GLM 5.2 or GPT-6 Astra: which is better on QueryWise?

Quality: the difference is obvious right away

GLM 5.2 from Z.ai currently has a QueryWise score of 6.7 and ranks 24. OpenAI’s GPT-6 Astra is in 4 place with 9.3. So there is already a clear answer to the broad question “which is better?”: GPT-6 Astra leads on the current score.

Still, GLM 5.2 should not be dismissed. Its coding score is 8.7, while its reasoning score is 9.2. It can untangle complicated requirements and produce working answers without a long warm-up. GPT-6 Astra scores 9.5 for coding, so GPT-6 Astra currently has the edge on programming tasks.

We put both models through real-world prompts. Asked to “find the error in this Python snippet, explain the cause, and rewrite the function so it does not fail on an empty list,” GLM 5.2 usually keeps the response well structured and provides corrected code. GPT-6 Astra more often checks edge cases and presents the final version more carefully. For a simple fix, the difference is small; in a larger project excerpt, GPT-6 Astra comes out ahead.

Speed and pricing in tenge

GLM 5.2 responds at a median speed of 13 seconds. That does not justify calling one model fast and the other slow: there simply is not a reliable sample for Astra yet.

Price affects the practical choice more than a gap in polished benchmark results. An average question sent to GLM 5.2 costs about 10 ₸, while one sent to GPT-6 Astra costs roughly 43 ₸. If you need to review dozens of contract pages, clean up a spreadsheet, or rewrite several small functions in one evening, GLM 5.2 is noticeably easier on the budget. For constant coding work, the more expensive GPT-6 Astra can be worth it when its extra accuracy saves time on manual review.

Both models are available from Kazakhstan on QueryWise: payments are made in tenge, the interface is available in Russian and Kazakh, and new users get three free questions. You can send one prompt to both models and see where they agree—and where one starts arguing with the requirements or overlooks a detail.

Context and task types

GLM 5.2 has a context of 1M, while GPT-6 Astra has 1.1M. The difference is real, but it should not be turned into a marketing gimmick. Both windows are designed for substantial material: documentation, long conversations, multiple code files, and extensive reports.

GLM 5.2 performs well in three practical scenarios: quickly drafting an SQL query, breaking down a technical guide in Russian and Kazakh, or finding a contradiction in a long list of requirements. Its low price makes these requests easy to run without rationing every prompt.

GPT-6 Astra is the more sensible choice when a task involves several connected steps. For example, you can provide a description of an online store, a database schema, and an error list, then ask for API changes, a migration, and tests. On this kind of request, GPT-6 Astra is more likely to preserve dependencies between the different parts of the answer. It is also useful for study when you need more than a formula: you can check the reasoning and ask for another way to explain it.

Both models have a downside: a long context does not guarantee that every fact will be checked perfectly. If an incorrect number is hidden in the source material, the model may treat it as true. Medical, legal, and financial decisions should not be handed over to this chat without review by a qualified professional.

The honest verdict

For coding, the winner by the current sub-score is GPT-6 Astra: it has 9.5 versus 8.7 for its rival.

GLM 5.2 wins on another dimension: it is the more rational choice for frequent, inexpensive questions, especially when an answer is needed in about 13 seconds and the task does not require maximum depth. GPT-6 Astra costs more, but its current score of 9.3, compared with 6.7 for GLM 5.2, better supports choosing it for complex code, large specifications, and final editorial review.

The undiplomatic bottom line: choose GPT-6 Astra for quality and demanding coding work. Choose GLM 5.2 for economical everyday use. QueryWise is convenient because you can test both models on the same question with three free prompts, then pay in tenge only for the level of response you actually need.