<ul data-eligibleForWebStory="false"><li>Microsoft has introduced Phi-4-mini-Flash-Reasoning, an efficient language model emphasizing long-context reasoning with high inference efficiency, released on Hugging Face.</li><li>The model's architecture, SambaY, combines State Space Models and attention layers using Gated Memory Units, significantly reducing latency in long-context scenarios.</li><li>Phi-4-mini-Flash-Reasoning excels in complex reasoning tasks, outperforming its predecessor and achieving impressive results on benchmarks like Math500 and AIME24/25.</li><li>With a focus on long Chain-of-Thought generation, the model delivers up to 10× higher throughput than its predecessor, showcasing efficient long-context processing and real-time inference capability.</li></ul>

Microsoft Releases Phi-4-mini-Flash-Reasoning: Efficient Long-Context Reasoning with Compact Architecture

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