GPT-6 Astra has been the successor to GPT-5.6 Sol since the beginning of September, and it costs around two and a half times as much. So the question is not whether Astra is better, but where the difference is big enough to justify the surcharge. We compared the official figures with the other frontier models on ChatX and drew up a few rules for when the switch pays off.
Astra next to Sol, Opus 5.5 and Fable 5.1
| GPT-6 Astra | GPT-5.6 Sol | Claude Opus 5.5 | Claude Fable 5.1 | |
|---|---|---|---|---|
| Price per token at the provider (Sol = 1) | ≈ 2.5× | 1× | 1× | ≈ 2.5× |
| Depth of thought | selectable, default “Low” | selectable, default “Off” | selectable from “Low” up, no “Off” | selectable from “Low” up, no “Off” |
| Web search / images | yes / yes | yes / yes | yes / yes | no / yes |
| Strength | mathematics, long multi-step tasks, reliability | everyday work and demanding texts | programming, analysis | the hardest individual cases |
The price row is the ratio of the official provider prices for input and output. ChatX passes those differences on, so for answers of the same length the ratio also applies to your token deduction. Astra and Fable 5.1 are on one level, Sol and Opus 5.5 together clearly below them.
Where Astra is measurably ahead
OpenAI names three results that describe the gap to Sol: 98 percent on FrontierMath Tier 4 (research mathematics), 99.9 percent on ARC-AGI-3 (abstract problem solving) and the top spot on Terminal-Bench 4.0, a test for programming tasks that run across many steps. Sol was clearly behind on each of them.
More important for users than the record numbers are two points from the system card:
- Fewer invented facts: in the reported error cases, Astra hallucinates less often than Sol. For research, summaries of technical texts and work with numbers, that is the most noticeable difference.
- More robust against injected instructions (prompt injections): if you put web pages, PDFs or emails into the chat, you less often get answers steered by hidden commands in the material. In coding simulations, OpenAI counts about half as many critical misbehaviours as with Sol.
Independent testers do report, however, that on very long tasks Astra introduces new errors that take time to track down. Checking the result stays mandatory, precisely because the model writes with such confidence.
When Astra is worth the surcharge
A few simple rules follow from the comparison on ChatX:
- Long documents: for contracts, studies or whole code bases running to several hundred thousand tokens, Astra keeps everything in one request with around a million tokens of context. Sol cuts off here.
- Numbers and logic: financial models, statistics, proofs and anything where a calculation error gets expensive. The FrontierMath gap shows up here in practice.
- Not for short tasks: emails, translations, summaries and factual questions are answered just as well by Sol, and often well enough by Luna and Terra, at a fraction of the cost.
- Choose the thinking depth deliberately: Astra starts on “Low”. “High” only pays off when the task has several possible solutions. On simple questions it costs waiting time and tokens without a better result.
- Use the answer length: on ChatX an answer may run up to 15,000 tokens. If you want a complete write-up, say so in the prompt (“complete, do not shorten”), otherwise Astra likes to summarise, just as Sol does.
A practical test: give the same task to Sol once and to Astra once, then read the answers side by side. If the difference is not obvious at first glance, Sol was the right choice.