 16 out of 896 experts, significantly enhancing computational efficiency and parameter utilization. The full model weights are planned to be released by July 27, 2026.

Image source: Official article
Image source: official article

Technical Positioning and Domain: Kimi K3 belongs to the large-scale language model domain, specifically positioned as an Agent-type large model designed for long-range autonomous tasks. Its core application areas include large-scale software engineering, GPU kernel optimization, compiler development, interactive 3D content creation, chip design, and scientific research programming—high-complexity scenarios requiring prolonged reasoning and execution. Unlike traditional conversational models, K3 emphasizes "long-range autonomy," enabling it to independently complete multi-step, multi-tool coordinated engineering tasks over hours or even days.

Development Background: This model was developed by the Moonshot AI team. Previously, Moonshot AI had launched a series of models including Kimi K2 and Kimi K2.7 Code, accumulating a solid technical foundation in long-text processing. The motivation behind K3's development was to break through the bottlenecks of open-source models in terms of parameter scale and long-range autonomous capabilities, allowing open-source models to compete with top-tier closed-source models in complex engineering tasks. Over the past 12 months, K3 has maintained the record for the upper limit of open-source model parameter scale for 9 months.

Core Value: Kimi K3 addresses the reliability issues of open-source models in long-range complex tasks. By leveraging its 2.8 trillion-parameter scale advantage, 1 million token context window, and native visual understanding capabilities, it enables developers to deploy an Agent system locally that can independently handle time-consuming tasks such as large-scale software engineering and chip design. In multiple benchmarks including Terminal Bench, Program Bench, and BrowseComp, K3 achieves performance comparable to or even surpassing that of top-tier closed-source models at significantly lower inference costs, greatly reducing the barrier to entry for high-performance AI Agents.

Technical Features: The core technical advantage of Kimi K3 lies in its KDA hybrid linear attention mechanism, which improves the attention calculation method to allow for smoother information flow across long sequences and deep model layers. The introduction of attention residual technology resolves gradient degradation issues in deep networks, ensuring stable training and inference within the MoE architecture of 896 experts. Additionally, the combination of native visual support and a 1 million token context window enables seamless iteration between code and real-time screenshots, forming a true visual feedback optimization loop.

2. Key Features

• Long-range programming and codebase understanding: Kimi K3 can independently understand and analyze large codebases, completing complex development tasks that would take hours or even days. It has the capability to coordinate the use of terminal tools and can autonomously execute commands, debug code, and submit changes within a development environment, achieving full automation from requirement analysis to code delivery. In software engineering benchmarks such as DeepSWE and FrontierSWE, K3 achieved scores of 67.5 and 81.2 respectively, demonstrating engineering capabilities close to those of top-tier closed-source models.

• Visual reasoning and closed-loop optimization: The model natively supports visual understanding, combining software engineering with visual reasoning to optimize the development process using screenshots and visual feedback. In scenarios such as game development, front-end development, and CAD, K3 can view interface screenshots, identify visual issues, generate modified code, and revalidate it, achieving iterative optimization through a "vision in the loop" feedback cycle. This capability gives it a significant advantage in interactive creation scenarios requiring visual feedback.

• GPU kernel optimization: Kimi K3 can independently analyze, rewrite, and verify GPU kernel optimizations within a GPU sandbox environment, supporting continuous iteration within 24 hours. It can identify performance bottlenecks, generate optimized CUDA or Triton kernel code, and validate performance improvements in a simulated environment. This feature provides automated low-level optimization capabilities for high-performance computing scenarios.

• Compiler development: The model can build a Triton-like GPU compiler (MiniTriton) from scratch, implementing a complete optimization-to-PTX code generation pipeline. This includes lexical analysis, syntactic analysis, intermediate representation generation, optimization, and code generation. K3 demonstrates its understanding and generation capabilities for low-level system software in this task, a rare feature among current large models.

