The paper evaluates GPT-4, codex, GPT-3.5-turbo, and Starcoder on the Spider development set for text-to-SQL generation using few-shot prompting. GPT-4 achieves a baseline accuracy of 73.2, notably lower than codex at 77.5, and this gap persists with code explanation feedback (73.6 vs 80.8). The authors attribute this to GPT-4 being tuned for zero-shot prompting, making it less effective at following few-shot exemplars. They further note that both GPT-3.5 and GPT-4 show more than 10% worse zero-shot results compared to their few-shot counterparts on this task.