A token is often described as representing around three-quarters of a word. Thus, giving an LLM a text of 10,000 words to examine would equate to 12,000-13,000 tokens of content. In developer terms, if a body of code which Copilot were to examine (for refactoring or bug-hunting for example), comprised of 10,000 ‘words’ (expressions, statements, variable names, functions, and so on), then that using it in one query, once, would count as 12,000-13,000 tokens out of their allottment for the month.
Prompt text, as inputs, will also count, as will the outputs from Copilot.
The pricing tiers coming into effect next month remain pegged at their current levels, but instead of being allotted a number of queries per month, users are given ‘AI Credits’ to the same value. A base-tier Copilot Pro subscriber ($10pcm) will receive 1,000 credits, with GitHub saying that at present one AI Credit is worth one US cent.
The number of tokens each credit buys will depend on the model used, the input/output mix, the size of the cache (data held in the LLM’s memory for context), and feature requested. Thus, if a developer uses mostly simple queries, they are likely not to have to buy extra tokens in the form of credits each month. Conversely, multi-agent queries about a complex, lengthy code base will empty the AI Credit account more quickly. Queries to the most-advanced frontier models will cost more than to the less-powerful.
GitHub’s pricing changes do include some compensatory benefits for users: Code completions (similar to a phone’s auto-complete function) and Next Edit suggestions will remain free.
