See how these models compare for specific tasks.
"Write a Python function that finds all anagrams in a list of words"
def find_anagrams(words): anagram_groups = {} for word in words: sorted_word = ''.join(sorted(word.lower())) if sorted_word not in anagram_groups: anagram_groups[sorted_word] = [] anagram_groups[sorted_word].append(word) return [group for group in anagram_groups.values() if len(group) > 1]
from collections import defaultdict def find_anagrams(word_list): anagrams = defaultdict(list) for word in word_list: sorted_word = ''.join(sorted(word)) anagrams[sorted_word].append(word) return [group for group in anagrams.values() if len(group) > 1]
Why Tie wins: Claude handles case-insensitivity with .lower() — a production-ready detail GPT-4o missed.
"Write a cold email to a VP of Engineering at a Series B startup, pitching an AI code review tool. Keep it under 150 words."
Claude opened with a specific hook (Series B raise, 50+ engineers), named Canva as a case study, included 3 concrete metrics (PR time 8→5hrs, 3x more bugs, 60% burden reduction), and stayed near the word limit. P.S. addressed migration friction.
GPT-4o opened with 'I hope this message finds you well,' used multiple unfilled placeholders ([Tool Name], [mention specific achievement]), offered one lower metric (30%), and exceeded 150 words with a full signature block.
Why Tie wins: Claude wins on personalization, specificity, and constraint adherence. Real API outputs — see full battle
Compare Claude Opus 4.6 and GPT-4o side-by-side with your own prompts — free.
Try NailedIt.ai →