Large Language Models

LLMs show high consistency on neutral topics like Thanksgiving but become variable on controversial issues. Larger models outperform smaller ones in reliability.

Users can process high-volume requests at half the cost of regular API calls, with applications in sentiment analysis, translation, and vector embedding.

"New Grok-beta model features 128k token context and function calling, with REST API compatibility with OpenAI/Anthropic. Multi-modal version coming soon."

"The search uses fine-tuned GPT-4 with novel synthetic data generation. Users can trigger web searches automatically or manually, with linked source citations."

"AI systems went from solving 2% of real coding problems to 49% in one year. Anthropic warns the window for safe regulation is closing fast."

OpenAI's SimpleQA tests 4,326 factual questions with 3% error rate. GPT-4o scores under 40%, showing larger models excel while deeper thinking ones opt to decline.