The article further details various models, including OpenAI's GPT-4 and GPT-3.5 Turbo, Anthropic's Claude 3, Meta's Llama 2, Google's Gemini and PaLM 2, and Mistral's models. Each model has its strengths, weaknesses, and price points, with some optimized for complex tasks, others for chatbots and conversational interfaces, and some for multilingual, reasoning, and coding tasks. The models also vary in their context length, speed, and cost, offering a range of options for different needs and budgets.
Key takeaways:
- AI Large Language Models (LLMs) pricing usually revolves around tokens, with 1,000 tokens roughly equivalent to about 750 words.
- Context length, or the model's short-term memory, affects the model's performance and cost. Longer context lengths allow for more complex tasks but are generally pricier.
- Different AI models offer different strengths and price points. For example, OpenAI's GPT-4 is great at complex tasks but is pricier, while Anthropic's Claude 3 has an impressive context length but is slower and more expensive.
- There are several options for different needs and budgets, including OpenAI's GPT-3.5 Turbo for chatbots, Meta's Llama 2 for English text summarization, Google's Gemini for performance, and Mistral's affordable and fast models.