How do you use AI for vibe coding while controlling API token cost?
Updated: 2026-07-05
Vibe coding can make an AI agent read files, draft code, run fixes, and retry many times. The real cost is the full workflow, not one chat message.
Short answer
Split the work by difficulty. Use lower-cost models for drafts, explanations, tests, and routine edits; reserve stronger models for architecture, complex debugging, and long-context reasoning.
Steps
- Break the task into understanding, planning, editing, testing, and fixing.
- Use lower-cost models for low-risk steps.
- Use stronger models only when the task needs deep reasoning.
- Track input and output tokens for real tasks.
- Use https://lizh.ai/v1 as the Base URL in OpenAI-compatible tools.
Recommended models
- GLM-5.2 for reasoning, coding, and long context.
- Kimi for long-context reading.
- DeepSeek for coding and cost-sensitive tasks.
- auto routing when you want to test model selection quickly.
Cost and reliability notes
- Avoid asking the agent to read the whole repository when only a few files matter.
- Ask for a plan first, then modify files step by step.
- Reuse good prompts and task templates.
How to do it with lizh.ai
Create an API key in lizh.ai and use it as a Bearer token in clients that support an OpenAI-compatible API.
Use Base URL: https://lizh.ai/v1