Tip: Copy the text below (including the header row) into a text file, save it as llm_comparison.csv, then import into your spreadsheet application.

"Model Name","Provider","Architecture/Model Family","Model Size (Parameters)","Context Window/Token Limit","Primary Use Case (Chat, Summaries, etc.)","Pricing Model (Free, Pay-as-You-Go, etc.)","Fine-Tuning Availability","Latency/Speed","Quality of Outputs (Benchmarks)","Data Privacy & Security","License (Open Source, Commercial, etc.)","Additional Notes"
"","","","","","","","","","","","",""
"","","","","","","","","","","","",""
"","","","","","","","","","","","",""

Column Explanations

1. Model Name
   • E.g., “GPT-4,” “Llama 2,” “Claude 2,” etc.
2. Provider
   • The company or organization offering the model (OpenAI, Meta, Anthropic, etc.).
3. Architecture/Model Family
   • For example, “Transformer-based,” “GPT series,” or “Llama family.”
4. Model Size (Parameters)
   • Approximate parameter count (e.g., 7B, 13B, 70B). Useful if publicly known.
5. Context Window/Token Limit
   • The maximum tokens or character length the model can handle in one prompt (e.g., 8k, 32k tokens).
6. Primary Use Case
   • Short note on what the model is best at (e.g., general conversation, coding help, summarization).
7. Pricing Model
   • Whether it’s free to use, subscription-based, pay-per-token, or self-hosted with infrastructure costs.
8. Fine-Tuning Availability
   • Can you fine-tune/customize the model? Is it accessible to the public, or restricted?
9. Latency/Speed
   • Any notes on how quickly the model responds or hardware requirements if self-hosted.
10. Quality of Outputs (Benchmarks)

• Include known performance metrics or personal impressions of writing style/accuracy.

1. Data Privacy & Security

• Summaries of how the model handles user data. For example, is data stored, encrypted, or used to re-train?

1. License (Open Source, Commercial, etc.)

• Note if the model is licensed under MIT, Apache, or if usage is restricted.

1. Additional Notes