GPT-6 Astra vs GPT-5.6 Sol: 2.5x the price, but for what exactly
Start with the fact most comparisons skip: these two models have identical context windows and identical output caps, 1,050,000 and 128,000. Moving from Sol to Astra does not buy you a bigger window. The official pricing page currently lists Astra at $10 per 1M input tokens and $50 per 1M output, against $4 and $20 for Sol, which OpenAI marks as promotional pricing available at least through 2026-11-21. The real dividing line is the reasoning tier and the knowledge cutoff: Astra's reasoning.effort does not support none, and Sol's does. That single difference makes the real cost gap on simple tasks considerably wider than 2.5x.
Updated 2026-09-10
Four official specifications, side by side
Context window: identical
The official model documentation gives both Astra and Sol a 1,050,000 context window and a 128,000 maximum output. So "we need Astra to fit more context" is not a valid reason to switch: both take exactly the same amount of input.
List price multiple
Astra is $10 / $50 and Sol is $4 / $20, exactly 2.5x on both input and output. Note that OpenAI labels Sol's tier as promotional pricing, stated as available at least through 2026-11-21; the standard rate afterwards has not been published, so the multiple itself has an expiry date.
The reasoning tier only Sol has
Per the official docs, Sol's reasoning.effort supports none, low, medium (default), high, xhigh and max; Astra supports low, medium, high, xhigh and max, with no none. For work that needs no reasoning, Sol can skip reasoning tokens entirely and Astra cannot.
Difference in knowledge cutoff
Astra's knowledge cutoff is 2026-04-30 and Sol's is 2026-02-16, a gap of 73 days. If your work depends on facts after February 2026, this is the one Astra advantage that no amount of configuration can substitute for.
Why "no none tier" matters more than the price tag
Reasoning tokens are billed at the output rate and count against the same 128,000 output cap. With reasoning.effort set to none, a simple Sol call produces zero reasoning tokens and you pay for the actual output only. Astra has no such tier and starts at low, so every single call produces reasoning tokens - even when the task is reformatting a paragraph into JSON. On simple work, therefore, the gap in your actual bill is not the 2.5x on the price list; it is 2.5x multiplied by a factor introduced by reasoning tokens, and that factor depends on the shape of your tasks. There is no universal value for it, which is exactly why this page gives no single "Astra costs N times more" number: the honest answer differs enormously between workloads.
How OpenAI positions the two
The official documentation describes Astra as "our most capable model, built for the hardest end-to-end work", recommended for complex reasoning, coding, computer use, research and document creation. Sol is described as "a flagship model in the GPT-5.6 family" that "roughly corresponds to the unsuffixed model tier used in earlier GPT-5 families", and the docs note that the gpt-5.6 alias routes requests to Sol. Read together, the intended split is clear: Astra for hard end-to-end work, Sol as the general-purpose main line. The selection question is therefore not "which is stronger" but "is my work actually hard end-to-end work".
Decision path: three questions
Does the task span multiple steps, call tools on its own, and decide for itself when it is finished? If yes, that is exactly what Astra is positioned for, and it is worth running a comparison on your real workload. If no, go to question 2.
Does the task need reasoning at all? If it does not - rewriting, extraction, formatting, classification - Sol's none tier takes reasoning tokens to zero, which Astra cannot do. This is where the cost gap is widest.
Does the task depend on facts after February 2026? If so, Astra's 73-day-later cutoff is a real advantage. If not, this is not a reason. Three noes means there is no case for switching to Astra.
Specification table
GPT-6 Astra
Released 2026-09-03 · model id gpt-6-astra · context 1,050,000 · max output 128,000 including reasoning tokens · knowledge cutoff 2026-04-30 · reasoning.effort supports low / medium / high / xhigh / max · list price $10 per 1M input and $50 per 1M output · officially positioned as the most capable model, built for the hardest end-to-end work.
GPT-5.6 Sol
Model id gpt-5.6-sol, with the gpt-5.6 alias routing to it · context 1,050,000 · max output 128,000 · knowledge cutoff 2026-02-16 · reasoning.effort supports none / low / medium (default) / high / xhigh / max · the official pricing page currently lists $4 per 1M input and $20 per 1M output, marked as promotional pricing available at least through 2026-11-21 · officially positioned as a flagship model in the GPT-5.6 family.
Two typical cases
Work that belongs on Astra
Long-horizon autonomous coding and refactoring, research-style tasks where the model chooses its own tools and decides when to stop, and anything depending on facts after February 2026. In these cases getting it right in one pass usually saves more than the 2.5x unit-price gap costs - but treat that as a hypothesis to verify against your own workload, not a conclusion to adopt.
