{"id":3063,"date":"2025-02-03T15:24:26","date_gmt":"2025-02-03T15:24:26","guid":{"rendered":"https:\/\/fusionmindlabs.com\/blogs\/qwen-2-5-beats-deepseek-v3-in-some-benchmarks-2\/"},"modified":"2025-02-03T15:24:26","modified_gmt":"2025-02-03T15:24:26","slug":"qwen-2-5-beats-deepseek-v3-in-some-benchmarks-2","status":"publish","type":"post","link":"https:\/\/fusionmindlabs.com\/blogs\/qwen-2-5-beats-deepseek-v3-in-some-benchmarks-2\/","title":{"rendered":"Qwen 2.5 Beats DeepSeek V3 in Some Benchmarks"},"content":{"rendered":"\n<\/p>\n
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Alibaba\u2019s response to DeepSeek is Qwen 2.5-Max, the company\u2019s latest Mixture-of-Experts (MoE) large-scale model.<\/p>\n

Qwen 2.5-Max boasts pretraining on over 20 trillion tokens and fine-tuning through cutting-edge techniques like Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF).<\/p>\n

With the API now available through Alibaba Cloud<\/a> and the model accessible for exploration via Qwen Chat, the Chinese tech giant is inviting developers and researchers to see its breakthroughs firsthand.<\/p>\n

Outperforming peers <\/h3>\n

When comparing Qwen 2.5-Max\u2019s performance against some of the most prominent AI models on a variety of benchmarks, the results are promising.<\/p>\n

Evaluations included popular metrics like the MMLU-Pro for college-level problem-solving, LiveCodeBench for coding expertise, LiveBench for overall capabilities, and Arena-Hard for assessing models against human preferences.<\/p>\n

According to Alibaba, \u201cQwen 2.5-Max outperforms DeepSeek V3 in benchmarks such as Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, while also demonstrating competitive results in other assessments, including MMLU-Pro.\u201d<\/p>\n

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