The short version
I would use GPT-5.6 Sol as the default for routine production traffic. I would test GPT-6 Astra when the real problem is execution: browser or desktop control, terminal work, long autonomous coding tasks, scientific tooling, or workflows where retries and human correction are expensive.
Both models support a 1.05-million-token context window, 128K maximum output, text and image input, reasoning, computer use, structured outputs, function calling, and modern tool-based API workflows. The important difference is not context capacity. It is how reliably each model turns that context into completed work.
Astra costs more per token, but the right comparison is:
Cost per accepted task = total cost of producing successful work, not simply token price.
