OpenAI Model Picker: GPT-5.5, GPT-5.4, Mini, Nano, and Codex

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-openai-model-picker-creators

1. A developer needs to build a classification system that processes thousands of customer support emails per hour. Which model should they start with according to the model picker strategy?

   1. gpt-5.3-codex optimized specifically for agentic loops
   2. gpt-5.4-nano because it is the cheapest GPT-5.4-class option for high volume
   3. gpt-5.5 for maximum accuracy on complex text
   4. gpt-5.4-mini for balanced cost and capability

2. What does the model picker approach recommend when your current model successfully handles a task but you want to optimize for lower operational costs?

   1. Downgrade to a smaller model and run evals to verify quality remains acceptable
   2. Switch to a completely different model family outside OpenAI
   3. Always upgrade to the flagship model for consistency
   4. Keep the current model since it works reliably

3. A team is building an AI coding assistant that will autonomously refactor large codebases inside an agentic loop. Which model best matches this use case?

   1. gpt-5.3-codex optimized for coding-agent loops
   2. gpt-5.4-mini for faster iteration speed
   3. gpt-5.5 because it is the flagship model with highest quality
   4. gpt-5.4-nano for lowest cost per operation

4. When establishing a baseline for model performance evaluation, what should you run first according to the picker methodology?

   1. The cheapest model to establish a cost floor
   2. A model from a different provider for comparison
   3. A random model to see what happens
   4. The frontier model to establish a quality ceiling

5. A developer is building a system that requires very hard slow analysis where accuracy is more important than response time. Which model variant should they consider?

   1. gpt-5.4-nano for fastest processing
   2. gpt-5.4-mini for cost-sensitive operations
   3. pro variant or background mode for more compute
   4. gpt-5.3-codex for coding-specific tasks

6. What does the lesson recommend about storing model identifiers in your application code?

   1. Keep them scattered throughout the codebase for flexibility
   2. Use the newest model name available
   3. Update them every time a new model is released
   4. Lock the chosen model ID in config, not scattered across code

7. What key metrics should be measured during model evaluation according to the picker methodology?

   1. Latency, refusals, schema validity, and human acceptance
   2. Only accuracy and speed
   3. Price per token only
   4. Number of API calls made

8. A startup is building a prototype and needs strong AI capabilities but has limited budget. According to the model picker approach, what should they do first?

   1. Skip AI entirely and build without it
   2. Use the most expensive model to ensure quality
   3. Use a free model regardless of quality needs
   4. Start with the smallest model that can reliably do the job

9. What is the recommended source for determining the exact current model IDs before deploying to production?

   1. Assuming the model names from the lesson are permanent
   2. OpenAI's current model guide or documentation
   3. Social media discussions about AI
   4. Model leaderboards on third-party websites

10. When would it make sense to upgrade from gpt-5.4-mini to gpt-5.5 for a coding task?

    1. When the mini model is too expensive
    2. When evals show the quality improvement justifies the higher latency and cost
    3. When you want to use less compute
    4. When the task is simple classification

11. What type of tasks is gpt-5.4-mini specifically designed for according to the model picker guide?

    1. Agentic coding loops
    2. Maximum quality complex reasoning
    3. Simple classification and extraction
    4. Cost-sensitive coding or subagents where latency matters

12. A developer wants to build an eval set for model selection. What does the lesson recommend as the source for these evaluation tasks?

    1. Random internet queries
    2. The model's training data
    3. Real user tasks from your specific use case
    4. Academic benchmarks only

13. What happens if you write production code that assumes model names will never change?

    1. You will need to update it when models are deprecated or renamed
    2. The code will work forever
    3. The AI will automatically adapt
    4. Nothing changes regardless of model updates

14. What is the relationship between model size and the recommended selection strategy?

    1. Model size does not matter for selection
    2. Use the smallest model regardless of quality
    3. Always use the largest model available
    4. Start with the smallest model that reliably完成任务, then consider larger models

15. What does running mini and nano models help you find in the evaluation process?

    1. The most expensive model
    2. The cost floor while maintaining acceptable quality
    3. The fastest model for all tasks
    4. The quality ceiling for your application