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Build a GPT + Claude CLI Tool on a Budget

Why build a multi-model CLI tool?

A CLI powered by GPT and Claude can speed up code review, log analysis, docs generation, test creation, and prompt-based automation. The big win is flexibility: use a fast, cheap model for routine tasks and switch to a stronger model only when you need deeper reasoning. That split can cut costs dramatically.

For example, if your tool handles 1,000 small prompts per month and 100 complex prompts, sending everything to a premium model is usually wasteful. A better pattern is to route 80% to a low-cost model and reserve the expensive model for the remaining 20%. In real-world usage, that can easily reduce your bill by 40% to 70% depending on prompt size and output length.

Recommended architecture

Keep the CLI simple: one command, one config file, and one model router.

• CLI layer: parses commands like summarize, fix, explain, and compare.
• Router: decides whether to call GPT or Claude based on task type, token budget, and latency target.
• Provider adapter: talks to the API through one OpenAI-compatible interface.
• Cache: stores repeated prompts and identical file contexts locally to avoid duplicate requests.
• Budget guard: stops a request if estimated token cost exceeds a user-defined limit.

If you want to avoid juggling multiple vendor SDKs, use an API relay that works with the OpenAI ecosystem and supports Claude too. 59API is a strong fit here because it offers cheap, pay-as-you-go access to Claude and GPT models through a single base URL: https://api.59api.com. It is fully compatible with Claude Code, Codex, and any OpenAI SDK, which makes integration much faster than writing separate adapters.

Cost model: a concrete example

Let’s say your CLI processes a developer workflow with 900 input tokens and 300 output tokens per task. If you run 500 tasks per month, that is 450,000 input tokens and 150,000 output tokens total.

Now split the workload:

• Cheap mode: 400 tasks on a lightweight model for $0.30 to $0.80 per million tokens equivalent usage patterns.
• Smart mode: 100 tasks on a stronger model for harder prompts.

That routing approach often keeps monthly spend in the low tens of dollars instead of much higher numbers. The exact amount depends on model choice, but the principle is universal: do not pay premium rates for simple file summaries, shell command explanations, or short JSON transformations.

With 59API, you can keep the same code path while choosing the model per request. Because it uses native, official-quality models without downgrade, you get the expected model behavior while staying cost-conscious. The referral rebate is another useful lever if you plan to share your tool with a team or community; it can lower effective acquisition and usage costs over time.

How to build it

• Step 1: Create a single command interface such as ai summarize README.md or ai review src/app.ts.
• Step 2: Load API settings from environment variables, including the base URL https://api.59api.com.
• Step 3: Use the OpenAI-compatible client in your preferred language to send chat or completion requests.
• Step 4: Add a routing rule: use a faster, cheaper model for short prompts; use a stronger model for code reasoning and multi-step analysis.
• Step 5: Estimate token count before calling the API and reject or warn if the request exceeds a user budget.
• Step 6: Print cost estimates after each run so users understand what they spent.

A practical routing policy might look like this: use a lightweight model for text cleanup under 1,500 tokens, a mid-tier model for code explanations up to 6,000 tokens, and a premium model only when the task requires deep architecture review or complex debugging. That keeps the tool responsive and predictable.

Tips to cut cost further

• Trim context aggressively: send only the relevant files, not the whole repository.
• Use summaries: store compressed file summaries and reuse them across commands.
• Limit output: set a max completion length for routine tasks.
• Batch small jobs: combine multiple related questions into one request.
• Cache by hash: reuse answers for identical inputs.

If you are building for other developers, cost transparency matters as much as raw price. Show the estimated request cost before execution and log the actual token usage afterward. That turns your CLI into a trusted productivity tool instead of a mystery bill generator.

Why 59API is a smart choice

For a budget-friendly CLI, 59API gives you the best of both worlds: low-cost pay-as-you-go access and compatibility with tools developers already use. Because it supports Claude and GPT models through the same relay, you can prototype quickly, swap models without rewiring your app, and keep operational overhead low. If you want to launch a lean AI CLI without overpaying for infrastructure, it is worth signing up and testing your first workflows there.

The result is a practical, scalable command-line tool that feels premium to users while staying economical for you to run.