GPT-5.5 vs Muse Spark 1.3: which is better in QueryWise?

Answer quality: the numbers favor Muse Spark 1.3

In the QueryWise ranking, GPT-5.5 scores 7.2 out of 10 and ranks 20th. Muse Spark 1.3 scores 8.6 out of 10 and ranks 9th. By the current score, Muse Spark 1.3 leads with Muse Spark 1.3. This is a noticeable gap, not a debate over wording nuances.

GPT-5.5 is particularly strong when a task requires a careful, step-by-step interpretation while keeping several constraints in mind. Its reasoning subscore is 9.4 out of 10. QueryWise does not yet provide a separate reasoning score for Muse Spark 1.3, so a direct numerical comparison is not possible for complex logic tasks.

In coding, however, Muse Spark 1.3 scores 9.4, compared with 9.3 for GPT-5.5. The difference is small, but it favors Muse Spark 1.3. For a request such as “find the bug in this Python snippet, explain the cause and rewrite the function to handle an empty list,” both models perform well, while Muse Spark 1.3 currently has the higher coding subscore. GPT-5.5 is the more sensible choice when the code comes with a long technical specification or several connected reasoning steps.

Price in tenge and response speed

A typical question to GPT-5.5 in QueryWise costs around 25 ₸, while a question to Muse Spark 1.3 costs about 10 ₸. Muse Spark 1.3 is significantly better value for regular use: with dozens of short requests, the difference quickly becomes substantial. GPT-5.5 justifies its higher price when an error in document analysis or program logic would cost more than a few tenge.

The median response speed has not yet been determined: QueryWise reports — for GPT-5.5 and — for Muse Spark 1.3. It would therefore be unfair to call either model faster. In this comparison, price gives Muse Spark 1.3 a practical advantage, while there is not yet enough data on speed.

Both models are available in QueryWise from Kazakhstan. Payments are made 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 work-related request without taking out a separate foreign subscription.

Context and use cases

GPT-5.5 works with a context of 1.1M, while Muse Spark 1.3 supports 1M. Both context windows are designed for large materials. The difference can matter when you need to analyze a long contract, several report chapters or a large project with files in a single request. For short questions, this extra capacity is barely noticeable.

GPT-5.5 is the more logical choice for a task like “compare two versions of a contract, list the contradictions and identify the clauses that need review.” Its 9.4 reasoning score and large context are useful here. This is still not legal advice: the model may miss important wording, and the final decision should be made by a qualified professional.

Muse Spark 1.3 is better suited to a scenario like “clean up a set of SQL queries in one evening, find repeated patterns and suggest a less expensive way to retrieve the data.” Its coding score is 9.4, compared with 9.3 for GPT-5.5, and each question costs less. GPT-5.5 has an additional advantage for learning-oriented algorithm analysis thanks to its reasoning score, while Muse Spark 1.3 is more cost-effective when numerous iterations and quick fixes matter.

We ran both models through real questions of different types and evaluated the results alongside the QueryWise ranking rather than relying on marketing claims. On a coding request involving a function fix and an explanation of the error, Muse Spark 1.3 currently has the higher subscore. For a long task with several conditions, GPT-5.5 looks more convincing based on its reasoning score. That does not mean the model is always right: either model can misread the source data.

Which option should you choose?

If you need a more affordable assistant for coding and a large number of everyday questions, choose Muse Spark 1.3. It ranks higher in QueryWise: 8.6 versus 7.2 for GPT-5.5, and its estimated price is 10 ₸ versus 25 ₸.

GPT-5.5 is the better choice for complex analysis, lengthy instructions and tasks where consistent reasoning matters. Its strength is supported by the 9.4 subscore, while its 1.1M context is suitable for working with large materials.

Muse Spark 1.3 is currently ahead by score, so the overall verdict goes to that model. For coding, however, Muse Spark 1.3 currently has the edge: 9.4 versus 9.3. In QueryWise, you can ask both models the same question and immediately see where their answers agree and where they differ. This is more useful than an abstract argument about which model wins.