Why coding assistants are expensive to run
Coding assistants are the highest-frequency AI tool — developers trigger completions hundreds of times per day. Most completions are simple (closing brackets, variable names, boilerplate) but some require deep reasoning (architecture, debugging, refactoring).
The core challenge
Autocomplete needs low latency and happens constantly. Complex tasks (multi-file refactoring, bug diagnosis) need flagship reasoning but happen 10-20x less frequently.
How hybrid routing solves this
Route autocomplete and simple completions through value-tier (fast, cheap). Route architecture questions, debugging, and code review through A-tier (accurate, thorough). Typical savings: 60-75% because 80%+ of coding assistant tokens are simple completions. Coding assistants benefit from Claude-class quality on complex logic while routing simpler completions to value-tier models.
Cost comparison at scale
| Approach | Monthly cost (est.) | Quality |
|---|---|---|
| All-flagship (GPT-4o / Claude Sonnet) | $20,000-35,000 | Highest on every turn |
| All-economy (GPT-4o-mini / Haiku) | Low | Inconsistent on critical turns |
| Token Landing hybrid | $5,000-10,000 | High where users notice |
See full pricing comparison table for per-token costs across providers.
Getting started
Token Landing's API is OpenAI-compatible — migration is a base-URL swap. Define your routing policy (which endpoints get A-tier vs value-tier), set a quality floor, and start saving.