Why Token Efficiency Is the New Page Speed

In 2010, Google made page speed a ranking factor. Slow websites got penalized. The entire web got faster as a result.

In 2026, we need the same thing for APIs. Not for humans, but for AI agents. The metric that matters is not page speed. It is token efficiency.

The problem

When an AI agent calls an API, every byte of the response gets converted to tokens. Tokens cost money. Tokens take time to process. Tokens fill up context windows.

Here's what we found scanning 200 tools:

Tool	Avg response	~Tokens	Problem
Notion (get page)	47 KB	~12,000	No field selection, deeply nested JSON
Slack (channel history)	28 KB	~7,000	Returns full user objects inline
Stripe (list charges)	4 KB	~1,000	expand[] reduces N+1 but no field selection
Resend (send email)	0.3 KB	~80	Minimal, focused responses

Notion's API returns 150x more tokens than Resend's for a comparable operation. That's not a rounding error. That's the difference between an agent that costs $0.001 per task and one that costs $0.15.

What makes an API token-efficient

Field selection: Let clients request only the fields they need. GraphQL gets this right. REST APIs can add ?fields=id,name,status.
Compact responses: Don't nest the same data repeatedly. Use IDs with a separate expansion mechanism.
Batch operations: Let agents create/update/delete multiple items in one request instead of N requests.
Pagination with cursors: Cursor-based pagination is more efficient than offset-based for agents that process items sequentially.
Conditional requests: Support ETags and If-Modified-Since so agents skip unchanged data.

The business case

If you build APIs, token efficiency is becoming a competitive advantage. Agent-native tools that optimize for token usage will get chosen by agents over bloated alternatives.

Check your tool's token efficiency score on agenttool.sh. If you score below 7, you have low-hanging improvements that will make agents prefer your tool.