InternVL3 5 30B A3B Alternatives

InternVL3_5-1B is a 1-billion-parameter multimodal model developed by OpenGVLab. It processes both images and text, supporting vision-language tasks such as image captioning, visual question answering, and multimodal chat. The model is available on Hugging Face and optimized for local or self-hosted inference.

InternVL3.5-8B is an 8 billion parameter multimodal model from OpenGVLab that processes both images and video alongside text. It supports advanced vision-language tasks including visual question answering and document understanding. The model is hosted on Hugging Face and can be used for local inference or integrated into custom applications.

InternVL2_5-4B is a multimodal large language model hosted on Hugging Face under the OpenGVLab organization. The model accepts both image and video inputs together with text and follows a chat-style conversation format that includes special tokens for different content types. Its tokenizer configuration defines specific system prompts that identify it as 书生·万象 with the English name InternVL. The prompt template supports roles for system, user, and assistant messages and inserts placeholder tokens such as <image or <video when the content type requires them. The end-of-sentence token is defined as <|im_end|, the beginning-of-sentence marker as <|im_start|, and padding uses <|endoftext|. The model page records over 80000 recent downloads and more than 723000 downloads in total. It was created on 2024-11-20 and remains open for community discussions. The repository provides the model weights, configuration files, and a chat template that enables direct inference through the Hugging Face ecosystem. As a foundation model, InternVL2_5-4B is intended for developers and researchers who integrate multimodal understanding capabilities into applications. The page does not specify licensing details, training data, or parameter count.

InternVL3-1B is a 1-billion-parameter multimodal large language model from Shanghai AI Laboratory. It processes images, video, and text and is optimized for efficiency while supporting both English and Chinese.

InternVL3_5-8B-HF is an 8B parameter multimodal model hosted on Hugging Face by OpenGVLab. It belongs to the class of foundation models designed for processing both image and video inputs alongside text. The model includes a dedicated chat template that handles mixed content types. When a message contains multiple content blocks the template checks each one: image entries insert an image context token, video entries insert a video token, and text entries insert the plain text. The template also manages role-based formatting with special start and end tokens and adds an assistant prompt when generation is required. It specifies a tokenizer configuration that sets the pad token to the end-of-text marker. Delivery occurs through the standard Hugging Face repository mechanism. The model can be loaded via the Transformers library using the repository identifier OpenGVLab/InternVL3_5-8B-HF. No additional inference providers are listed as available at the time of the repository metadata. The entry was created on 29 August 2025 and last modified on 8 September 2025. No pricing, licensing terms, or intended user roles are stated in the repository metadata. The page provides only the model card skeleton and configuration details without further description of capabilities or target audience.

InternVL3-8B is an 8 billion parameter multimodal model developed by Shanghai AI Laboratory and partners. It processes images, videos, and text together, enabling powerful vision-language tasks. The model includes a detailed system prompt in both Chinese and English and is designed for researchers and developers working on advanced multimodal AI applications.

InternVL3-1B-hf is a 1-billion-parameter vision-language model developed by OpenGVLab. It supports multimodal inputs including images, video, and text, and uses a custom chat template for conversational interactions. The model is available in Hugging Face format and can be used for various vision-language tasks.

InternVL3-78B-AWQ is a large multimodal model from Shanghai AI Laboratory and collaborators. It processes images, videos, and text using a unified approach. The AWQ quantized version enables more efficient inference while maintaining strong performance across vision-language tasks. It is accessible via the Hugging Face ecosystem.

InternVL3.5 GPT-OSS 20B is a preview release of a large open-source multimodal model developed by OpenGVLab. It combines vision understanding with language generation and supports tool-calling capabilities. The model is designed for research and development of advanced vision-language systems.

OpenGVLab's InternVL2_5-8B-AWQ is a quantized version of the InternVL2.5 vision-language model. It supports image-text-to-text tasks and is optimized for lower resource usage. The model is hosted on Hugging Face and integrates with the Transformers library for multimodal applications.

Step3-VL-10B is a 10-billion parameter vision-language model developed by StepFun AI. It supports image and text inputs and includes advanced features such as function/tool calling. The model is distributed on Hugging Face with support for pip and Docker deployment, making it suitable for local inference or integration into custom AI applications.

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.