Qwen-2.5 Max : This NEW LLM BEATS DEEPSEEK-V3 & R1? (Fully Tested)
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In this video, I'll be telling you about Qwen 2.5 Max that claims to beat Deepseek V3 & R1, but does it really?.. Today, I'll test it and we'll see if it can really beat the Deepseek V3 & R1 Models.
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Key Takeaways:
🚀 Qwen 2.5 Max, the latest language model from Qwen, enters the arena with bold claims of matching Deepseek V3 performance, a large MoE marvel pre-trained on extensive datasets with sophisticated SFT and RLHF.
📊 Benchmarks suggest that the new qwen model outperforms Deepseek V3 in specific tasks like Arena Hard and LiveBench, showcasing a highly competitive edge against top-tier models, however, the raw power and model size might tell a different story.
🔒 Unlike the open nature of some competitors, Qwen 2.5 Max is primarily accessible through their API or chat interface, a notable limitation for users seeking open source options for their projects related to AI models or Large Language Models.
🤖 While the free chat platform provides a convenient way to test the model’s capabilities, relying solely on an API can be a major drawback for many developers who might be looking for open weights models with more control and flexibility in their workflows.
🤔 The code generation capabilities, based on initial testing, seem somewhat subpar compared to the performance benchmarks achieved by Deepseek V3 models, indicating that code completion capabilities need more work.
🏆 The model shows some promise in creative tasks and reasoning problems, even if it's not as good as deepseek, with some impressive SVG code generation and some math problems getting solved by the model which shows potential in language models and reasoning task.
💭 Despite the competitive claims, Qwen 2.5 Max, might not be a real deepseek killer yet based on current assessment, and its closed-source nature might push users to consider options like Gemini Flash or other open-source alternatives for their projects around Large Language models.
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Timestamps:
00:00 - Introduction
01:41 - NinjaChat (Sponsor)
02:48 - Testing
07:33 - Final Charts & Thoughts
