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A credit is a unit of usage on ZBrain. Credits are consumed whenever you perform actions such as embedding documents or querying an app. Here’s how credits are utilized:
When you upload a document, it is processed and embedded to create a searchable vector representation. This step consumes credits based on the size of the document and the embedding model used.
When you ask a question, the relevant context is retrieved from the embeddings and processed by the Large Language Model (LLM). This step incurs a credit cost based on the model and the number of input tokens.
The LLM then generates a response based on the processed input, consuming additional credits based on the number of output tokens.
The cost in credits for various models and processes is detailed below:
Model | Input Cost | Output Cost |
---|---|---|
GPT-4o | 2,500 credits / 1M tokens | 7,500 credits / 1M tokens |
GPT-4 | 15,000 credits / 1M tokens | 30,000 credits / 1M tokens |
GPT-4-32k | 30,000 credits / 1M tokens | 60,000 credits / 1M tokens |
GPT-3.5 Turbo | 250 credits / 1M tokens | 750 credits / 1M tokens |
Model | Input Cost | |
---|---|---|
text-embedding-3-small | 20 credits / 1M tokens | |
text-embedding-3-large | 130 credits / 1M tokens | |
ada v2 | 100 credits / 1M tokens |
When you create a knowledge base or query an app, credits are deducted based on the embedding, input, and output token usage. This ensures you only pay for what you use while leveraging the full power of advanced AI models.