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InternVL Family: Closing the Gap to Commercial Multimodal Models with Open-Source Suites —— A Pioneering Open-Source Alternative to GPT-5

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[📜 Mini-InternVL Paper] [📜 InternVL2 Blog] [📜 InternVL 1.5 Paper] [📜 InternVL 1.0 Paper]

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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.

<details> <summary>More News</summary>

• 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 t