GPT-6.1 Terra or GPT-6 Astra — Which Is Better?

Quality: GPT-6.1 Terra vs. GPT-6 Astra

At the time of publication, GPT-6.1 Terra scores 9.0 out of 10 and ranks 5 in QueryWise. GPT-6 Astra scores 9.8 out of 10 and holds 2 place. By the current score, GPT-6 Astra is ahead with a result of GPT-6 Astra.

The difference between the models is most noticeable in tasks that require a long chain of reasoning or careful work with a large amount of source material. Terra looks like a sensible choice for everyday questions, rewriting text, and quick explanations. Astra is designed for heavier requests: it performs best when an answer needs to pass through several stages of verification.

QueryWise updates its rankings every hour, so the final values may change. For this pair, the overall score is not the only factor: Astra already has a coding score of 9.5, while no separate coding metric is currently published for Terra.

Price and speed in tenge

An average question to GPT-6.1 Terra costs approximately 10 ₸, while an average question to GPT-6 Astra costs about 43 ₸. In other words, Astra is noticeably more expensive per request. For a one-off complex task, that may be a reasonable price for a stronger current result; for ongoing conversations, the difference quickly adds up.

Consider a typical workday: you need to process dozens of short emails, translate wording into Kazakh, and bring all the replies into a consistent tone. Terra looks more practical here if each request is simple and there are many of them. Paying extra for Astra in such a queue is not always justified.

Speed cannot honestly be called an advantage of either model yet: QueryWise has not collected enough median measurements. Telemetry reports — for Terra and — for Astra. Reliability is similar: there is not yet a sufficient body of observations, so claiming that one model responds more consistently than the other would be speculation.

Context and task types

GPT-6 Astra has a context window of 1.1M. That provides substantial capacity for long documents, large conversations, and projects involving multiple files. GPT-6.1 Terra’s context window is currently listed as 1.1M, so an exact quantitative comparison is not possible here.

Here is a straightforward use case for Astra: upload a 200-page contract and ask it to find contradictions between sections, then create a risk table citing the specific clauses involved. For this kind of work, a long context is more valuable than a low price for a short question. The model can keep more source material in a single conversation instead of forcing the user to split the document into many parts.

Terra makes more sense for a different request: “Turn this email to a supplier in Almaty into a polite reply in Russian and Kazakh, while preserving the delivery dates and amount.” If the source is short and the task is clear, the more expensive Astra will not necessarily produce a proportionally better result.

Coding clearly tips the scales toward Astra. Its QueryWise score is 9.5. For example, you can ask it to fix a bug in a Python function, explain the cause of the failure, and write several tests for edge cases. Astra is stronger when the task requires changing code, justifying the solution, and preserving interface details at the same time.

We ran both models through this practical question: “Compare two excerpts from a lease agreement, find discrepancies in the notice periods, and indicate which clause needs clarification.” Terra is useful for a preliminary analysis of this kind, but it is better suited to shorter documents. Astra is more confident at keeping several conditions in one answer, especially when the excerpts are long and similarly worded. This is not legal advice: the agreement should still be reviewed by a professional.

Which model should you choose in QueryWise?

For coding, the winner is clear: GPT-6 Astra, thanks to its 9.5 score and large context. Choose it when you need to analyze a project, keep track of many files, or get a reasoned fix rather than simply generate a small script.

Astra is also ahead for long documents and complex analysis. It costs more, but the current ranking places it above Terra: 9.8 versus 9.0. If the question is important and a second review would cost more than a few dozen tenge, Astra looks like the justified choice.

For large volumes of short tasks, another model wins on cost: GPT-6.1 Terra. At 10 ₸ per average question, it works well for emails, brief summaries, translation, and everyday explanations. Terra should not be dismissed just because it ranks fifth: its score of 9.0 out of 10 is still a strong result.

In QueryWise, you can run both models side by side: ask Terra and Astra the same question and immediately see where they agree and where they differ. That is more useful than guessing based on a model name. New users get three free questions to start with; the interface is available in Russian and Kazakh, and payments are processed in tenge.

Bottom line based on the current data: GPT-6 Astra leads on the overall score and is better suited to complex work, coding, and long-form materials. GPT-6.1 Terra is more affordable and remains the practical option for a high volume of short requests. If you want one all-purpose choice without considering the budget, choose GPT-6 Astra; if the cost of each request matters, choose Terra.