Comparing AI Models in Cursor: Opus 5.5 vs. Grok 4.7

Objective

As part of Hacktoberfest, I compared Opus 5.5 and Grok 4.7 in Cursor across three common software development tasks: TypeScript code generation, asynchronous JavaScript debugging, and code explanation.

Methodology

I used the same prompt for both models in each experiment and compared their outputs for correctness, readability, reasoning, edge-case handling, and practical usefulness.

Experiment 1: TypeScript Code Generation

Task: Reverse a string without using the built-in reverse() method, handle empty input, and provide three test cases.

Opus 5.5: Used for...of and prepended each Unicode code point to the result. It also explained the limitation involving multi-code-point emoji.

Grok 4.7: Used a backward index loop and appended characters to the result. The implementation was simple, but UTF-16 indexing can split supplementary Unicode characters.

Observation: Both solutions met the basic requirements, while Opus paid more attention to Unicode behavior.

Experiment 2: Asynchronous JavaScript Debugging

Task: Identify and fix the missing await in a function that fetches users.

Grok 4.7: Correctly identified the unresolved promise and provided a minimal fix.

Opus 5.5: Fixed the same issue, added an HTTP response-status check, and demonstrated caller-side error handling.

Observation: Both models found the main bug. Opus also addressed the fact that fetch() does not automatically reject for HTTP error statuses such as 404 or 500.

Experiment 3: Code Explanation

Task: Explain a chained filter() and map() expression, provide an example, analyze complexity, and identify edge cases.

Grok 4.7: Delivered a focused explanation, correct complexity analysis, and examples of JavaScript type coercion.

Opus 5.5: Provided a more extensive tutorial, intermediate results, and additional cases involving BigInt, sparse arrays, null, and numeric precision.

Observation: Both explanations were correct for the intended numeric-array use case. Opus explored a wider range of edge cases, while Grok was more concise.

Overall Findings

In these three experiments, both models produced useful solutions and explanations. Opus 5.5 consistently explored more edge cases and provided more detailed reasoning, whereas Grok 4.7 tended toward simpler, more direct responses.

This was a small, qualitative experiment rather than a standardized benchmark. The results demonstrate how the choice of model can affect the depth and style of AI-assisted development.

Conclusion

Testing the same prompts across models helped me look beyond the first answer and evaluate correctness, assumptions, and robustness. It also reinforced the importance of running tests and reviewing AI-generated code instead of relying on explanations alone.

Opus 5.5

Grok 4.7