Alibaba Qwen Challenges OpenAI and DeepSeek with Multimodal AI Automation and 1M-Token Context Models

With the release of Qwen2.5, Alibaba has introduced AI models capable of analyzing images, videos, and documents, while supporting context inputs up to one million tokens.

Alibaba has released its latest Qwen2.5 series, a family of advanced AI models designed to tackle complex multimodal tasks and process long-context inputs up to one million tokens.

These models, which include both instruction-tuned and base variants, aim to rival offerings from OpenAI, Anthropic, and DeepSeek.

With capabilities in video understanding, document parsing, and tool integration, the Qwen2.5 series reinforces Alibaba’s competitive presence in the global AI ecosystem.

The flagship model, Qwen2.5-VL-72B-Instruct, has been made available through Alibaba’s Qwen Chat platform, with additional versions hosted on Hugging Face and Alibaba ModelScope.

According to Alibaba, the models offer unprecedented versatility in analyzing diverse data formats, including scanned documents, video frames, and intricate charts, while also serving as interactive agents capable of executing tasks on mobile and desktop platforms.

Related: Meta Employees Say Their AI Team Is in “Panic Mode” After DeepSeek R1 Model Release

Redefining Multimodal Performance with Qwen2.5-VL

One of the standout features of the Qwen2.5-VL models is their ability to comprehend and process complex multimodal inputs.

Unlike conventional AI systems that specialize in either textual or visual data, Qwen2.5-VL seamlessly integrates both modalities. The models excel at analyzing structured and unstructured data, such as extracting key details from invoices, identifying objects in images, and summarizing events in videos longer than an hour.

Example of Precise Object Grounding with Qwen2.5-VL (Source: Alibaba)

The Qwen2.5-VL series uses a streamlined visual encoder architecture, enhanced with Window Attention mechanisms and a native dynamic resolution Vision Transformer (ViT).

These upgrades improve both training and inference efficiency, enabling faster and more accurate performance. A notable demonstration involved Qwen2.5-VL booking a flight on a mobile app without explicit task-specific fine-tuning. This practical example, shared by Philipp Schmid, CTO of Hugging Face, shows the model’s potential as a robust visual agent.

According to Alibaba’s Qwen team, “These models are not just about recognition; they actively engage with tools, making them capable of performing complex tasks across devices.”

Benchmarks confirm the superiority of Qwen2.5-VL in domains like document understanding, general visual question answering, and video comprehension, where it has outperformed OpenAI’s GPT-4o and Google’s Gemini 2.0 Flash.

Source: Alibaba Qwen

Long-Context Mastery with Qwen2.5-1M

In addition to its multimodal advancements, Alibaba has introduced Qwen2.5-1M, a model family specifically designed to process long-context inputs of up to one million tokens.

Traditional large language models often struggle to maintain coherence when dealing with extended sequences, but Qwen2.5-1M addresses this challenge with an innovative Dual Chunk Attention (DCA) mechanism.

By remapping positional distances within transformer architectures, DCA prevents accuracy degradation during long-context inference. The Qwen team explains:

“We have extended the model’s context length from 128k to 1M, which is approximately 1 million English words or 1.5 million Chinese characters, equivalent to 10 full-length novels, 150 hours of speech transcripts, or 30,000 lines of code. The model achieves 100% accuracy in the 1M length Passkey Retrieval task and scores 93.1 on the long text evaluation benchmark RULER, surpassing GPT-4’s 91.6 and GLM4-9B-1M’s 89.9. Additionally, the model maintains very strong competitiveness in short sequence capabilities, on par with GPT-4o-mini.”

Source: Alibaba Qwen

The long-context capabilities of Qwen2.5-1M have been tested against industry benchmarks, including RULER, LV-Eval, and Longbench-Chat.

Results show that Qwen2.5-1M consistently outperforms its competitors, making it a great tool for applications like summarizing legal documents, analyzing scientific datasets, and generating detailed reports.

Source: Alibaba Qwen

Alibaba has also released an optimized inference framework on GitHub, which accelerates processing speeds by up to seven times compared to traditional methods, ensuring cost-effective deployment.

Competing in a Crowded Market

Alibaba’s Qwen2.5 models enter a highly competitive AI landscape dominated by OpenAI, Anthropic, and emerging players like DeepSeek. OpenAI recently launched its Operator AI agent, which interacts with graphical user interfaces (GUIs) to automate tasks such as form-filling and reservation booking.

Meanwhile, DeepSeek’s Janus Pro models have set new benchmarks for multimodal AI, utilizing rectified flow and decoupled encoder architectures for enhanced performance.

Related: How DeepSeek R1 Surpasses ChatGPT o1 Under Sanctions, Redefining AI Efficiency Using Only 2,048 GPUs

Despite these strong competitors, Qwen2.5 holds its ground with its unique combination of multimodal capabilities and scalable long-context processing. Its open-source licensing for smaller models, such as Qwen2.5-VL-7B, further broadens its appeal among developers seeking accessible AI solutions.

Navigating Regulatory and Ethical Challenges

As a product of a Chinese company, Qwen2.5 models just like DeepSeek’s models face limitations in terms of regulatory compliance. For example, the models restrict responses to politically sensitive topics, such as questions about “Xi Jinping’s mistakes,” which return error messages when queried.

This reflects broader regulatory oversight in China’s AI sector, aimed at aligning outputs with government-mandated guidelines.

Despite these constraints, the Qwen2.5 series demonstrates Alibaba’s commitment to advancing AI technology somewhat responsibly. Its modular approach to licensing and deployment ensures that developers worldwide can adapt the models to meet diverse requirements while maintaining some ethical safeguards.

A Shift Toward Efficiency-First AI Development

The release of Qwen2.5 highlights a growing trend toward efficiency-focused AI development. While companies like Meta invest heavily in large-scale infrastructure—Meta recently announced plans to deploy over 1.3 million GPUs in 2025—Alibaba and DeepSeek are proving that innovative engineering can achieve competitive results at a fraction of the cost.

DeepSeek’s R1 model, for instance, was trained on restricted Nvidia H800 GPUs and delivered performance metrics comparable to OpenAI’s o1 models, demonstrating the viability of resource-efficient strategies.

By prioritizing scalability and cost-effectiveness, Alibaba, just like DeepSeek, is positioning itself as a formidable player in the AI market.

Markus Kasanmascheff
Markus Kasanmascheff
Markus has been covering the tech industry for more than 15 years. He is holding a Master´s degree in International Economics and is the founder and managing editor of Winbuzzer.com.

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