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Qwen 2.5 - Alibaba's Large Language Model Series

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qwenlm.github.io·qwenlm.github.io/

Qwen 2.5 is a frontier model series from Alibaba, relevant to AI safety discussions around capability proliferation, open-weight model risks, and the geopolitical landscape of advanced AI development outside Western labs.

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Summary

Qwen 2.5 is Alibaba's latest series of large language models, representing significant capability advances across language understanding, coding, mathematics, and multimodal tasks. The series includes models of various sizes designed for both research and commercial deployment. It represents a major frontier model release from a leading Chinese AI lab.

Key Points

  • Qwen 2.5 is a family of open-weight LLMs from Alibaba Cloud with models ranging from small to very large parameter counts
  • Models demonstrate strong performance on coding, math reasoning, and instruction-following benchmarks
  • Includes specialized variants such as Qwen2.5-Coder and Qwen2.5-Math for domain-specific tasks
  • Released with open weights, enabling broad research access and deployment flexibility
  • Represents growing frontier AI capabilities from non-Western labs, relevant to AI governance and competition dynamics

Cited by 1 page

PageTypeQuality
AI Proliferation Risk ModelAnalysis65.0

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![logo](https://img.alicdn.com/imgextra/i4/O1CN01a6pmNi24dfWQwmMp3_!!6000000007414-2-tps-270-90.png)

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Qwen Code

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Research

Latest Advancements

![Qwen3.5-Max-Preview Now Available on Arena](https://img.alicdn.com/imgextra/i1/O1CN01oNNHZp1u2RCpQ3Kyj_!!6000000005979-2-tps-1590-954.png)

Qwen3.5-Max-Preview Now Available on Arena

We are pleased to announce the deployment of Qwen3.5-Max-Preview on Arena, where it has demonstrated exceptional performance during the preliminary evaluations. As we proceed with final optimizations ahead of the release within the next two weeks, we invite the community to evaluate the model's capabilities via <a href=" [https://arena.ai/](https://arena.ai/)" target="\_blank"...

Release

2026/03/18

![Qwen3.5: Towards Native Multimodal Agents](https://img.alicdn.com/imgextra/i4/O1CN01Ux3jxM1qOBXoBo82c_!!6000000005485-2-tps-1590-954.png)

Qwen3.5: Towards Native Multimodal Agents

We are delighted to announce the official release of Qwen3.5, introducing the open-weight of the first model in the Qwen3.5 series, namely Qwen3.5-397B-A17B. As a native vision-language model, Qwen3.5-397B-A17B demonstrates outstanding results across a full range of benchmark evaluations, including reasoning, coding, agent capabilities, and multimodal understanding, empowering developers and...

Open-Source

2026/02/15

![Qwen-Image-2.0: Professional infographics, exquisite photorealism](https://img.alicdn.com/imgextra/i1/O1CN01IKBGxr1EqAL8Px7tj_!!6000000000402-2-tps-1590-954.png)

Qwen-Image-2.0: Professional infographics, exquisite photorealism

We are launching Qwen-Image-2.0, a next-generation foundational image generation model. The key highlights of Qwen-Image-2.0 include: Professional Typography Rendering: Supports 1k-token instructions for direct generation of professional infographics, including PPTs, posters, comics, and more. Stronger Semantic Adherence: Native 2K resolution support for finely detailed realistic scenes,...

Release

2026/02/10

![Qwen3-Coder-Next: Pushing Small Hybrid Models on Agentic Coding](https://img.alicdn.com/imgextra/i1/O1CN01LwK7vH21HjjuHvHWQ_!!6000000006960-2-tps-1590-954.png)

Qwen3-Coder-Next: Pushing Small Hybrid Models on Agentic Coding

\-\-\- We introduce Qwen3-Coder-Next, an open-weight language model designed specifically for coding agents and local development. Built on top of Qwen3-Next-80B-A3B-Base, which adopts a novel architecture with hybrid attention and MoE, Qwen3-Coder-Next has been agentically trained at scale on large-scale executable task synthesis, environment interaction, and reinforcement learning, obtaining...

Open-Source

2026/02/02

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Release

2026/03/18

Qwen3.5-Max-Preview Now Available on Arena

We are pleased to announce the deployment of Qwen3.5-Max-Preview on Arena, where it has demonstrated exceptional performance during the preliminary evaluations. As we pr

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