Token Efficiency
90% of your AI coding bill is paying for context you didn't need to send
Here are 10 things senior AI engineers stopped wasting tokens on:
Auto-context loading 50 files for a 30-line fix: $1.20/turn for tokens you'll never read. 80% input waste, every session
Running Opus on lint, format, and rename tasks: $0.60 for what Haiku nails at $0.02. 30x overpay on the cleanup tier
Tool call loops that re-send the full repo on every retry: 5x context cost per agentic flow. fixing these alone cuts 30-50% of bills
Sonnet as the default model: Kimi 2.6 matches its quality on most coding tasks at 1/6 the cost. defaulting to Sonnet in 2026 is leaving 60-70% on the table
Streaming responses on stable-prefix workflows: kills your prompt cache. you pay 10x for tokens that should have cost cents
"Just in case" file includes: 80,000-token prompts that should be 3,000. context bloat is the silent budget killer
Per-session knowledge rebuilding: 10 min writing a SKILL.md once vs paying agents to re-figure out your environment every run. $4 vs $0.30 per execution
Single-model setups: premium tier on every task is the most expensive mistake in AI coding right now
Asking 10 small questions one at a time: 10 separate input prefix charges vs one batched call. 70-90% savings on routine workflows
Buying Claude Pro + ChatGPT Plus + Cursor Pro: you seriously use one. the other two are habit, not utility
what actually compounds instead:
- context discipline (grep before fetching, always)
- prompt caching on every stable prefix
- multi-model routing (Kimi 2.6 default, Opus for the 10%)
- graduated skills via SKILL.md files
- profiling tool calls before optimizing prompts
- the routing mindset (right model for right task)
in 12 months, the gap between developers shipping on $200/month and $4,000/month budgets won't be skill
it'll be how well they route
study this.