DeepSeek V4 Pro or Kimi K3 — Which Is Better in QueryWise?

The comparison is quite revealing. DeepSeek V4 Pro, developed by DeepSeek, scores 7.8 out of 10 and ranks 14 in QueryWise. Kimi K3 from Moonshot currently scores 9.4 out of 10 and holds position 3. So the formal answer is already clear: Kimi K3 is ahead on the current score.

But a single overall score does not explain why the models differ. Coding matters more to a developer, while a student may care more about retaining the task requirements and explaining the reasoning behind a solution. Sometimes the price of a single request is the deciding factor. There is a difference here too.

Answer quality according to our data

DeepSeek V4 Pro scores 8.1 in coding and 9.0 in reasoning. That is a strong profile for technical questions: the model is well suited to debugging, writing small functions, and checking architectural decisions. It works especially well if you are prepared to clarify requirements and ask it to verify the result.

Kimi K3 scores higher: 9.7 for coding and 9.2 for reasoning. In practical work, this is noticeable on tasks that require several constraints to be handled at once: for example, building an API with validation, handling edge cases, and adding tests. Kimi more often feels like a polished implementer rather than a generator of a first draft.

The gap in reasoning may be smaller than the gap in programming, but the current values should still be reviewed separately. If a question involves logic, comparing options, or a long chain of conditions, use 9.0 versus 9.2 as your reference. For coding, the picture is 8.1 versus 9.7. That makes the verdict more meaningful than simply saying, “This model is smarter.”

Speed and price in tenge

The median response speed for Kimi K3 in QueryWise telemetry is 29 seconds. There is not yet enough data for DeepSeek V4 Pro, so its speed is displayed as —. That is not a reason to label DeepSeek slow: the sample is simply not large enough for a careful comparison.

An average QueryWise request costs approximately “≈10 ₸” for DeepSeek V4 Pro and “≈13 ₸” for Kimi K3. The difference per request is small, but hundreds of requests per month can turn it into a noticeable amount. DeepSeek looks like the more sensible option for drafts, quick explanations, and regularly testing ideas. Kimi justifies the premium when a coding error or missed requirement would cost more than a few tenge.

We added Kimi K3 to QueryWise on its release day and have evaluated it since then based not on polished demos, but on real user requests. Kimi’s reliability in our current telemetry is . There is still not enough data for DeepSeek: . Telemetry changes over time, so this page shows live values rather than freezing them until the next update.

Context and task types

Both models work with contexts of 1M and 1M. This is useful when you need to upload a long document, a large code excerpt, or several related messages. A larger context window does not guarantee consistent quality across its entire length, but both models are better suited to serious work materials than tools with short contexts.

I would choose DeepSeek V4 Pro for tasks such as:

• debugging Python or JavaScript and suggesting several possible causes;
• condensing a long technical document into a clear plan;
• explaining a formula, algorithm, or database structure to a student in plain language.

DeepSeek’s price is particularly attractive in these scenarios. For studying in Kazakhstan, where answers in Russian and Kazakh matter, it is a practical option: QueryWise offers RU/KK interfaces and accepts payment in tenge.

Kimi K3 looks stronger when you need a finished result with fewer manual corrections. For example, you can ask it to design an endpoint for an online store, write an SQL query with grouping and filters, or compare two database migration approaches. Another good use case is analyzing several requirements files when a contradiction is hidden in one of the sections.

In QueryWise, you can send the same prompt to both models and see where they agree and where they differ. For complex code, this is more useful than blindly trusting a single answer. Human review is still necessary, however: models do not replace code review, medical advice, legal assistance, or a financial professional.

Our honest verdict

Based on the current score, the winner is Kimi K3. If your priorities are programming, complex technical requirements, and a more consistent result, choose Kimi K3: its current coding score of 9.7, reasoning score of 9.2, and reliability of give it an advantage.

If you ask inexpensive questions frequently, study, create drafts, or want to reduce the cost of high-volume requests, DeepSeek V4 Pro looks more sensible. Its price is approximately “≈10 ₸”, while its current score is 7.8. It is not the winner of this comparison, but it is a solid working option for the money.

The conclusion is simple: Kimi K3 leads the overall QueryWise ranking, while DeepSeek V4 Pro remains a strong choice for budget-conscious and educational use cases. The ranking updates every hour, so check the current values of 7.8 and 9.4 before an important task.