Large Language Models

Microsoft's BitNet b1.58 2B4T is a 2-billion-parameter 1-Bit LLM natively trained with 1.58-bit ternary weights {-1, 0, +1}, matching full-precision models in benchmarks like MMLU (78.5%) and GSM8K (45.7%) while using just 0.4 GB memory and 15 ms/token latency on CPUs. It outperforms 4-bit quantized alternatives in energy (0.5 J/1K tokens) and speed, enabling on-device AI for mobiles, IoT, and wearables. Key features include absmean quantization, BitLinear layers, and bitnet.cpp for seamless inference. This breakthrough supports edge AI without GPUs, reducing costs and carbon footprint for developers and enterprises.

OpenAI's GPT-5 advances AI with 95% accuracy in complex reasoning, handling math, logic, and multi-step problems like geometry proofs. It processes text, images, audio, and video seamlessly—for instance, analyzing a circuit board photo with audio to pinpoint faults. Benchmarks show 94.2% on MMLU, 89.5% on HumanEval, and 78.3% on MATH, surpassing GPT-4 by up to 25%. Technical tweaks reduce hallucinations by 80%, enabling applications in healthcare diagnostics, legal analysis, and software development. Available via API in Q2 2026, with tiers starting at $0.10 per million input tokens, plus strong ethical filters for safe deployment.