MiniMax M Alternatives

MiniMax-M2.7-NVFP4 is a community-quantized (NVFP4) version of a MiniMax large language model. It includes a custom chat template supporting tool calling and structured output. The model is hosted on Hugging Face and is intended for local inference using compatible frameworks.

This is an NVIDIA-optimized FP4 quantized version of the MiniMax-M3 multimodal foundation model. It supports text, image, and video understanding along with advanced tool-calling features. The quantization enables faster and more memory-efficient inference on NVIDIA hardware while preserving model capabilities.

This is a quantized (MXFP4) version of the MiniMax-M3 model, optimized for efficient inference. It supports image and video inputs, tool calling via XML format, and configurable thinking modes. The model is distributed on Hugging Face and can be used with standard transformer libraries or specialized inference tools.

MiniMax-M2.7 is an open-source large language model hosted on Hugging Face, designed for text generation and natural language processing tasks. It can be run locally or in the cloud, and is accessible via CLI tools and Python packages, making it suitable for AI researchers and developers.

A community-quantized version of a large MiniMax language model (172B parameters with A10B active). It uses NVFP4 and GB10 quantization techniques to make the model more accessible for local or lower-resource inference. The model includes support for tool calling and is distributed on Hugging Face for use with the Transformers library and compatible inference engines.

MiniMax-M2 is an open-weight multimodal foundation model hosted on Hugging Face. It is distributed as part of the MiniMaxAI organization repository and includes template code for handling tool calls, visible text extraction, and system message construction during inference. The model supports rendering of tool namespaces by converting function definitions to JSON within XML-style tags and processes content that may contain text items or strings. Its system prompt construction falls back to an identity statement identifying it as MiniMax when no explicit system message is supplied. These elements appear in a Jinja-style template designed to format inputs for the model. It is delivered as downloadable model files on the Hugging Face platform, where users can access it alongside the associated template for integration with standard inference pipelines. The hosting aligns with Hugging Face's ecosystem for models, datasets, and related resources. No specific pricing, licensing terms, target audience roles, or additional capabilities are stated on the page.

MiniMax-M3 is an open-source multimodal AI foundation model supporting up to 1 million context tokens. It can be run locally via CLI or Docker, enabling advanced inference for text, image, and other modalities. Designed for AI researchers and developers seeking customizable, large-context models.

MiniMax-M2.7-AWQ-4bit is an open-source, quantized large language model designed for efficient local inference. It supports Python and Docker deployment, making it accessible for AI researchers and developers.