GPT-6 Astra vs GPT-6.1 Sol

OpenAI's own framing of this pair is that GPT-6.1 Sol delivers near-Astra performance at a lower cost. The price half of that claim is checkable from the table above; the performance half isn't, from published figures, so this page states the cost gap precisely and leaves quality to a test on your own tasks.

What they share

The same context window, the same maximum output, the same knowledge cutoff, the same effort levels from low to max, and the same request and token limits per minute at every usage tier. Neither accepts none effort, and both require the Responses API for tool calling. In capacity terms there is nothing to choose between them.

The price gap

Astra costs more on every cell: input, cached input, cache writes and output, in both context bands and at every tier both offer. The cached-input gap is the widest, because GPT-6.1 Sol also has the deepest cache-read discount of any current model. OpenAI's counterargument is that Astra often finishes a task with substantially fewer output tokens, so its cost per task can come out lower than its per-token rates suggest. That's an empirical claim about your workload, not something a price table can settle — measure turns and tokens per finished task on both.

What only Astra has

Ultrafast. Astra is the only model with an Ultrafast row, with its own token limits; GPT-6.1 Sol's Ultrafast support is described as coming later. Astra also publishes no default effort level, where GPT-6.1 Sol defaults to medium. And OpenAI documents behavior differences for Astra — more willing to stop and ask a clarifying question, and more sensitive to instructions in AGENTS.md and skill files.

How to decide

Start on GPT-6.1 Sol — it's the CLI default and the cheaper baseline — and move a task to Astra when it's demonstrably too hard for Sol, or when Ultrafast's speed is worth its price.