InternVL

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News

• 2025/08/30: We open-source the training code of InternVL3_5-GPT-OSS-20B-A4B and CascadeRL, which consists of a offline RL stage and a online RL stage. The training data for these two stages (MMPR-v1.2 and MMPR-Tiny) are also open-sourced.
• 2025/08/26: We introduce InternVL3.5, a new family of open-source multimodal models that significantly advances versatility, reasoning capability, and inference efficiency along the InternVL series. Our largest model, i.e., InternVL3.5-241B-A28B, attains state-of-the-art results among open-source MLLMs across general multimodal, reasoning, text, and agentic tasks. We also provide a 20B-A4B version (i.e., InternVL3_5-GPT-OSS-20B-A4B), which is built up on GPT-OSS-20B-A4B. Notably, we provide two model formats: the GitHub format, consistent with prior releases, and the HF format, aligned with the official transformers standard.
• 2025/04/17: We open-source the data construction pipeline and training scripts of MPO and VisualPRM. Additionally, the data construction scripts for MPO and VisualPRM are also released for reference.
• 2025/04/11: We introduce InternVL3, an advanced multimodal large language model (MLLM) series that demonstrates superior overall performance. InternVL3-78B achieves SoTA performance in both perception and reasoning performance among open-source MLLMs. The key designs of InternVL3-78B include Variable Visual Position Encoding, Native Multimodal Pre-Training, Mixed Preference Optimization, and Multimodal Test-Time Scaling.
• 2025/03/13: We introduce VisualPRM, an advanced multimodal Process Reward Model (PRM) with 8B parameters, which improves the overall reasoning performance of InternVL2.5-8B and InternVL2.5-78B by 8.4 and 5.9 points, respectively. The training data for this model, termed VisualPRM400K, is also open-sourced. Please refer to our paper and project page for more details.
• 2024/12/20: We release the InternVL2.5-MPO, which is finetuned with Mixed Preference Optimization on MMPR-v1.1. The resulting models outperform their counterparts without MPO by an average of 2 points across all model scales on the OpenCompass leaderboard. These models are available at HF link.
• 2024/12/17: InternVL2/2.5 is supported in PaddleMIX by Paddle Team.
• 2024/12/05: We release the InternVL2.5, an advanced multimodal large language model (MLLM) series with parameter coverage ranging from 1B to 78B. InternVL2_5-78B is the first open-source MLLMs to achieve over 70% on the MMMU benchmark, matching the performance of leading closed-source commercial models like GPT-4o. These models are available at HF link.
• 2024/11/14: We introduce MMPR, a high-quality, large-scale multimodal reasoning preference dataset, and MPO, an effective preference optimization algorithm. The resulting model, InternVL2-8B-MPO, achieves an accuracy of 67.0 on MathVista. Please refer to our paper, project page and document for more details.

• 2024/10/21: We release the Mini-InternVL series. These models achieve impressive performance with minimal size: the 4B model achieves 90% of the performance with just 5% of the model size. For more details, please check our project page and document.
• 2024/08/01: The Chartmimic team evaluated the InternVL2 series models on their benchmark. The InternVL2-26B and 76B models achieved the top two performances among open-source models, with the InternVL2 76B model surpassing GeminiProVision and exhibiting comparable results to Claude-3-opus.
• 2024/08/01: InternVL2-Pro achieved the SOTA performance among open-source models on the CharXiv dataset, surpassing many closed-source models such as GPT-4V, Gemini 1.5 Flash, and Claude 3 Sonnet.
• 2024/07/24: The MLVU team evaluated InternVL-1.5 on their benchmark. The average performance on the multiple-choice task was 50.4%, while the performance on the generative tasks was 4.02. The performance on the multiple-choice task ranked #1 among all open-source MLLMs.
• 2024/07/04: We release the InternVL2 series. InternVL2-Pro achieved a 62.0% accuracy on the MMMU benchmark, matching the performance of leading closed-source commercial models like GPT-4o.
• 2024/07/18: InternVL2-40B achieved SOTA performance among open-source models on the Video-MME dataset, scoring 61.2 when inputting 16 frames and 64.4 when inputting 32 frames. It significantly outperforms other open-source models and is the closest open-source model to GPT-4o mini.
• 2024/07/18: InternVL2-Pro achieved the SOTA performance on the DocVQA and InfoVQA benchmarks.
• 2024/06/19: We propose Needle In A Multimodal Haystack (MM-NIAH), the first benchmark designed to systematically evaluate the capability of existing MLLMs to comprehend long multimodal documents.
• 2024/05/30: We release ShareGPT-4o, a large-scale dataset that we plan to open-source with 200K images, 10K videos, and 10K audios with detailed descriptions.
• 2024/05/28: Thanks to the lmdeploy team for providing AWQ quantization support. The 4-bit model is available at OpenGVLab/InternVL-Chat-V1-5-AWQ.
• 2024/05/13: InternVL 1.0 can now be used as the text encoder for diffusion models to support multilingual generation natively in over 110 languages worldwide. See MuLan for more details.
• 2024/04/18: InternVL-Chat-V1-5 has been released at HF link, approaching the performance of GPT-4V and Gemini Pro on various benchmarks like MMMU, DocVQA, ChartQA, MathVista, etc.
• 2024/02/27: InternVL is accepted by CVPR 2024 (Oral)!
• 2024/02/21: InternVL-Chat-V1-2-Plus achieved SOTA performance on MathVista (59.9), MMBench (83.8), and MMVP (58.7). See our blog for more details.
• 2024/02/12: InternVL-Chat-V1-2 has been released. It achieves 51.6 on MMMU val and 82.3 on MMBench test. For more details, please refer to our blog and SFT data. The model is now available on HuggingFace, and both training / evaluation data and scripts are open-sourced.
• 2024/01/24: InternVL-Chat-V1-1 is released, it supports Chinese and has stronger OCR capability, see here.
• 2024/01/16: We release our customized mmcv/mmsegmentation/mmdetection code, integrated with DeepSpeed, which can be used for training large-scale detection and segmentation models.

Documents

Get Started

• Installation: Installation Guide | requirements.txt
• Chat Data Format: Meta File | ✏️ Text | ️ [Single-Image](https