InternVL2 5 4B 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.

InternVL3_5-30B-A3B is a multimodal foundation model hosted on Hugging Face by OpenGVLab. It processes text along with image and video inputs through a unified chat template that inserts special tokens for different content types. The model uses a specific tokenizer configuration with defined pad, bos, and eos tokens. Its chat template supports messages that interleave text with image and video markers, followed by an assistant generation prompt. This structure enables the model to handle mixed-modality conversations in a consistent format. The repository provides the model weights and configuration files for download and local use. It records over 110,000 recent downloads and nearly 760,000 downloads in total. The model was created on August 25 2025 and last modified on August 29 2025. It belongs to the class of foundation models and is delivered as a downloadable asset on the Hugging Face platform. No pricing, licensing details, or specific task benchmarks are stated in the repository metadata.

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.

InternVL3.5 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.

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.

InternVL3-78B-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.

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.

GLM-4V-9B is a multimodal variant of the GLM-4 large language model series. It combines a vision encoder with a 9-billion-parameter language model, enabling it to process images alongside text. The model supports both Chinese and English and is suitable for vision-language tasks such as image captioning, visual question answering, and document understanding.

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.

GLM-4.1V-9B-Thinking is an open-weight multimodal large language model that processes text, images, and video. It features a specialized "thinking" mode for step-by-step reasoning and supports advanced vision-language tasks through a custom chat template.

Granite Vision 4.1 4B is IBM's open multimodal model capable of processing both text and images. It excels at chart-to-code, chart-to-CSV, chart summarization, and table extraction tasks. The model uses a chat template optimized for visual reasoning and is available for local inference through the Hugging Face ecosystem.

NVLM-D-72B is NVIDIA's large vision-language model capable of processing both images and text. It supports advanced tool calling and follows a Qwen-style chat template. The model is designed for complex multimodal reasoning tasks.