我如何削減大語言模型 API 開支 40%，並建立免費成本計算器
How I Cut My LLM API Bills by 40% (And Built a Free Cost Calculator)
作者透過多模型任務路由，將大語言模型 API 開支降低近 40%，並建立免費的前端成本計算器。計算器支援英中切換，涵蓋 Claude-Max、Azure-GPT、Gemini 及主要中國模型。作者把佔 Agent 呼叫量 70% 的簡單任務交由較便宜的模型處理，並在信心不足時才升級至 Claude。
If you are building anything with LLMs right now, you know the pain. You start with OpenAI's GPT-4, everything works great, and then you look at your monthly bill and realize your side project is bleeding money.
I recently started building a multi-model workflow. I use Claude for complex reasoning and long-context tasks, GPT for general coding, and DeepSeek for bulk text processing.
Here is the problem: The cost observability is a nightmare.
Every time I tweaked the routing logic, my monthly bill changed in ways I couldn't predict. Because different providers charge differently (per 1M tokens vs 1K, input vs output, cached vs uncached), I found myself opening 3 different pricing tabs and doing mental math until 2 AM.
Step 1: Building the Calculator
To stop guessing, I decided to build a simple, frontend-only cost calculator. I wanted something fast, no email wall, and no sign-up. Just plug in your token volume, pick a model, and see the real cost gap.
(You can try it here for free: llm-api-cost-calculator.vercel.app)
I built it with plain HTML/JS so it loads instantly. It supports bilingual (EN/CN) switching, and covers models like Claude-Max, Azure-GPT, Gemini, and major domestic Chinese models.
Step 2: The "Task Routing" Strategy
Once I could quantify the costs, I realized I was wasting money using expensive models for easy tasks. Here is the routing logic I implemented:
- Tier 1 (High Cost, High Intelligence): Claude-Max or Azure-GPT. Used ONLY for architecture planning, complex debugging, and long-context refactoring.
- Tier 2 (Balanced): Gemini-Ultra or GPT-4o. Used for standard code generation and UI logic.
- Tier 3 (Low Cost, High Speed): Gemini-Flash or domestic models like DeepSeek. Used for bulk classification, summarizing, and simple regex generation.
By routing the "dumb" tasks (which make up 70% of my agent's calls) to cheaper models, and only escalating to Claude when confidence is low, my bills dropped by nearly 40%.
Step 3: Managing the Infrastructure
Managing 3 different API keys, billing dashboards, and rate limits is incredibly annoying. I eventually moved to using an API aggregator (gateway) to unify the endpoints.
Using a unified endpoint means I just send one OpenAI-compatible request, and the gateway handles the routing and automatic failover. If Claude goes down or hits a rate limit, it instantly falls back to GPT.
(If you want to test a unified multi-model API with zero downtime, you can grab some free credits on my platform here: APiZ Registration)
Conclusion
The days of just using one expensive model are over. The real skill in 2026 is cost-aware routing.
If you want to know exactly how much you could be saving, go run your numbers on the calculator:
👉 Free LLM API Cost Calculator
How are you guys handling multi-model costs in production right now? Let me know in the comments!
來源：DEV Community · dev.to