https://the-decoder.com/qwen3-vl-can-scan-two-hour-videos-and-pinpoint-nearly-every-detail/ Skip to content Ad THE DECODER Artificial Intelligence: News, Business, Research DE AI research Nov 28, 2025Nov 28, 2025 # Jonathan Kemper Qwen3-VL can scan two-hour videos and pinpoint nearly every detail Alibaba Qwen3-VL can scan two-hour videos and pinpoint nearly every detail [svg] Jonathan Kemper Jonathan writes for THE DECODER about how AI tools can improve both work and creative projects. Profile E-Mail Content summary Summary A few months after launching Qwen3-VL, Alibaba has released a detailed technical report on the open multimodal model. The data shows the system excels at image-based math tasks and can analyze hours of video footage. Ad The system handles massive data loads, processing two-hour videos or hundreds of document pages within a 256,000-token context window. In "needle-in-a-haystack" tests, the flagship 235-billion-parameter model located individual frames in 30-minute videos with 100 percent accuracy. Even in two-hour videos containing roughly one million tokens, accuracy held at 99.5 percent. The test works by inserting a semantically important "needle" frame at random positions in long videos, which the system must then find and analyze. Heatmap mit Video-Langen auf der y-Achse und Frame-Positionen auf der x-Achse. Die meisten Zellen zeigen hohe Genauigkeitswerte in Prozent, mit perfekten Ergebnissen bei kurzeren Videos.The needle-in-a-haystack test measures the model's ability to locate specific frames in long videos. | Image: Alibaba Share Recommend our article Share In published benchmarks, the Qwen3-VL-235B-A22B model often beats Gemini 2.5 Pro, OpenAI GPT-5, and Claude Opus 4.1 - even when competitors use reasoning features or high thinking budgets. The model dominates visual math tasks, scoring 85.8 percent on MathVista compared to GPT-5's 81.3 percent. On MathVision, it leads with 74.6 percent, ahead of Gemini 2.5 Pro (73.3 percent) and GPT-5 (65.8 percent). Ad Ad THE DECODER Newsletter The most important AI news straight to your inbox. Weekly Free Cancel at any time Please leave this field empty[ ] [ ] [Subscribe] Check your inbox or spam folder to confirm your subscription. Tabelle mit Benchmark-Ergebnissen von Qwen3-VL-235B, Gemini 2.5 Pro, OpenAI GPT-5 und Claude Opus 4.1Gemini's older 2.5 Pro model maintains a slight lead in general image understanding. | Image: Alibaba The model also shows range in specialized benchmarks. It scored 96.5 percent on the DocVQA document comprehension test and 875 points on OCRBench, supporting 39 languages - nearly four times as many as its predecessor. Balkendiagramm der OCR-Genauigkeit von Qwen3-VL fur 39 Sprachen, wobei die meisten Balken uber der 70-Prozent-Marke liegen.Qwen3-VL achieves over 70 percent accuracy on OCR tasks in 32 of the 39 supported languages. | Image: Alibaba Alibaba claims the system demonstrates new capabilities in GUI agent tasks. It achieved 61.8 percent accuracy on ScreenSpot Pro, which tests navigation in graphical user interfaces. On AndroidWorld, where the system must independently operate Android apps, Qwen3-VL-32B hit 63.7 percent. The model handles complex, multi-page PDF documents as well. It scored 56.2 percent on MMLongBench-Doc for long document analysis. On the CharXiv benchmark for scientific charts, it reached 90.5 percent on description tasks and 66.2 percent on complex reasoning questions. It is not a clean sweep, however. In the complex MMMU-Pro test, Qwen3-VL scored 69.3 percent, trailing GPT-5's 78.4 percent. Commercial competitors also generally lead in video QA benchmarks. The data suggests Qwen3-VL is a specialist in visual math and documents, but still lags in general reasoning. Key technical advances for multimodal AI The technical report outlines three main architectural upgrades. First, "interleaved MRoPE" replaces the previous position embedding method. Instead of grouping mathematical representations by dimension (time, horizontal, vertical), the new approach distributes them evenly across all available mathematical areas. This change aims to boost performance on long videos. Recommendation AI research DeepSeek's latest R1 model matches OpenAI's o1 