Tess AI offers its users a multitude of combinations of IAS models, formats and different qualities. There is, therefore, one element that will be necessary for all the functionalities that use our Intelligence within the platform. We're talking about the credits system, which is what allows us to create images, videos, chat with Tess and much more.
How do credits work?
Credits are like coins. Any content generated by Artificial Intelligence has a cost, and credits are the simplest way to quantify it. As such, the cost of credits is directly related to the configuration of the content you generate. The more advanced, larger and more labor-intensive (in terms of AI work) your content is, the more credits it will consume.
In these two images, for example, we used the same prompt and the same settings, but by changing the Alchemy option (which usually optimizes the results) to active, the credit consumption increased from 3 to 4.
What are Tokens and How Do They Work?
When we talk about text AIs, Tokens help us calculate how much credit will be used. The relationship between Tokens and Credits is linked to the size of your text. The longer the text, the more tokens there are, which will later be converted into credits.
The count of these tokens varies according to OpenIA tokenization. It's difficult to guarantee a standard, as this issue involves languages, AI models and other factors, but we can base ourselves on the following count:
English: 1 word ≈ 1.3 tokens
Spanish: 1 word ≈ 2 tokens
Portuguese: 1 word ≈ 2 tokens
The relationship between the two
In Tess templates, for example, the credit count is related to the tokens used, according to the calculation above. The higher the number of tokens, the more credits need to be spent.
In practice, templates spend credits for execution and size at the same time, according to the tokens.