Which AI Has The Best Memory? - Undercode Testing

The ability of AI models to retain and process large amounts of information is determined by their context window—the number of tokens (words or characters) they can handle in a single input. Recently, advancements like GPT-4.1 have introduced a 1 million-token context window, significantly improving long-context retention.

For more details, check NetworkChuck’s video:

👉 https://lnkd.in/dgrpEXxQ

You Should Know:

1. Testing AI Memory with Code

To evaluate an AI’s memory, you can use Python and OpenAI’s API to test context retention:

import openai

response = openai.ChatCompletion.create(
model="gpt-4",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Summarize a 5000-word document in 100 words."}
],
max_tokens=1000
)
print(response.choices[bash].message['content'])

2. Measuring Context Window in Linux

Use `curl` and `jq` to test API-based AI models:

curl https://api.openai.com/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_API_KEY" \
-d '{
"model": "gpt-4",
"messages": [{"role": "user", "content": "Explain context windows in AI."}],
"max_tokens": 150
}' | jq '.choices[bash].message.content'

3. Comparing AI Models

Use `diff` in Linux to compare outputs from different AI models:

echo "AI Memory Test" > gpt3.txt 
echo "AI Memory Test" > gpt4.txt 
diff gpt3.txt gpt4.txt

4. Windows PowerShell AI Testing

Check AI response times using PowerShell:

Invoke-RestMethod -Uri "https://api.openai.com/v1/chat/completions" `
-Method POST `
-Headers @{"Authorization"="Bearer YOUR_API_KEY"} `
-Body '{"model":"gpt-4","messages":[{"role":"user","content":"What is a context window?"}]}' | Select-Object -ExpandProperty choices

5. Monitoring AI Performance

Use `htop` in Linux to monitor API call resource usage:

sudo apt install htop -y && htop

What Undercode Say:

AI memory advancements, like 1M-token context windows, revolutionize long-document processing. However, testing with Python, Bash, and PowerShell ensures optimal performance. Always verify API responses, compare models, and monitor system resources when deploying AI solutions.