• Interactive 3D content creation: By integrating 3D reasoning, programming, and visual capabilities, Kimi K3 can transform conceptual descriptions into playable interactive experiences. Users only need to provide textual descriptions or reference screenshots, and the model can generate 3D scenes, game logic, and interactive interfaces, iteratively optimizing them through visual feedback. This capability reduces the technical barriers to 3D content development.

• Chip design: Based on open-source EDA tools, Kimi K3 can autonomously complete chip architecture design, optimization, and verification. It can run the full chip design workflow, including RTL writing, logic synthesis, layout and routing, timing analysis, and functional verification, achieving "model-driven chip design." Over a 48-hour Agent run, K3 can continuously iterate on the design, optimizing chip performance, power, and area (PPA).

• Scientific programming and data analysis: Kimi K3 can bridge scientific literature and executable code, autonomously implementing, verifying, and analyzing complex computational research workflows. It can read mathematical formulas from academic papers, convert them into numerical computation code, run simulation experiments, detect inconsistencies in published formulas, and generate interactive data dashboards. This capability covers the complete research workflow from literature comprehension to result visualization.

3. How to Use

1. Official Entry for Direct Use: Access the Kimi official website (kimi.com), the latest version of the Kimi mobile app, or the Kimi Work desktop client to directly invoke the Kimi K3 model for conversations. Users require no configuration; after logging in, they can switch to K3 in the model selection. The default setting currently enables the "max" (extreme) thinking mode, where the model utilizes maximum computational resources for deep reasoning. Future updates will support "low" and "high" thinking intensity levels, allowing users to flexibly switch based on task complexity.

2. Programming-Specific Client: Use the Kimi Code client to leverage the K3 model, which is optimized for development scenarios such as long-term programming, codebase understanding, and terminal tool coordination. Kimi Code offers both an IDE plugin and a standalone client, supporting features like code auto-completion, code review, terminal command execution, and version control operations. Within Kimi Code, the K3 model ID is "k3," alongside Kimi K2.7 Code (kimi-for-coding). Users can choose based on the complexity of their tasks.

3. API Integration: Integrate K3 into your own applications or workflows by calling the Kimi API. The API endpoint is located at platform.kimi.com and supports RESTful interface calls. The K3 API enables automated task scheduling, third-party system integration, and batch processing. Developers need to register for a Kimi Open Platform account to obtain an API key and then configure request parameters according to the API documentation. The current API supports text and image input, with a context window of 1 million tokens. For specific pricing and rate limits, please refer to the official API documentation.

4. Model Weight Deployment (Pending Release): The full model weights are planned to be released before July 27, 2026. At that time, developers will be able to download the model weights from the official GitHub repository or HuggingFace. The model uses the MXFP4 quantization format, enabling inference on consumer-grade GPUs. It is recommended to use a GPU with at least 80GB of VRAM (such as NVIDIA A100 or H100), or to use multi-GPU distributed inference. For deployment, it is necessary to use an inference framework that supports the MoE architecture, such as vLLM or TGI. Moonshot AI is collaborating with inference partners and open-source maintainers to ensure the model can be reliably deployed across the entire ecosystem.

5. Swarm Workstation Multi-Agent Deployment: Kimi K3 supports the Swarm Workstation mode, allowing up to 300 agents to work collaboratively. Each agent can independently execute subtasks and coordinate to achieve complex engineering goals through shared context and results. Instructions for downloading and using the Swarm Workstation will be updated simultaneously with the release of the model weights. This mode is suitable for scenarios requiring multi-agent collaboration, such as large-scale software refactoring and chip design.

4. Pros and Cons Analysis

5. Comparative Analysis with Similar Tools

Selection Recommendations: For enterprise users seeking top-tier performance and full functionality, if budget allows and local deployment is not required, Claude Fable 5 performs best in multiple evaluations, particularly excelling in software engineering tasks such as DeepSWE and FrontierSWE. However, for scenarios requiring local deployment, data privacy protection, or limited budgets, Kimi K3, as an open-source model, offers a highly competitive option. Its 2.8T parameter scale and 1 million token context window excel in long-range programming and Agent tasks, with lower inference costs.

