Kimi K3 Alternatives

Kimi-K3 is a multimodal foundation model developed by Moonshot AI that supports image-text-to-text tasks. It can analyze images and answer questions about their content while also handling standard text-based conversations. The model is available on Hugging Face with support for the Transformers library and can be deployed locally or via inference providers.

Kimi-K2-Instruct is a large instruction-tuned model from Moonshot AI that supports multimodal inputs including text and images. It features advanced capabilities such as tool calling and follows a custom chat template. The model is designed for complex reasoning tasks and is available on Hugging Face for local or hosted inference.

Kimi-K2.5 is an open-source large language model developed by moonshotai for text generation and conversational AI. It can be run locally via CLI and is suitable for research, experimentation, and building AI-powered applications.

Kimi-K2.6 is an open-source large language model available on Hugging Face, designed for local text generation and research. It supports Python and Docker deployment, enabling developers and researchers to run advanced language models on their own infrastructure.

Kimi-K2-Thinking is a state-of-the-art reasoning model released by Moonshot AI. It features a sophisticated chat template supporting tool calls, multimodal (image and text) inputs, and extended thinking capabilities. The model is designed for complex problem solving and agentic workflows and is available on Hugging Face for integration into developer applications.

Kimi-K2-Instruct-0905 is a large instruction-tuned model developed by Moonshot AI. It supports multimodal inputs including text, images, and video, along with advanced tool calling and structured generation. The model is available with open weights on Hugging Face for local deployment or use through compatible inference frameworks.

Kimi-VL-A3B-Thinking is an open-source 3B parameter vision-language model developed by Moonshot AI. It supports multimodal inputs including images and text, features a specialized "thinking" mode, and uses a custom chat template. The model is designed for local deployment and research in multimodal AI applications.

Kimi K2.7-Code is an open-source AI model designed for efficient code generation and completion. It offers improved token efficiency and can be used via API or CLI, making it suitable for developers and researchers seeking customizable AI coding solutions.

Kimi API Platform offers developers access to the K2.7 Code large language model via API, supporting advanced code generation, long context handling, and tool calling. It is designed for building AI-powered applications and intelligent code assistants.

Kimi-VL-A3B-Instruct is a 3-billion-parameter multimodal model developed by Moonshot AI. It processes both image and text inputs and generates coherent responses following user instructions. The model uses a specialized chat template and is designed for vision-language tasks including visual question answering and image captioning.

Kimi-K2.5-NVFP4 is an open-weight multimodal foundation model published by NVIDIA on Hugging Face. It supports text, image, video, and tool-calling capabilities through a custom chat template. The model is designed for local inference and developer integration via the Hugging Face ecosystem and Transformers library.

Kimi-K2.5 is a community port of the Kimi model optimized for the MLX framework, allowing efficient inference on Apple devices. The model supports advanced features including multimodal inputs (text, image, video), tool calling, and custom chat templates. It is distributed on Hugging Face and can be used with both pip and npm tooling for local deployment.

kimi-k2.6-eagle3-mla is an open-source speculative-decoding draft model designed for use with Kimi-K2.6. It features Multi-Head Latent Attention and is compatible with vLLM, enabling faster and more efficient language model inference for AI researchers and developers.

Kimi AI is a platform that enables users to build full-stack websites, automate document management, and coordinate complex tasks using autonomous agents. It offers features like Agent Swarm for teamwork, document-to-skill conversion, and deep research tools. Designed for developers and professionals seeking to streamline workflows.

Chat with Kimi-VL-A3B-Thinking-2506 is a web-based application hosted as a Hugging Face Space by moonshotai. The tool functions as a multimodal assistant, enabling users to interact through typed questions or messages. In addition to text, users have the option to attach an image, a short video, or a PDF with their message. The system then generates a reply based on the provided input. This assistant is designed to facilitate conversational exchanges that incorporate both textual and visual information. By supporting multiple input types—text, images, short videos, and PDFs—it addresses scenarios where users seek responses or analysis that may involve documents or media files, not just plain text. The evidence does not specify particular user roles or industries, but the tool’s conversational interface and support for various media suggest a general-purpose application for users who need to discuss or analyze content across different formats. Delivery is entirely web-based via the Hugging Face platform, with no mention of desktop, mobile, or other deployment models. The evidence does not provide information regarding pricing, licensing, or access requirements, nor does it specify any integrations or advanced features beyond the ability to accept and respond to text, image, video, or PDF inputs. There is no information about the underlying model’s capabilities, privacy, or data handling. As positioned in the evidence, Chat with Kimi-VL-A3B-Thinking-2506 is a multimodal conversational assistant accessible through a web interface, supporting a range of input types for interactive exchanges.

kimi-k2.6-eagle3-mla is an Eagle3 MTP draft model that incorporates Multi-Latent Attention for speculative decoding acceleration of the Kimi-K2.6 model. Hosted on Hugging Face by LightSeek Foundation, it serves as a specialized component in text generation pipelines that employ speculative decoding to improve inference speed. The model was trained using TorchSpec, an online speculative decoding training framework that performs FSDP training and inference concurrently. Its use of Multi-Latent Attention provides two concrete advantages over an MHA draft model when paired with Kimi-K2.6: it consumes less KV cache memory, thereby lowering serving memory pressure, and it aligns with Kimi-K2.6's own MLA architecture for more natural integration into the inference engine's KV cache handling. The model card lists it under the speculative-decoding and eagle3 tags and identifies its primary task as text generation. It can be deployed through vLLM or SGLang. The repository supplies instructions for launching a server with either backend and includes a citation section for academic reference. The model files are provided in Safetensors format. The license is listed as other. No pricing information appears in the repository.