OpenAI released GPT-6 Astra on September 3, 2026. In the API it costs $10 per million input tokens and $50 per million output tokens for prompts up to 272K input tokens, above which the whole request bills at $20 and $75. Its predecessor GPT-5.6 Sol has been $4 and $20 since a promotional cut on August 21, held at least through November 21. On September 22 OpenAI added two cheaper models to the GPT-6 line, gpt-6-sol at $2 and $10 and gpt-6-luna at $0.10 and $0.50.
Cost per task
On Artificial Analysis’s Intelligence Index v4.3, in a run published on September 9, Astra at max effort scores 53, level with Claude Fable 5.1 at max with fallback and six points above GPT-5.6 Sol. Fallback is Anthropic’s setting that routes safety-flagged requests to another Claude model, and it served about 4% of output tokens across the index. Astra’s tasks come to $3.26 each and Fable 5.1’s to $7.63, which Artificial Analysis describes as matching the score “at ~40% of the cost per task”. Against GPT-5.6 Sol the comparison runs the other way: “At max effort, Astra is ~60% more expensive than GPT-5.6 Sol”, which Artificial Analysis has since priced at $1.99 per Intelligence Index task.
Most of that difference comes from output tokens. At max effort Astra uses 27k output tokens per task where Fable 5.1 at max with fallback uses 78k — about a third, for the same score. The two models list at the same rates, $10 and $50 per million, which leaves the token counts to explain the gap, and Astra gets there even though its cached input costs $1.00 per million against Fable 5.1’s $0.25. Cost per task is not purely an output-token bill, though. Artificial Analysis calculates each evaluation’s cost “from input, cache hit, cache write, reasoning, and answer token prices”, so the input and cache sides of the run are in the figure too.
OpenAI makes a claim of its own in its GPT-6 guide, that Astra delivers “a lower estimated API cost per task than earlier models despite its higher per-token pricing”. In the September 9 run Artificial Analysis put every Astra effort level on its Intelligence Index cost frontier, and Astra still defines the frontier for output tokens per task. Sitting on that frontier meant nothing else in the comparison set reached a given score for less.
In the Coding Agent Index, where Astra runs inside Codex, it scores 62, level with Fable 5.1 in Claude Code and seven points ahead of GPT-5.6 Sol. Per task at max effort it costs $7.09, which Artificial Analysis puts at about 15% more than GPT-5.6 Sol for those seven points, and about 40% less than Fable 5.1 for the same score.
Fewer turns
Not every result in the same report moves in Astra’s favor. On GDPval-AA v2, an adaptation of OpenAI’s dataset covering economically valuable tasks across 44 occupations, Astra comes in about 45 Elo points below GPT-5.6 Sol. Alongside that, Artificial Analysis records how many turns each model took: 24 per task for Astra at max effort, 45 for GPT-5.6 Sol, and 60 each for Fable 5.1 and Claude Opus 5. A turn is roughly one exchange between the model and the harness running it.
Astra took the fewest turns of the four models here, in the same direction as the output-token figures on the Intelligence Index. But on this benchmark the lower count sits next to a lower score. Artificial Analysis reports the turn counts and the Elo drop as two observations from the same runs, and whether one produced the other is not something the published figures settle. The drop is an Elo figure, so it places the models relative to each other rather than against a fixed score, and the evaluation has since moved to v2.1.
Effort levels, and two weeks later
Astra’s costs above are max-effort numbers. Artificial Analysis prices its low effort at $0.82 per task on the same index. Two weeks after that run the picture had moved again: on September 22 Anthropic cut Claude Opus 5.5 to $4 and $20 per million, where it scores 58 on the Intelligence Index, and Artificial Analysis measured OpenAI’s new GPT-6 Sol at $1.06 per Intelligence Index task against GPT-5.6 Sol’s $1.99.
This note is put together from OpenAI’s pricing page and GPT-6 guide and from Artificial Analysis’s published runs, and every figure in it carries a date because at this rate none of them will hold for long. Between the run it leans on and the day this was written, both OpenAI and Anthropic shipped something cheaper than what that run measured. That is not long enough for a price comparison to go stale, or it should not be, but at the moment it seems to be.
