ChatGPT API pricing is based on the number of tokens processed, not per request or by character count.

How Tokens Work

• A token can be a word, part of a word, or punctuation.
• Example:
  • "ChatGPT is great!" → 5 tokens
  • "Hello, world!" → 3 tokens
• Generally, 1 token ≈ 4 characters in English.

How OpenAI Charges

You are charged for:

1. Input Tokens (tokens in your request, including system messages, user input, and conversation history).
2. Output Tokens (tokens in the AI’s response).

GPT-4o Mini Pricing (Cheapest Option)

For GPT-4o Mini, the current pricing is:

• $0.15 per 1 million input tokens
• $0.60 per 1 million output tokens

So, a short conversation might only cost fractions of a cent.

Example Cost Calculation

1. Suppose a request has 100 input tokens and gets a 200-token response.
2. That’s 300 total tokens.
3. Cost breakdown:
   • Input: 100 tokens → $0.000015
   • Output: 200 tokens → $0.00012
   • Total cost: $0.000135 (less than 1 cent!)

Would you like help optimizing your script to reduce token usage and save costs? 🚀

How Big a Ollama Model is Required to Reword Questions?

To get decent rewording of questions using Ollama, you’d likely need a model that can handle natural language processing (NLP) tasks efficiently. Here are some options based on model size and capability:

Recommended Models for Rewording Questions in Ollama

1. Mistral 7B (7 billion parameters)
   • Pros: Small, fast, and efficient. Good at paraphrasing.
   • Cons: May struggle with deeper contextual understanding.
   • Best for: Basic rewording, adding variety to questions.
2. Llama 3 8B or 13B (Meta’s models)
   • Pros: More refined, good for question Restructuring.cloud/restructuring/” target=”_blank” rel=”noopener noreferrer”>restructuring.
   • Cons: Slightly more demanding on resources.
   • Best for: Keeping question intent intact while varying phrasing.
3. Mixtral 8x7B (A sparse mixture of experts model)
   • Pros: Balances efficiency and intelligence, activating only part of the model at a time.
   • Cons: Larger resource requirements than Mistral 7B.
   • Best for: More advanced sentence Restructuring.cloud/restructuring/” target=”_blank” rel=”noopener noreferrer”>restructuring.
4. Llama 3 70B (Very large but powerful)
   • Pros: Near GPT-4-level paraphrasing.
   • Cons: Heavy resource demand (needs a high-end GPU).
   • Best for: Highly intelligent rewording with nuanced variation.

Which Model Should You Use?

• If you want the fastest, cheapest option → Mistral 7B
• If you want a good balance of performance & cost → Llama 3 8B or Mixtral 8x7B
• If you need high-quality rewording with nuanced changes → Llama 3 70B (if you have the hardware for it)

Would you like help integrating one of these into your script? 🚀

Is there an Online Dictionary/Thesaurus that Python Could Use?