GLM 5.2 vs Muse Spark 1.3: which one is better?

Answer quality: which model leads on score?

In the QueryWise ranking, GLM 5.2 is in 24 place with 6.7 out of 10. Muse Spark 1.3 ranks 9 and scores 8.6 out of 10. Based on the current overall score, Muse Spark 1.3 is ahead. It is the better choice if you want one model without spending time analyzing every scenario.

GLM 5.2 has a strength that the overall result slightly hides: its reasoning score is 9.2. That is a solid foundation for questions that require analyzing conditions, finding contradictions, or working through a chain of calculations. The model scores 8.7 for coding.

Muse Spark 1.3 stands out in coding, with a score of 9.4. That is a more convincing result for writing a function, finding a bug in a handler, or rewriting an SQL query. So it would be wrong to automatically give it the edge on complex logic.

The top of the QueryWise ranking currently includes Claude Opus 5.5 with 9.9, GPT-6 Astra with 9.9, and other models. GLM 5.2 has not reached that level, while Muse Spark 1.3 is already among the notable options: its result is higher than GLM 5.2's.

Speed and price in tenge

GLM 5.2 responds in a median of 13 seconds. That is not instant for an ordinary question, but the wait is predictable. In our measurements, GLM 5.2 achieved 100% reliability.

So claiming that it is faster than GLM 5.2 would be speculation. For a workflow where you need an answer every few minutes, this telemetry gap matters.

An average question costs approximately ≈10 ₸ with GLM 5.2 and ≈10 ₸ with Muse Spark 1.3. There is no practical price winner here: neither model carries a meaningful premium. If you need to analyze a long document or make a series of code edits in one evening, choose based on quality rather than a difference of a few tenge.

Context and real-world tasks

The context windows are 1M for GLM 5.2 and 1M for Muse Spark 1.3. That is enough to upload a large contract, a technical specification, or several related files and ask the model to work with the material as a whole. But context size alone does not guarantee an accurate answer.

When you need to check the terms of a 200-page contract, GLM 5.2 looks preferable thanks to its reasoning score of 9.2. You could ask: “Find the clauses where the termination period conflicts with the section on penalties, and explain the consequences.” This tests more than summarization: it tests the ability to connect distant parts of a document.

For programming, the choice shifts toward Muse Spark 1.3. For example, you can give it a Python service with a time-zone handling bug and ask it to add tests. With a coding score of 9.4, it is the stronger candidate for an initial review and fix. GLM 5.2 also scores 8.7, so it will handle a simple function or SQL query, but it trails Muse Spark 1.3 in the coding ranking.

We ran GLM 5.2 through questions with several constraints. It generally keeps track of the conditions and lays out the solution step by step. In a prompt about conflicting contract deadlines, it does not jump straight to generic advice. Muse Spark 1.3 is more useful when you need a working code snippet, a test review, or a careful edit to an existing solution. These are different profiles, not simply two versions of the same assistant.

In QueryWise, you can place both models side by side and see where they agree and where they diverge. This is especially useful for a disputed technical decision: Muse Spark 1.3 suggests an implementation, while GLM 5.2 checks the logic and constraints.

The honest verdict

Based on the current score, the winner is Muse Spark 1.3. If you need one general-purpose choice for new tasks, pick the model with the higher Muse Spark 1.3 value and Muse Spark 1.3 ranking. It performs better in QueryWise's overall assessment, not just in one isolated test.

For coding, the winner is Muse Spark 1.3: 9.4 versus 8.7 for GLM 5.2. Their prices are practically identical, so the task should determine your choice.

Both models are available in QueryWise from Kazakhstan: payments are accepted in tenge, the interface is available in Russian and Kazakh, and new users receive three free questions when they start. You can try both without a separate subscription to an overseas service. Just do not treat a model's answer as medical, legal, or financial advice.