For users prioritizing Chinese scenarios or requiring integration with the Alibaba Cloud ecosystem, Qwen3.8-Max-Preview provides strong Chinese support. For those seeking the highest cost efficiency and a mature community ecosystem, DeepSeek V4-Pro's MIT license and established community support make it a reliable choice. If the task involves visual feedback loops (such as game development or front-end development), Kimi K3's native visual support gives it a unique advantage. Overall, K3 is best suited for development teams requiring long-range autonomous Agent capabilities, willing to accept a certain level of deployment complexity, and valuing open-source and cost control.

6. Editor's Summary

The release of Kimi K3 marks a new phase for open-source large models, entering the 3 trillion parameter scale. From a technological innovation perspective, the combination of its KDA hybrid linear attention mechanism and attention residual technology provides an engineering solution to address challenges in long-sequence processing and deep network training stability in ultra-large MoE models. The design that activates only 16 experts among 896 ensures that the 2.8 trillion parameter model maintains controllable computational costs during inference, which is key to achieving performance close to top-tier closed-source models at a relatively low cost in multiple evaluations. Notably, K3 achieved first place in evaluations such as Terminal Bench, Program Bench, and BrowseComp, demonstrating its practical advantages in Agent tasks and long-term autonomous capabilities. These evaluations are more aligned with real-world application scenarios than traditional academic benchmarks.

In terms of practical value, K3's "long-term autonomy" capability is its most core differentiating feature. Supporting 24-hour GPU kernel optimization and 48-hour chip design Agent operations means the model can handle complex engineering tasks that require continuous reasoning and execution—an ability that is currently out of reach for most models. For development teams in fields such as large-scale software engineering, chip design, and compiler development, K3 offers a potential automation solution that can significantly enhance development efficiency. Additionally, the integration of native visual understanding with a 1 million token context provides a unique advantage in scenarios requiring visual feedback.

This model is primarily targeted at professional developers and researchers, especially teams working in large-scale software engineering, high-performance computing, chip design, and scientific research computing. For users requiring local deployment, data privacy protection, or with limited budgets, K3, as an open-source model, offers a highly competitive option. However, its extremely high hardware requirements and partially unreleased weights make it more suitable for use through API or official client interfaces at this stage. In the future, with the official release of model weights and the maturation of the community ecosystem, K3 is expected to play a role in more scenarios, particularly in areas requiring long-term autonomous Agent capabilities. Moonshot AI is collaborating with inference partners and open-source maintainers to ensure the model can be reliably deployed across the entire ecosystem, which will further reduce the barrier to entry.

7. Application Scenarios

• Large Software Engineering and Code Refactoring: Kimi K3 can independently analyze and refactor large codebases, continuously completing complex development tasks that would otherwise take hours or even days. Development teams can hand over the entire project repository to K3, specifying refactoring goals and constraints, and the model will autonomously perform code analysis, modification, testing, and validation. In the DeepSWE and FrontierSWE benchmarks, K3 achieved scores of 67.5 and 81.2 respectively, demonstrating its capabilities in real-world software engineering tasks.

• GPU Kernel and Compiler Optimization: In high-performance computing scenarios, K3 can automatically optimize GPU kernels or build domain-specific compilers from scratch within a GPU sandbox environment. Developers only need to provide the original kernel code or a description of compiler requirements, and K3 can continuously iterate and optimize within 24 hours, generating more efficient CUDA or Triton kernel code. This capability holds practical value for AI training, scientific computing, and graphics rendering scenarios that demand peak performance.

• Interactive 3D Content Development: By combining visual feedback for rapid iteration, Kimi K3 can transform concepts into playable 3D games, simulators, or visualizations. Game developers can describe game mechanics and visual styles, and K3 will generate 3D scenes, game logic code, and interactive interfaces, iteratively optimizing through screenshot feedback. This scenario fully leverages K3's visual reasoning and code generation capabilities.

