GPT-6 Sol or Grok 4.7 — which is better in QueryWise?

This pair is not a story about having “one model for everything.” GPT-6 Sol ranks 10th in the QueryWise leaderboard with a score of 8.6, while Grok 4.7 is 12th with 8.3. The gap changes along with the live leaderboard, so the final answer here is tied to the tokens rather than a fixed number.

Quality: GPT-6 Sol leads

GPT-6 Sol leads QueryWise’s overall score with. That is the main practical takeaway for anyone choosing one model without wanting to fine-tune every setting. When the scores are close, the difference is better found in how the models respond: GPT-6 Sol is designed to handle very long material, while Grok 4.7 offers a smaller but still substantial context window.

GPT-6 Sol has a score of 8.6 and ranks 10th. Grok 4.7 has a score of 8.3 and ranks 12th. The current overall winner is GPT-6 Sol, not simply the model that makes the loudest claims about its capabilities.

That means it would not be fair to name a winner in either category right now. The overall winner is already clear: GPT-6 Sol.

Price and speed in tenge

An average question to GPT-6 Sol costs approximately 10 ₸, while a question to Grok 4.7 costs about 10 ₸. For ordinary use, the difference is not large enough to make one model an obvious choice over the other: both sit in the same price range. If you send dozens of short requests during a workday, the total will still depend on the number of requests and the length of the conversation.

Calling one model faster would therefore be guesswork. Both are available from Kazakhstan in QueryWise, accept payment in tenge, and work with interfaces in Russian and Kazakh. You can start with 3 free questions for each model.

Long documents and specific tasks

GPT-6 Sol has a context of 1.1M, compared with 500K for Grok 4.7. The difference matters when a question involves a large amount of source material. Consider a contract several hundred pages long: GPT-6 Sol is the more logical choice for finding contradictions between sections, mapping obligations, and checking whether an exception was missed in an appendix. A legal professional should still provide the final legal assessment.

Grok 4.7 makes more sense for a compact working dialogue. For example, you can ask it to analyze correspondence with a client, identify disputed requirements, and draft a calm response. There is no need to upload a huge archive for this scenario, and the result depends mainly on how precisely the request is phrased.

We tested both models with a real question: “Prepare a brief note in Russian on a Kazakhstani supply contract, listing payment deadlines, penalties, and clauses that require review separately.” GPT-6 Sol benefited from a prompt containing the full contract: it preserved the document structure and connected the penalty to the payment section. Grok 4.7 performed better on the shortened version: it quickly produced a clear draft, but the additional lengthy conditions had to be highlighted in separate messages. This is a working draft, not legal advice.

There is also an everyday example. If you need to review a 200-page report in one evening and ask it a series of follow-up questions, GPT-6 Sol’s larger context is a practical advantage. If the task is editing an email, briefly explaining a topic, or rewriting text in a business style, you do not need to pay extra for a large context window: the two models are similarly priced.

What to choose in QueryWise

Choose GPT-6 Sol for long documents and step-by-step analysis. Its context of 1.1M is considerably wider, and its current overall score is 8.6. This is a choice for working with extensive source material, not proof of absolute superiority in every task.

Grok 4.7 suits compact dialogues and affordable everyday requests. It costs about 10 ₸, has a context of 500K, and an overall score of 8.3. However, GPT-6 Sol currently leads the QueryWise ranking with, so if you want one general-purpose model, the winner is GPT-6 Sol.

In QueryWise, you can send one question to GPT-6 Sol and Grok 4.7 at the same time, then see where their answers agree and where they differ. This is more useful than marketing promises: on a difficult question, matching wording does not guarantee correctness, while a disagreement immediately points to something worth checking.