GLM 5.2 or Kimi K3 — which is better in QueryWise?

These models are easier to compare by looking beyond marketing claims and focusing on what QueryWise users actually see: the overall score, speed, price, and performance on specific tasks. Both models support contexts of 1M and 1M, while their reliability in our telemetry is 100%. In other words, the choice is not about which model is “smarter in general,” but what you are willing to pay for and how many seconds you are willing to wait.

Quality: Kimi K3 is currently ahead

In the QueryWise ranking, GLM 5.2 scores 8.6 out of 10 and ranks 11. Kimi K3 scores 9.4 out of 10 and holds position 3. By the current score, Kimi K3 is ahead with Kimi K3. It is better to view the gap as a practical difference rather than a chasm: both models are already suitable for serious work, but Kimi K3 will more often be the first choice for difficult prompts.

For coding, GLM 5.2 scores 8.9, while Kimi K3 scores 9.7. Kimi K3 has the clearer advantage here: it is better suited to analyzing someone else’s code, finding the cause of an error, and preparing several related changes in a project. If you only need a small script quickly, the difference may barely be noticeable. It becomes more apparent on larger tasks.

For reasoning, GLM 5.2 scores 9.0, compared with 9.2 for Kimi K3. On this measure, Kimi K3 is ahead in the comparison. GLM 5.2 is not a weak option, though: it remains competitive for analyzing conditions, checking arguments, and structuring a long prompt. Results depend on how the request is phrased, so for a critical task it makes sense to run the same prompt through both models.

Speed and price in tenge

The median GLM 5.2 response in our telemetry takes 38 seconds. Kimi K3 completes the task in 29 seconds. If you ask one question in the evening, the difference is barely noticeable. In a workflow where you need to check dozens of text or code snippets, the faster model saves time and reduces the frustration of waiting.

The average price of a question in QueryWise is approximately 10 ₸ for GLM 5.2 and 13 ₸ for Kimi K3. That makes GLM 5.2 the more rational choice for frequent prompts, drafts, summaries, and preliminary analysis. Kimi K3 costs more, but the extra charge may be justified when speed and code quality matter. This is not investment or financial advice—just a comparison of the cost of one request in the service.

We added it on release day and immediately ran it through the same set of real-world prompts. In daily use, Kimi K3 feels more responsive, while GLM 5.2 is easier on the budget. Both models are available in QueryWise in Kazakhstan: payments are made in tenge, the interface is available in Russian and Kazakh, and new users receive three free questions to get started.

Context and task types

Both models have large context windows: 1M for GLM 5.2 and 1M for Kimi K3. This is useful when you upload lengthy documentation, several project files, or a large contract for an initial review. But window size alone does not guarantee a perfect answer: a model may miss a detail if the prompt is vague.

Where GLM 5.2 makes more sense

GLM 5.2 works well for inexpensive, repetitive tasks: turning a long meeting into a concise plan, comparing two document versions, explaining a formula to a student, or preparing a report outline. For studying, it is a convenient assistant when you need several explanations in a row without overpaying for every question. You will still need to verify the facts yourself.

Where Kimi K3 wins

Kimi K3 is the more logical choice for programming, complex analysis, and working with large volumes of material. For example, you can ask it to find an incompatibility between an API and client code, break application requirements into stages, or compare provisions across several long documents. According to current QueryWise evaluations, coding is where Kimi K3 has its most noticeable edge.

In QueryWise, you can send the same prompt to both models and see where they agree and where they diverge. This is more useful than an abstract ranking: if the answers match on facts and logic, confidence increases; if they do not, you can check the disputed passage separately.

An honest verdict

Kimi K3 is currently ahead by score: . For programming and demanding tasks where speed matters, my choice is Kimi K3 if the current 9.7 and 29 figures retain their advantage. For frequent prompts, studying, and budget-conscious tasks, I would choose GLM 5.2: at 10 ₸ per average question, its cumulative cost is noticeably easier to manage.

If you want one all-purpose favorite based on the ranking, choose Kimi K3. If the cost of each request matters more, GLM 5.2 looks like the smarter option. Before subscribing or sending a large batch of prompts, use the three free questions in QueryWise and compare both models on your own data.