• Scientific Computing and Data Analysis: Research teams can use K3 to autonomously complete the entire scientific research process, from literature review to result visualization. The model is capable of reading academic papers, understanding mathematical formulas, converting them into numerical computation code, running simulation experiments, identifying inconsistencies in published formulas, and generating interactive data dashboards. This capability spans multiple fields, including computational science, physics, and engineering.

• Hardware Chip Design: Based on open-source EDA toolchains, Kimi K3 can autonomously complete chip architecture design, optimization, and verification. Chip design teams can describe design specifications and performance goals, and K3 will execute the full chip design workflow, including RTL coding, logic synthesis, layout and routing, timing analysis, and functional verification, continuously iterating and optimizing the design during an Agent runtime that can last up to 48 hours.

8. FAQ

Q: When will the model weights for Kimi K3 be available for download?
A: The full model weights are planned to be released before July 27, 2026. Moonshot AI is currently working closely with inference partners and open-source maintainers to align technical details and ensure the model can be reliably deployed across the entire ecosystem. At that time, developers will be able to download the weights from the official GitHub repository or HuggingFace. More details about the architecture, training, and evaluation will be published alongside the Kimi K3 technical report.

Q: What kind of hardware is required to run Kimi K3?
A: Due to its total of 2.8 trillion parameters, even with the MoE architecture activating only 16 experts (approximately 500 billion parameters), full inference still requires a high-end GPU with at least 80GB of VRAM, such as NVIDIA A100 (80GB), H100, or higher configurations. Consumer-grade GPUs (e.g., 24GB VRAM on the RTX 4090) cannot directly run the full model. Moonshot AI has adopted the MXFP4 quantization format to reduce VRAM requirements, but specific deployment requirements will be determined by real-world testing data after the weights are released.

Q: What is the difference between Kimi K3 and Kimi K2.7 Code?
A: Kimi K3 is Moonshot AI's flagship model, surpassing K2.7 Code comprehensively in parameter scale (2.8T vs. approximately 1T), context window (1 million vs. approximately 128K), and visual capabilities (native support vs. text-based). Kimi K3 is designed for complex engineering tasks requiring long-range autonomous reasoning and visual feedback, while K2.7 Code is more suitable for everyday code assistance. Both models are available side-by-side in the Kimi Code client, allowing users to choose based on task complexity.

Q: What languages does Kimi K3 support?
A: Kimi K3 is primarily optimized for Chinese and English scenarios, performing well on both Chinese and English tasks. Its training data covers multiple programming languages (Python, C++, CUDA, Triton, JavaScript, TypeScript, etc.) and natural languages. In evaluations requiring web understanding, such as BrowseComp, K3 has demonstrated cross-language information retrieval capabilities. More detailed information about support for other languages will be available after the technical report is published.

Q: What is the API call cost for Kimi K3?
A: Please refer to the official documentation of the Kimi Open Platform for specific pricing information. According to industry standards, as a flagship model, K3's API call cost will be higher than that of the next-generation models like K2.7. However, Moonshot AI emphasizes that K3 achieves performance close to top-tier closed-source models at a lower cost in multiple evaluations, indicating that its cost-effectiveness is competitive. It is recommended that users compare the performance of K3 and K2.7 Code in the Kimi Code client based on their actual task requirements and choose the model with the most cost-effective option.

9. Project Links

• Kimi API Open Platform (Official Documentation and Quick Start Guide): https://platform.kimi.com/docs/guide/kimi-k3-quickstart
• Kimi K3 HuggingFace Model Page (Weights to be Released): https://huggingface.co/blog/ResterChed/kimi-k3-model-overview-mxfp4-quantization-open-wei
• Kimi Official Website: https://kimi.com
• Kimi K3 Status Page (Changelog and Release Information): https://kimi-k2.org/zh/kimi-k3-status