I don't believe that is the model that you used. I wrote a script and pounded 01 mini and gpt 4 with a wide vareity of tempature and top_p parameters, and was

I don't believe that is the model that you used. I wrote a script and pounded 01 mini and gpt 4 with a wide vareity of tempature and top_p parameters, and was unable to get it to give the wrong answer a single time. Just a whole bunch of: (openai-example-py3.12) <redacted>:~/code/openAiAPI$ python3 featherOrSteel.py Response 1: A 10.01-pound bag of fluffy cotton is heavier than a 9.99-pound bag of steel ingots. Response 2: A 10.01-pound bag of fluffy cotton is heavier than a 9.99-pound bag of steel ingots. Response 3: The 10.01-pound bag of fluffy cotton is heavier than the 9.99-pound bag of steel ingots. Response 4: The 10.01-pound bag of fluffy cotton is heavier than the 9.99-pound bag of steel ingots. Response 5: A 10.01-pound bag of fluffy cotton is heavier than a 9.99-pound bag of steel ingots. Response 6: The 10.01-pound bag of fluffy cotton is heavier than the 9.99-pound bag of steel ingots. Response 7: The 10.01-pound bag of fluffy cotton is heavier than the 9.99-pound bag of steel ingots. Response 8: The 10.01-pound bag of fluffy cotton is heavier than the 9.99-pound bag of steel ingots. Response 9: The 10.01-pound bag of fluffy cotton is heavier than the 9.99-pound bag of steel ingots. Response 10: A 10.01-pound bag of fluffy cotton is heavier than a 9.99-pound bag of steel ingots. All responses collected and saved to 'responses.txt'. Script with one example set of params: import openai import time import random # Replace with your actual OpenAI API key openai.api_key = "your-api-key" # The question to be asked question = "Which is heavier, a 9.99-pound bag of steel ingots or a 10.01-pound bag of fluffy cotton?" # Number of times to ask the question num_requests = 10 responses = [] for i in range(num_requests): try: # Generate a unique context using a random number or timestamp, this is to prevent prompt caching random_context = f"Request ID: {random.randint(1, 100000)} Timestamp: {time.time()}" # Call the Chat API with the random context added response = openai.ChatCompletion.create( model="gpt-4o-2024-08-06", messages=[ {"role": "system", "content": f"You are a creative and imaginative assistant. {random_context}"}, {"role": "user", "content": question} ], temperature=2.0, top_p=0.5, max_tokens=100, frequency_penalty=0.0, presence_penalty=0.0 ) # Extract and store the response text answer = response.choices[0].message["content"].strip() responses.append(answer) # Print progress print(f"Response {i+1}: {answer}") # Optional delay to avoid hitting rate limits time.sleep(1) except Exception as e: print(f"An error occurred on iteration {i+1}: {e}") # Save responses to a file for analysis with open("responses.txt", "w", encoding="utf-8") as file: file.write("\n".join(responses)) print("All responses collected and saved to 'responses.txt'.")