Why Microsoft Uses Credits Instead of Tokens
By hermestrismegistus369 – Tech enthusiast and AI tinkerer. This post was crafted with the brilliant assistance of M365 Copilot GPT5 . Full credit to Copilot for this distill. I am the editor.
Last night I had a great discussion with some Microsoft colleagues around Copilot — credits vs. tokens, in particular one of my favorite Senior Solutions Engineer @ Microsoft | Conversational AI and Hyperautomation. We discussed that fact the AI Industry measures AI throughput (Granular level) in “Tokens” and Microsoft has chosen to measure AI throughput in (Abstracted level) in “Credits”.
At first I really questioned Microsoft approach as why would you not measure the way the industry does? Copilot answered my question — shared below.
Using “Credits” captures of a unit of work to be completed which most everyone would understand while “Tokens” is too fined grained (unless your are a technical) and this abstraction layer should simplify things for non-technical folks.
Makes sense now that I understand the reasoning.
📊 AI Credits vs Tokens: Comparison Chart
🔄 How Credits Convert to Tokens (Microsoft)
Microsoft does not publish a fixed conversion rate between credits and tokens. However, based on internal documentation and usage patterns:
1 AI Credit typically corresponds to a single AI-powered action, such as:
Summarizing an email inbox
Rewriting a paragraph
Generating a table in Excel
Editing an image in Designer
Each of these actions may consume hundreds to thousands of tokens behind the scenes, depending on:
The complexity of the prompt
The model used (e.g., GPT-4 vs GPT-4 Turbo)
Whether images or documents are involved (which are converted into tokens) 1
For example:
A simple text generation task might use ~500–1,000 tokens.
A document summarization could use ~2,000–4,000 tokens.
An image generation task might consume even more due to higher compute costs.
🧠 Why Microsoft Uses Credits
Microsoft’s credit system is designed to:
Simplify billing for non-technical users.
Unify usage across diverse apps (Word, Excel, Outlook, Designer, Bing, etc.).
Abstract model complexity so users don’t need to understand tokenization or model pricing.
Microsoft uses “Credits” instead of “Tokens” to measure AI consumption workloads primarily for user experience clarity and billing simplicity. Here’s a breakdown of why:
🧠 1. Tokens vs. Credits: What’s the Difference?
Tokens are a technical unit used by AI models (like GPT-4) to measure how much text is processed. They’re granular and precise but not intuitive for most users.
Credits, on the other hand, are abstracted units that represent a bundle of AI usage—like generating a document, editing an image, or summarizing an email. They’re easier to understand and manage from a consumer and enterprise billing perspective.
💡 2. Why Microsoft Chooses “Credits”
Simplified User Experience: Most Microsoft 365 users aren’t developers or AI engineers. Credits offer a clearer way to track usage across apps like Word, Excel, Outlook, and Designer.
Cross-App Consistency: Credits unify AI usage across multiple services (Copilot, Bing Image Creator, Windows Notepad, etc.), making it easier to manage quotas and limits.
Subscription Alignment: Credits are tied to Microsoft 365 plans (e.g., Personal gets 60/month), making it easier to bundle AI features with existing subscriptions.
Cost Abstraction: Behind the scenes, Microsoft still calculates costs based on tokens and compute time. But presenting it as “credits” helps abstract the complexity for end users.
🧾 3. Industry Comparison
OpenAI, Anthropic, and others use tokens because their platforms are geared toward developers and technical users who need fine-grained control.
Microsoft targets a broader productivity audience, so credits are more aligned with task-based billing rather than raw data throughput.