nanoGPT

« This Mixture-of-Experts model, with approximately 1 trillion parameters, features a 1M-token context, virtually infinite Engram memory, and multimodal capabilities for text, images, and video. It aims to achieve performance scores comparable to Claude Opus while remaining significantly more affordable (to be released under the Apache 2.0 license) »

« Designed for intensive tasks, this model from Google’s Gemini family outperforms its predecessor in token efficiency while improving performance in coding and knowledge-based tasks. With a 1-million-token context, full multimodality, and better agentic task performance »

« Take advantage of Anthropic’s AI model—more reliable, more honest, and four times less likely to overlook its own errors. Its fast mode delivers up to 2.5 times more tokens per second (at the same API rate as the previous version) »

« A Google AI model capable of handling complex tasks involving text, images, audio, or video. It can summarize lengthy sources, analyze data, and assist in coding end-to-end interfaces »

« Anthropic's most powerful model, capable of handling contexts of 1 million tokens and reasoning adaptively. It coordinates multiple agents in parallel to solve complex tasks. You control the level of effort, intelligence, or cost with 4 available settings »

« Tackle time-consuming and complex tasks by letting AI plan, use tools, check its work, and see tasks through to completion with minimal supervision. This model significantly improves coding, the use of virtual machines, online research, and knowledge work tasks »

« OpenAI's most powerful frontier model directly controls your computer, processes 1 million tokens, and reasons about very long tasks. It is claimed to reduce errors by 33% and significantly improve code and document analysis »

« Anthropic's latest high-end model, optimized for agentic code, long-running tasks, and critical business scenarios. It features self-checking of responses and a context window of one million tokens. It is also capable of automatically adjusting the depth of its reasoning based on the difficulty of the problem »