GLM 5.2 or Kimi K3.1 Pro: which is stronger on QueryWise?

GLM 5.2 from Z.ai and Kimi K3.1 Pro from Moonshot cost roughly the same on QueryWise: about 10 ₸ and 10 ₸ per average question. So price does not explain the difference here. Users are choosing between a model that performs well on specific difficult tasks and a contender near the top of the rankings.

Both versions are available in Kazakhstan: payments are processed in tenge, the interface is available in Russian and Kazakh, and new users receive three free questions. It is a convenient way to test the models on your own documents and work-related prompts without buying a separate subscription.

Quality: Kimi leads comfortably on the overall score

On the QueryWise leaderboard, GLM 5.2 scores 6.7 out of 10 and ranks 26th. Kimi K3.1 Pro is in 3rd place with a score of 9.5 out of 10. Kimi K3.1 Pro is currently ahead: this is not a rounding artifact, but a meaningful gap between the models in QueryWise’s overall evaluation.

GLM 5.2 is not a weak model. Its coding and reasoning scores are 8.7 and 9.2. That combination is especially useful when a prompt requires unpacking the conditions, finding an algorithmic error, or checking several constraints step by step. For a one-off technical task, GLM may be more accurate than its overall ranking suggests.

Kimi K3.1 Pro currently scores — for coding and — for reasoning. On QueryWise, these figures should be considered alongside the overall score: that is what currently places Kimi above GLM. If you need one general-purpose choice for difficult prompts, Kimi has the edge.

Speed and price in tenge

Our measurements for GLM 5.2 show a median response time of 13 seconds. Telemetry reports 100% reliability. That is a strong profile for work chats: the model does not keep you waiting and consistently returns results in tested scenarios.

There is not yet enough data on Kimi K3.1 Pro’s median speed and reliability. We do not fill the gap with guesses: the model has a high QueryWise ranking, but full telemetry is still being collected. So GLM currently wins on measurement transparency, not on proven quality.

In cost, the models are practically tied: GLM costs about 10 ₸ per average question, while Kimi costs 10 ₸. If you ask ten questions a day, the price difference will not be decisive. For Kazakhstan, it matters more that both amounts are charged in tenge, without a separate calculation through a foreign-currency tariff.

Context and task types

Both models support contexts of up to 1M and 1M tokens. That is enough for substantial work: for example, you can upload a long lease agreement, ask it to find penalty clauses, and then compare them with a specific situation. The result should not be treated as legal advice, but it can be useful for an initial document review.

GLM 5.2 is the more sensible choice when the task looks like debugging. For example: “Find the error in this Python code, explain the cause, and suggest tests for three edge cases.” In our checks, GLM broke this kind of request into clear steps and kept track of the conditions. If you ask it to compare a large set of sources, however, it may sometimes repeat generic wording instead of giving a clear conclusion.

Kimi K3.1 Pro looks stronger on prompts that require keeping track of many inputs. A practical example: “Create a launch plan for a Kazakhstan-based online store, taking into account a budget in tenge, delivery between Almaty and Astana, returns, and two customer groups.” Here, consistency across constraints matters more than polished paragraphs. Based on the current overall QueryWise score, Kimi is the better choice for this kind of complex prompt.

The difference also depends on the format when studying. GLM is useful for working through one formula, checking a solution, or explaining a coding error. Kimi is preferable when you need to turn several textbook chapters into a clear outline in one evening and prepare self-check questions. Facts and links still need to be verified: both models can make mistakes.

Verdict by use case

If you want one winner based on the current QueryWise ranking, it is Kimi K3.1 Pro. The current ranking token is used here: the comparison leader is determined by the value of Kimi K3.1 Pro, while the model scores are 6.7 and 9.5. In practical terms, Kimi K3.1 Pro wins as the stronger general-purpose model, while GLM 5.2 gets the advantage when its known speed of 13 seconds and confirmed 100% reliability matter.

In these two areas, the model with the higher relevant token leads, and that may not be the same as the overall ranking leader. We do not turn an individual score into a universal conclusion.

On QueryWise, you can run GLM 5.2 and Kimi K3.1 Pro side by side and see where their answers agree. That is more useful than marketing promises: agreement on facts increases confidence, while disagreement highlights something to check manually. Based on current data, there is no need to pay more for Kimi because the price is nearly the same. But if you want the best overall result, choose Kimi K3.1 Pro.

GLM 5.2 remains a rational choice for quick technical questions and scenarios where confirmed stability matters. Kimi K3.1 Pro is worth choosing for large multi-step prompts, long materials, and general-purpose work. Neither model replaces a doctor, lawyer, or financial adviser.