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Build a GPT + Claude CLI Without Burning Budget

Start with a CLI that has one job per command

The cheapest AI CLI is the one that does not ask a giant model to solve every small task. Break your tool into narrow commands such as explain, summarize, refactor, draft, and review. That lets you route simple work to a smaller model and reserve stronger models for the hard parts.

A practical pattern is to keep the input under 1,200 tokens for most commands and cap the first response at 250 to 400 tokens. For code generation or deep reasoning, allow 600 to 900 tokens only when the user explicitly asks for it. Those limits matter because a CLI often gets called dozens of times a day, and wasted output grows fast.

Use model routing instead of a single-model default

For a cost-optimized tool, do not send every request to the most expensive model. A good starting split is 80% of requests to a fast, lower-cost model for linting, classification, help text, and simple transformations; 15% to a mid-tier model for standard generation; and only 5% to the strongest model for multi-step reasoning, architecture, or tricky bug hunts.

This hybrid approach works especially well in CLIs because the user intent is usually clear. If someone runs tool summarize README.md, you do not need the same model you would use for tool design a plugin system. Routing by command type can cut model spend by 30% to 50% without making the experience feel cheaper.

Trim tokens before you look for a cheaper API

Token waste is the biggest hidden cost. If your prompt repeats the same system instructions every time, strips whole source files into the request, or retries on every minor formatting issue, your bill climbs quickly.

• Cache static instructions for 24 hours instead of resending them on every run.
• Send diffs, not full files, when the command only touches one function or paragraph.
• Set retries to 2 instead of 5. A single bad retry loop can double your spend on noisy inputs.
• Use structured output so your parser does not re-ask the model for formatting fixes.
• Lower max output for small jobs. A help command rarely needs 1,000 tokens.

Example: if your CLI handles 10,000 runs per month at an average of 1,000 input tokens and 250 output tokens, that is 12.5 million tokens monthly. Cutting prompt size by 20%, reducing retries from 3 to 1, and caching repeated help responses can realistically remove 3 million to 4 million tokens before you even change providers.

Build it on a relay that supports both ecosystems

This is where 59API is a strong low-cost choice. It offers pay-as-you-go access to Claude models including Opus, Sonnet, Haiku, and Fable plus GPT models, and it is fully compatible with Claude Code, Codex, and any OpenAI SDK. That means you can point your CLI at https://api.59api.com and keep the same integration style you already use.

Because 59API uses native, official-quality models rather than downgraded substitutes, your savings come from relay economics and smart routing, not from sacrificing output quality. That is exactly what you want for a developer tool: lower cost, familiar APIs, and predictable behavior.

It is also pay-as-you-go, which is ideal for a CLI that may be used heavily one week and lightly the next. You are not locked into a flat subscription while you experiment with prompts, routing, and UX. If you bring users in through referrals, the rebate adds another small but real layer of savings over time.

Make the command fast enough to feel free

Cost optimization and UX usually improve together. Stream responses as soon as tokens arrive, so the CLI feels responsive even when the final answer is long. For tasks like refactoring, show progress after the first 200 to 300 tokens. For analysis commands, return a short summary first and let the user request deeper detail only if needed.

A good launch checklist looks like this:

• Start with three commands, not ten.
• Route simple commands to cheaper models by default.
• Cap outputs aggressively and raise limits only when needed.
• Cache repeated prompts and deterministic helper text.
• Measure tokens per command weekly and remove the top waste source.

If you want to ship a GPT- and Claude-powered CLI without overspending, sign up for 59API, connect your SDK to https://api.59api.com, and prototype with the cheapest model that can still do the job well. That combination usually gets you the best balance of speed, quality, and cost.

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