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Alibaba's Qwen-powered AI agent leads global benchmark

2025-07-04•Mudit Dube•Original Link•1 minutes
ArtificialIntelligence
Alibaba
MachineLearning

Alibaba's open-source Qwen AI model drives DeepSWE to top global performance rankings, outperforming rival systems

DeepSWE is based on Alibaba Cloud's third-generation Qwen3-32B model

July 4, 2025 | 3:35 pm

An AI agent built on Alibaba Group's open-source Qwen artificial intelligence model has achieved global leadership in software engineering benchmarks, according to developers Agentica and Together AI.

Key Achievement

  • DeepSWE framework scored 59% accuracy in the latest SWEBench-Verified test
  • System built on Alibaba Cloud's Qwen3-32B large language model (LLM)
  • Outperformed competing AI agent frameworks

"The success demonstrates Alibaba's growing influence in the global open-source AI community," industry observers noted.

Technical Breakthrough

The agentic framework represents a significant advancement in:

  • Autonomous task execution
  • Complex problem-solving
  • Software engineering applications

Developers emphasized the benefits of Alibaba's open-source approach, which allows for:

  1. Community-driven improvements
  2. Faster debugging
  3. Customization for specialized use cases

What Makes Agentic Frameworks Special?

These advanced platforms enable AI systems to:

  • Self-organize workflow tasks
  • Dynamically allocate computational resources
  • Solve multi-step problems without human intervention

Learn more about Alibaba Group

The benchmark results position Alibaba as a major contender in the race to develop practical, high-performance AI solutions for enterprise applications.

Related News

2025-07-04•by Maria Deutscher

Wonderful secures $34M for AI-powered multilingual customer support

Customer support automation startup Wonderful raises $34M in seed funding to expand its AI platform for non-English markets.

ArtificialIntelligence
CustomerSupport
StartupFunding
2025-07-04•Ben Dickson

Sakana AI's TreeQuest Boosts LLM Performance by 30% with Multi-Model Collaboration

Sakana AI's new inference-time scaling technique, TreeQuest, uses Monte-Carlo Tree Search to orchestrate multiple LLMs for superior task performance.

AI
MachineLearning
LLMs

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