GPT-5: Key characteristics, pricing and model card

Summary

GPT-5: Key characteristics, pricing and model card 7th August 2025 I’ve had preview access to the new GPT-5 model family for the past two weeks, and have been using GPT-5 as my daily-driver. It’s my new favorite model. It’s still an LLM—it’s not a dramatic departure from what we’ve had before—but it rarely screws up and generally feels competent or occasionally impressive at the kinds of things I like to use models for. I’ve collected a lot of notes over the past two weeks, so I’ve decided to break them up into a series of posts. This first one will cover key characteristics of the models, how they are priced and what we can learn from the GPT-5 system card. Key model characteristics Let’s start with the fundamentals. GPT-5 in ChatGPT is a weird hybrid that switches between different models. Here’s what the system card says about that (my highlights in bold): GPT-5 is a unified system with a smart and fast model that answers most questions, a deeper reasoning model for harder problems, and a real-time router that quickly decides which model to use based on conversation type, complexity, tool needs, and explicit intent (for example, if you say “think hard about this” in the prompt). [...] Once usage limits are reached, a mini version of each model handles remaining queries. In the near future, we plan to integrate these capabilities into a single model. GPT-5 in the API is simpler: it’s available as three models—regular, mini and nano—which can each be run at one of four reasoning levels: minimal (a new level not previously available for other OpenAI reasoning models), low, medium or high. The models have an input limit of 272,000 tokens and an output limit (which includes invisible reasoning tokens) of 128,000 tokens. They support text and image for input, text only for output. I’ve mainly explored full GPT-5. My verdict: it’s just good at stuff. It doesn’t feel like a dramatic leap ahead from other LLMs but it exudes competence—it rarely messes up, and frequently i...