Reka Flash 3
Reka Flash 3 is a 21-billion-parameter multimodal AI model developed by Reka AI, designed to excel in general chat, coding, instruction following, and function calling. It processes and reasons with text, images, video, and audio inputs, offering a compact, general-purpose solution for various applications. Trained from scratch on diverse datasets, including publicly accessible and synthetic data, Reka Flash 3 underwent instruction tuning on curated, high-quality data to optimize performance. The final training stage involved reinforcement learning using REINFORCE Leave One-Out (RLOO) with both model-based and rule-based rewards, enhancing its reasoning capabilities. With a context length of 32,000 tokens, Reka Flash 3 performs competitively with proprietary models like OpenAI's o1-mini, making it suitable for low-latency or on-device deployments. The model's full precision requires 39GB (fp16), but it can be compressed to as small as 11GB using 4-bit quantization.
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OpenAI o3-mini
OpenAI o3-mini is a lightweight version of the advanced o3 AI model, offering powerful reasoning capabilities in a more efficient and accessible package. Designed to break down complex instructions into smaller, manageable steps, o3-mini excels in coding tasks, competitive programming, and problem-solving in mathematics and science. This compact model provides the same high-level precision and logic as its larger counterpart but with reduced computational requirements, making it ideal for use in resource-constrained environments. With built-in deliberative alignment, o3-mini ensures safe, ethical, and context-aware decision-making, making it a versatile tool for developers, researchers, and businesses seeking a balance between performance and efficiency.
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GPT-4.1 mini
GPT-4.1 mini is a compact version of OpenAI’s powerful GPT-4.1 model, designed to provide high performance while significantly reducing latency and cost. With a smaller size and optimized architecture, GPT-4.1 mini still delivers impressive results in tasks such as coding, instruction following, and long-context processing. It supports up to 1 million tokens of context, making it an efficient solution for applications that require fast responses without sacrificing accuracy or depth.
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Phi-4-mini-reasoning
Phi-4-mini-reasoning is a 3.8-billion parameter transformer-based language model optimized for mathematical reasoning and step-by-step problem solving in environments with constrained computing or latency. Fine-tuned with synthetic data generated by the DeepSeek-R1 model, it balances efficiency with advanced reasoning ability. Trained on over one million diverse math problems spanning multiple levels of difficulty from middle school to Ph.D. level, Phi-4-mini-reasoning outperforms its base model on long sentence generation across various evaluations and surpasses larger models like OpenThinker-7B, Llama-3.2-3B-instruct, and DeepSeek-R1. It features a 128K-token context window and supports function calling, enabling integration with external tools and APIs. Phi-4-mini-reasoning can be quantized using Microsoft Olive or Apple MLX Framework for deployment on edge devices such as IoT, laptops, and mobile devices.
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