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  • DeepSeek R1 0528 (May 25) vs. MiniMax M1 40k
    Comparison between DeepSeek R1 0528 (May '25) and MiniMax M1 40k across intelligence, price, speed, context window and more
  • New Deepseek R1-0528 Update is INSANE - Analytics Vidhya
    DeepSeek-R1-0528 delivers major leaps in reasoning, code generation, and overall reliability With this release, DeepSeek is positioning itself as a serious open-source challenger to Gemini 2 5 Pro, and in some cases, it even brushes up against the performance of OpenAI’s o3 and o4-mini on coding benchmarks
  • DeepSeek’s Update Brings R1 Model Back to the Top
    While the R1 euphoria later grew thin, the tech startup is back with an updated R1 reasoning model dubbed DeepSeek-R1-0528 The new update is available for commercial use under the MIT license, with DeepSeek claiming it has an improved benchmark performance, better front-end capabilities, and fewer hallucinations than the original R1 model
  • DeepSeek-R1-0528 Release | DeepSeek API Docs
    DeepSeek-R1-0528 Release 🚀 DeepSeek-R1-0528 is here! 🔹 Improved benchmark performance 🔹 Enhanced front-end capabilities 🔹 Reduced hallucinations
  • DeepSeek R1-0528 Released: Open-Source AI Model Rivals GPT-4 . . .
    DeepSeek has set the world buzzing again as it is gaining recognition as a powerful, open-source large language model (LLM) that challenges models from the major tech players In their newest update to DeepSeek, deepseek r1-0528, they have signification improved performance and added new features
  • Run the Full DeepSeek-R1-0528 Model Locally - KDnuggets
    DeepSeek-R1-0528 is the latest update to DeepSeek's R1 reasoning model that requires 715GB of disk space, making it one of the largest open-source models available However, thanks to advanced quantization techniques from Unsloth , the model's size can be reduced to 162GB, an 80% reduction
  • DeepSeek-R1-0528 · GitHub Models · GitHub
    In the latest update, DeepSeek R1 has significantly improved its depth of reasoning and inference capabilities by leveraging increased computational resources and introducing algorithmic optimization mechanisms during post-training





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