Grok-Code — coding benchmarks and reality

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-modelx-grok-code-builders

1. What type of application is Grok-Code designed to specialize in?

   1. Video editing workflows
   2. Developer coding tasks
   3. Database administration
   4. Email marketing campaigns

2. Based on the comparison table, which model has the lowest price per million output tokens?

   1. Claude Sonnet 4.6
   2. GPT-5.5
   3. Grok-Code
   4. All three cost the same

3. Which capability is NOT listed as a strength of Grok-Code?

   1. Shell and test-runner tool use
   2. Multi-file edits with explicit diffs
   3. Explaining legacy code in plain English
   4. Writing marketing copy for products

4. What does the lesson say matters more than a small benchmark score difference?

   1. Token pricing
   2. The actual dev experience gap
   3. The number of parameters
   4. Model size

5. According to the comparison table, which model has the lowest refusal tendency?

   1. Grok-Code
   2. All three are equally willing
   3. Claude Sonnet 4.6
   4. GPT-5.5

6. What is SWE-bench used to measure?

   1. Image generation quality
   2. Natural language fluency
   3. Speech recognition accuracy
   4. Coding model performance on real-world software engineering tasks

7. A developer wants to integrate a coding model into their IDE. Based on the lesson, which model has the most polished IDE extensions available?

   1. The lesson says all three are equal
   2. Grok-Code
   3. GPT-5.5
   4. Claude Sonnet 4.6

8. If a startup is building a coding assistant and wants to minimize costs while maintaining high benchmark performance, which model would the lesson suggest is most promising based on its pricing?

   1. Any of the three would work equally
   2. GPT-5.5
   3. Grok-Code
   4. Claude Sonnet 4.6

9. A developer is working with a large codebase written years ago by other engineers. Which Grok-Code feature would be most helpful for understanding this code?

   1. Multi-file edits with explicit diffs
   2. Shell and test-runner tool use
   3. Image generation capabilities
   4. Explaining legacy code in plain English

10. What does the comparison table indicate about Grok-Code's benchmark performance relative to Claude Sonnet 4.6 and GPT-5.5?

    1. It scores equally high
    2. It scores significantly lower
    3. The table shows no data
    4. It is not measured on benchmarks

11. A team is choosing between coding models. They prioritize being able to run shell commands and test runners directly. Which model best matches this need based on the lesson?

    1. Grok-Code
    2. GPT-5.5
    3. None of the models support this
    4. Claude Sonnet 4.6

12. The lesson mentions that Grok-Code scores well on benchmarks but has a weakness. What is this weakness?

    1. High refusal rate
    2. Inability to handle multi-file projects
    3. Fewer polished IDE extensions compared to competitors
    4. Poor code generation quality

13. What type of fine-tune is Grok-Code described as?

    1. A specialist fine-tune for developer workflows
    2. A general-purpose language model
    3. An image generation model
    4. A voice assistant

14. A developer needs to refactor code that involves changing imports and type annotations across multiple files. Which model capability would be most relevant?

    1. Multi-file edits with explicit diffs
    2. Email composition
    3. Image generation
    4. Video rendering

15. What does the lesson identify as a key term related to coding models?

    1. Graphic design
    2. Tool use
    3. Cooking recipes
    4. Social media marketing