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 **StreamingBench** evaluates **Multimodal Large Language Models (MLLMs)** in real-time, streaming video understanding tasks. 🌟
 
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+[**NEW!** 2025.05.15] 🔥: [Seed1.5-VL](https://github.com/ByteDance-Seed/Seed1.5-VL) achieved ALL model SOTA with a score of 82.80 on the Proactive Output.
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+[**NEW!** 2025.03.17] ⭐: [ViSpeeker](https://arxiv.org/abs/2503.12769) achieved Open-Source SOTA with a score of 61.60 on the Omni-Source Understanding.
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+[**NEW!** 2025.01.14] 🚀: [MiniCPM-o 2.6](https://github.com/OpenBMB/MiniCPM-o) achieved Streaming SOTA with a score of 66.01 on the Overall benchmark.
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+[**NEW!** 2025.01.06] 🏆: [Dispider](https://github.com/Mark12Ding/Dispider) achieved Streaming SOTA with a score of 53.12 on the Overall benchmark.
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+[**NEW!** 2024.12.09] 🎉: [InternLM-XComposer2.5-OmniLive](https://github.com/InternLM/InternLM-XComposer) achieved 73.79 on Real-Time Visual Understanding.
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 ## 🎞️ Overview
 
 As MLLMs continue to advance, they remain largely focused on offline video comprehension, where all frames are pre-loaded before making queries. However, this is far from the human ability to process and respond to video streams in real-time, capturing the dynamic nature of multimedia content. To bridge this gap, **StreamingBench** introduces the first comprehensive benchmark for streaming video understanding in MLLMs.