Explore the OpenGVLab: InternVL3 78B Model

InternVL3 78B excels in multimodal perception and reasoning.

Input: image · text Output: text Context: 32,768 tokens Release: 2025-09-15

Discover OpenGVLab's InternVL3 78B, a multimodal large language model released in September 2025. This model excels in processing both text and images, generating text outputs with a substantial context length of 32,768 tokens. Built on the foundation of the Qwen2.5 Chat models, InternVL3 enhances multimodal perception and reasoning, making it a versatile tool for complex tasks. With advanced native multimodal pre-training, it offers improved text performance, making it suitable for a range of applications that require integrating visual and textual data.

Use Cases

Here are a few ways teams apply OpenGVLab: InternVL3 78B in practice—from fast drafting to multimodal understanding. Adapt these ideas to your workflow.

Integrate visual and textual data

Enhance multimodal reasoning tasks

Improve text performance in applications

Utilize large context for detailed outputs

Key Features

A quick look at the capabilities that make this model useful in real projects.

Processes text and image inputs

Generates text outputs

32,768 token context length

Enhanced multimodal reasoning

Built on Qwen2.5 Chat models

Native multimodal pre-training

Specs

Overview

Vendor
opengvlab

Model ID
opengvlab/internvl3-78b

Release
2025-09-15

Modalities & context

Input
image · text

Output
text

Context
32,768 tokens

Parameters & defaults

Supported parameters: frequency_penalty, max_tokens, presence_penalty, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, top_k, top_p

Defaults: temperature 0.2, top_p 0.95

Benchmark tests: OpenGVLab: InternVL3 78B

We ran this model against a few representative prompts to show its range. Review the outputs below and be the judge.

Text

Prompt:

Write 150 words on how AI might positively upend work, leisure and creativity

The OpenGVLab InternVL3 78B is a large-scale visual-language model designed for various tasks in natural language processing and computer vision. It features advanced capabilities in image understanding, text generation, and multimodal interactions, making it suitable for applications such as image captioning, visual question answering, and content generation based on visual inputs. The model is trained on a diverse dataset, allowing it to generalize across different domains and contexts. Notable constraints include the requirement for substantial computational resources for optimal performance, as well as potential limitations in handling highly specialized or niche content that may not be well-represented in the training data. Additionally, users should be aware of the model's sensitivity to input quality, as ambiguous or unclear images may lead to suboptimal outputs. Overall, the InternVL3 78B serves as a versatile tool for developers and researchers looking to integrate visual and textual data processing capabilities into their applications.

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Image

Prompt:

Generate an SVG of a pelican riding a bicycle.

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up.end

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verb

To “upend” means to completely disrupt, overturn, or drastically change the established order or structure of something. It implies a significant shift or alteration that can potentially have far-reaching consequences. When something is upended, it is turned upside down or transformed in a way that challenges conventional norms or expectations. The term often carries a sense of innovation, transformation, and sometimes even a hint of upheaval, indicating that the changes are not just minor adjustments but rather a fundamental reimagining of the status quo.

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