GPT-OSS 120B vs Muse Spark 1.3: which is better?

Quality: the difference is obvious

In the QueryWise ranking, GPT-OSS 120B is currently in 35 place with a score of 3.7 out of 10. Muse Spark 1.3 ranks 9 and scores 8.6 out of 10. Muse Spark 1.3 leads on the overall score, while the current QueryWise leader, Claude Opus 5.5, scores 9.9.

The gap is especially clear in practical work. GPT-OSS 120B scored 5.0 for coding and 9.9 for reasoning. Muse Spark 1.3 has a coding score of 9.4, while its reasoning metric is listed as — in the current QueryWise data. So for a task that requires working code quickly, Muse Spark 1.3 currently has the edge on the relevant metric.

GPT-OSS 120B has an interesting trait: strong reasoning does not translate into a high overall score. The model may analyze a prompt confidently but perform less well on a complete practical test requiring accurate code, a specific response format, and compliance with constraints. That is a useful warning against choosing a model based on one impressive number.

Speed and price in tenge

There is not yet enough data to calculate a median response speed for either GPT-OSS 120B or Muse Spark 1.3. QueryWise lists — for the first model and — for the second. It is too early to conclude that one is faster: that would simply be speculation.

An average question costs about 10 ₸ with GPT-OSS 120B and 10 ₸ with Muse Spark 1.3. For most users, this difference is not meaningful enough to determine the choice. Ten questions cost roughly ten times as much as one request to either model, but the final amount depends on the current QueryWise rate.

Both models are available in Kazakhstan: payments are made in tenge, the interface is available in Russian and Kazakh, and three free questions are included at the start. This makes it easy to test your own workflows before buying a separate subscription.

Context and task types

GPT-OSS 120B works with a context of 131K, while Muse Spark 1.3 accepts up to 1M. For a short prompt, this hardly matters. The difference appears when you need to upload a long contract, technical specification, or several code files to the chat.

Consider a specific request: “Find all early-termination conditions in this 200-page contract, put them in a table, and cite the relevant clauses.” With a large amount of source material, Muse Spark 1.3 looks more practical thanks to its 1M context. GPT-OSS 120B can also analyze documents, but with a long input you will need to watch the 131K limit more carefully.

Another example: “Write a Python function that combines CSV files, removes duplicates by national ID number, and produces an error report.” Here, coding becomes the deciding factor. Muse Spark 1.3 leads on the current subscore, so I would start with that model and then verify the result by running it locally. Neither model should be treated as a replacement for tests and code review.

For a learning question such as “explain why this SQL JOIN returns extra rows and show the corrected query,” GPT-OSS 120B may be useful because of its 9.9 reasoning score. But if the answer must include a clean, executable example right away, Muse Spark 1.3 has a coding score of 9.4, which is a strong point in its favor.

We tested the models with questions involving format constraints and code. GPT-OSS 120B confidently breaks down complex requirements step by step, but its practical code more often needs syntax and edge-case checks. Muse Spark 1.3 looks more focused on these tasks, especially when the goal is a ready-to-run programming result rather than an explanation alone.

A fair choice by use case

For coding, I would choose Muse Spark 1.3: the current coding-score difference is 9.4 versus 5.0 in favor of the corresponding model. If the task is about unpacking complex logic rather than writing code, GPT-OSS 120B deserves a separate test because of its 9.9 score.

For long documents, the choice is clearer: Muse Spark 1.3, with its 1M context, is more convenient when the material cannot be shortened manually. For budget-conscious use, both models are in roughly the same price range, so focus on the result rather than a difference of a few tenge.

In QueryWise, you can send the same question to both models and see where they agree and where they diverge. This is particularly useful for a contract, a calculation, or code that must pass review. Muse Spark 1.3 currently leads on the overall score. I would keep GPT-OSS 120B as a second opinion for complex reasoning and choose Muse Spark 1.3 for primary work when coding and a large context matter.

The right choice depends on the task, but not enough to call the models equal. At a similar price, Muse Spark 1.3 is currently stronger as an all-purpose work model, while GPT-OSS 120B is more interesting when you need to untangle difficult logic and get an independent check of the answer.