Work that should stay on Sol
High-frequency short tasks, format conversion, extraction and classification, and any call where you could set reasoning.effort to none. Switching these to Astra is usually a pure cost increase: the window did not get bigger, but reasoning tokens went from zero to mandatory.
How to measure this so the answer means something
Do not compare price tags on a single request; run a day of your real traffic and compare total bills. The method: send the same batch of requests to both models, record input tokens, output tokens and reasoning tokens separately for each, then multiply by the corresponding rates. Recording reasoning tokens separately is the part that matters - they bill at the output rate, cannot be zero on Astra, and are the main source of the difference. What you get at the end is a multiple that belongs to your workload; it may be well under 2.5 for complex work and well over 2.5 for simple work forced to pay for reasoning. No published multiple substitutes for this measurement.
Running the comparison on QCode
One QCode key calls gpt-6-astra and the three GPT-5.6 tiers; switching is a model id change with no new endpoint and no new auth. That means the comparison above needs one set of credentials and one integration - send the same batch again with a different model id. Billing is broken out per model in the same console, reasoning tokens included, so you do not have to assemble the numbers yourself. One caveat: Astra's migration constraints differ from Sol's (sampling parameters, reasoning tiers, tool-call path), so read the migration guide before running the comparison, or it will fail on parameter errors rather than tell you anything.
Frequently asked questions
Does Astra have a bigger context window than Sol?
No, they are the same. The official model documentation lists a 1,050,000 context window and a 128,000 maximum output for both. If your reason for switching to Astra was the window, that reason does not hold.
Is 2.5x worth it?
It depends on the shape of the work; there is no universal answer. On complex end-to-end tasks, getting it right in one pass can save enough round trips that the total bill comes out lower than Sol. On simple tasks, because Astra has no none reasoning tier, you pay for reasoning tokens on every call and the real gap exceeds 2.5x. The only reliable approach is to total up your own traffic using the method above.
What is the none reasoning tier and why does it matter?
reasoning.effort controls how much reasoning the model does before answering. Setting it to none means no reasoning tokens are produced. Reasoning tokens bill at the output rate and consume the 128,000 output cap, so for work that needs no reasoning, whether you can set none directly determines cost. The official docs show Sol supports none and Astra does not, starting at low.
Will Sol stay at $4/$20?
OpenAI marks it as promotional pricing and states it is available at least through 2026-11-21. The standard rate after that has not been published. So if you are budgeting long term around the 2.5x multiple, note that its denominator has an expiry date and may change after 2026-11-21.
Are gpt-5.6 and gpt-5.6-sol the same thing?
The official documentation states that the gpt-5.6 alias routes requests to GPT-5.6 Sol, so writing gpt-5.6 gets you Sol. The advantage of writing the full id is explicitness: if OpenAI later points the alias at a different tier, your code does not silently follow it.
Can I mix the two?
Yes, and that is usually the cost-optimal answer. Route by task type: short simple calls to Sol with none, complex end-to-end work to Astra. The prerequisite is that the model name in your code is a configurable value rather than a hard-coded string - which is the first step of migration preparation anyway.
Sources
Specifications and reasoning tiers come from the official OpenAI model documentation at developers.openai.com/api/docs/models/gpt-6-astra and /gpt-5.6-sol; prices and Sol's promotional window come from the official pricing page at developers.openai.com/api/docs/pricing, retrieved 2026-09-10. Astra's release date comes from OpenAI's launch post of 2026-09-03. This page cites no third-party benchmarks and publishes no performance multiple it has not measured.
One key, one model id change, and you have the comparison
Astra and the three GPT-5.6 tiers sit in the same account, with billing broken out per model and reasoning tokens visible separately.
Related pages
Complete GPT-6 guide
Astra specification, pricing and practical usage.
GPT-6 Astra migration guide
The four parameter changes needed when moving from GPT-5.6 to Astra.
GPT-5.7 status and the GPT-5.6 tiers
How Luna, Terra and Sol divide the work, with current prices.
Specifications and prices on this page come from OpenAI's official documentation and pricing page, with retrieval dates given under Sources. Sol's $4/$20 is marked by OpenAI as promotional pricing available at least through 2026-11-21; the standard rate afterwards is unpublished. This page gives no unmeasured performance comparison: the cost ratio between these two models on your workload has to be computed from your own traffic.