GPT-4.1 or Muse Spark 1.3: which is better in QueryWise?

Answer quality: the difference is immediate

In the QueryWise ranking, GPT-4.1 scores 3.9 and holds 34 place. Muse Spark 1.3 scores 8.6 and ranks 9. Muse Spark 1.3 currently leads with. This is not a cosmetic gap: Muse Spark 1.3 sits in the upper part of the ranking, while GPT-4.1 trails it noticeably on the overall score.

That said, GPT-4.1 has a strength that can get lost behind the headline number. Its coding score is 8.6. Muse Spark 1.3 scores 9.4 in the same category. Muse Spark 1.3 comes out ahead here: in our evaluation, it handles programming tasks better. GPT-4.1 is still a practical choice when you need to generate a function quickly, fix an obvious bug, or explain an unfamiliar piece of code.

The reasoning results call for a more careful interpretation. GPT-4.1 has a score of 5.1, while the current data lists — for Muse Spark 1.3. That means it would be misleading to name a winner in this subcategory: one model has an available score, while the other may be assessed in a different format. The overall winner is clear, but answers involving complex chains of reasoning should still be checked against primary sources.

Price and speed in QueryWise

A typical question to GPT-4.1 costs about 10 ₸, while a question to Muse Spark 1.3 costs around 10 ₸. For users in Kazakhstan, that is practically the same spending level. The price difference does not justify choosing the weaker model: at roughly equal cost, it makes more sense to use Muse Spark 1.3 more often, especially for large or high-stakes tasks.

There is not yet enough data for a reliable median response time: QueryWise currently uses — for GPT-4.1 and — for Muse Spark 1.3. So claiming that one model is consistently faster would be speculation. For a simple question, you may barely notice any time difference. With a long prompt involving code, tables, or several conditions, latency depends on system load and response length.

Both models are available in QueryWise from Kazakhstan: payments are processed in tenge, the interface is available in Russian and Kazakh, and new users receive three free questions to get started. That is more convenient than arranging international payment and dealing with currency-conversion fees separately.

One-million-token context: the same scale, different working styles

GPT-4.1 has a context of 1M, while Muse Spark 1.3 has 1M. In practice, both models are designed for very large materials. You can upload a long lease agreement, an archive of technical documentation, or several project files and ask the model to identify connections between them. Context size alone does not guarantee that every detail will be used accurately.

GPT-4.1 is a sensible choice when you need to review a 200-page contract in an evening and compile a list of clauses for further checking. It is also useful for refactoring a specific module, generating tests, and explaining code errors. But it does not replace legal advice: its wording should be checked against the document and reviewed by a qualified specialist.

Muse Spark 1.3 looks stronger when the task calls for a more complete, ready-to-use result—for example, turning a description of an internal service into an API draft with error handling and test examples. Our evaluations give it the higher coding score, so the choice is simpler for this kind of scenario. For medical, legal, and financial decisions, both models remain assistants rather than sources of definitive advice.

We ran both models through a specific prompt: “Write a Python function that reads a CSV containing Kazakhstani phone numbers, normalizes their format, and returns the rows with errors.” GPT-4.1 confidently built the foundation and explained the regular expression. Muse Spark 1.3 offered a more complete version with a separate check for empty values and accompanying tests. This is one example, not proof of superiority across every task, but it aligns well with the difference in coding scores.

Which model should you choose?

Based on the current figures, Muse Spark 1.3 is the coding winner: it scores, compared with 8.6 for GPT-4.1. If you need a ready-made function draft, a set of tests, or an analysis of a project error, start with Muse Spark 1.3.

GPT-4.1 makes sense for very long materials when you already understand the structure of the prompt and are prepared to check the result manually. It is also useful as a second opinion on code. Although it loses on the overall score, its response may help reveal a missed condition or questionable line in the first model’s answer.

In QueryWise, both models are available side by side, so you can ask them the same question and see where they agree. This mode is useful for code and document analysis: matching answers still need to be checked, but disagreements quickly highlight what deserves a closer look.

The honest verdict

Muse Spark 1.3 wins on both the overall score and coding. At roughly the same price, it is the better choice for most technical prompts and tasks where a finished result matters. GPT-4.1 is not useless: keep it as a second opinion for long documents, code explanations, and checking debatable solutions. If you want one primary option in QueryWise, choose Muse Spark 1.3; for a difficult task, compare its answer with GPT-4.1 in the same conversation.