in reasoning benchmarks DeepSeek's latest R1 model matches OpenAI's o1 in reasoning benchmarks DeepSeek's latest R1 model matches OpenAI's o1 in reasoning benchmarks Schematische Darstellung der Qwen3-VL-Architektur mit Vision Encoder links und Large Language Model rechts, verbunden durch Datenflusse und DeepStack-Verbindungen.Qwen3-VL combines a vision encoder and language model to process text, images, and videos simultaneously. DeepStack uses visual information from different processing levels. | Image: Alibaba Second, DeepStack technology allows the model to access intermediate results from the vision encoder, not just the final output. This gives the system access to visual information at different levels of detail. Third, a text-based timestamp system replaces the complex T-RoPE method found in Qwen2.5-VL. Instead of assigning a mathematical time position to every video frame, the system now inserts simple text markers like "<3.8 seconds>" directly into the input. This simplifies the process and improves the model's grasp of time-based video tasks. Training at scale with one trillion tokens Alibaba trained the model in four phases on up to 10,000 GPUs. After learning to link images and text, the system underwent full multimodal training on about one trillion tokens. Data sources included web scrapes, 3 million PDFs from Common Crawl, and over 60 million STEM tasks. In later phases, the team gradually expanded the context window from 8,000 to 32,000 and finally to 262,000 tokens. The "Thinking" variants received specific chain-of-thought training, allowing them to explicitly map out reasoning steps for better results on complex problems. Ad Ad Join our community Join the DECODER community on Discord, Reddit or Twitter - we can't wait to meet you. Open weights under Apache 2.0 All Qwen3-VL models released since September are available under the Apache 2.0 license with open weights on Hugging Face. The lineup includes dense variants ranging from 2B to 32B parameters, as well as mixture-of-experts models: the 30B-A3B and the massive 235B-A22B. While features like extracting frames from long videos aren't new - Google's Gemini 1.5 Pro handled this in early 2024 - Qwen3-VL offers competitive performance in an open package. With the previous Qwen2.5-VL already common in research, the new model is likely to drive further open-source development. Share Support our independent, free-access reporting. Any contribution helps and secures our future. Support now: Bank transfer Summary * Alibaba's Qwen3-VL, launched in September, outperforms GPT-5 and Gemini 2.5 Pro on benchmarks that require solving math questions using images, analyzing videos, and understanding documents. * The latest technical report highlights that Qwen3-VL can process very long videos and large amounts of text at the same time, accurately identify video frames, and recognize text in 39 languages. * The model was trained on a trillion text and image samples using 10,000 GPUs, is openly available. Sources Arxiv [svg] Jonathan Kemper Jonathan writes for THE DECODER about how AI tools can improve both work and creative projects. Profile E-Mail Share AI in practice Oct 4, 2025Oct 4, 2025 Alibaba releases Qwen3 compact open source multimodal models News, tests and reports about VR, AR and MIXED Reality. What happens next with MIXED My personal farewell to MIXED Meta and Anduril are now jointly developing XR headsets for the US military MIXED-NEWS.com AI in practice Sep 23, 2025Sep 23, 2025 Alibaba's Qwen introduces new models for voice, image editing and safety Alibaba's Qwen introduces new models for voice, image editing and safety Alibaba's Qwen introduces new models for voice, image editing and safety Load more Google News Join our community Join the DECODER community on Discord, Reddit or Twitter - we can't wait to meet you. To top * The Decoder + About + Advertise + Publish with us * Topics + AI research + AI in practice + AI and society THE DECODER Newsletter The most important AI news straight to your inbox. Weekly Free Cancel at any time Please leave this field empty[ ] [ ] [Subscribe] Check your inbox or spam folder to confirm your subscription. 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