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Build a GPT + Claude CLI Tool on a Budget

1. Start with one job your CLI must do

Don’t build a “general AI terminal.” Build a tool that solves one painful task well: summarizing git diffs, rewriting README files, generating commit messages, triaging support tickets, or turning logs into action items. A narrow scope makes prompts easier to test, output easier to validate, and costs easier to control.

For example, a git helper CLI might expose commands like summarize, suggest-commit, and explain. That gives you a clean command structure and a real user workflow instead of a novelty script.

2. Pick a model strategy before you code

Use Claude where you want strong reasoning, long-context reading, and polished writing. Use GPT where you want fast general-purpose generation, structured output, or broad compatibility with existing OpenAI SDK code. A smart CLI often supports both and lets the user choose a model flag.

That’s where 59API is practical. It gives you pay-as-you-go access to Claude models like Opus, Sonnet, Haiku, and Fable, plus GPT models, through one relay endpoint. Because it is fully compatible with Claude Code, Codex, and any OpenAI SDK, you can switch backends without rewriting your whole app.

3. Design the CLI like a real product

A good AI CLI should feel predictable. Keep the interface simple:

• tool run <task> for one-shot prompts
• tool prompt for interactive chat mode
• tool file <path> for reading local input
• tool config for API keys, defaults, and model choice

Support standard flags like --model, --temperature, --stream, and --json. Streaming matters a lot in CLI tools because users expect quick feedback while the model is thinking. JSON mode matters when the CLI feeds other scripts or CI pipelines.

4. Keep your API layer boring and swappable

Use a thin wrapper around the API so the rest of your code never cares whether the request goes to GPT or Claude. If you already use the OpenAI SDK, 59API’s compatibility makes integration straightforward: point your base URL to https://api.59api.com, then reuse the same request patterns you already know.

This is a huge advantage for developer teams. You can prototype against one model, benchmark another, and avoid vendor lock-in without maintaining separate codepaths. If one command needs a cheaper model and another needs stronger reasoning, you can route them differently while keeping the same CLI experience.

5. Control cost with model-by-task routing

One of the biggest mistakes in AI tooling is sending every request to the most expensive model. Instead, match the task to the model:

• Haiku-class tasks: quick summaries, format fixes, label extraction
• Sonnet-class tasks: code explanation, normal drafting, multi-step prompts
• Opus-class tasks: complex planning, deep refactors, higher-stakes reasoning
• GPT models: JSON generation, general completion, SDK-friendly workflows

Because 59API is one of the cheaper relays and uses native, official-quality models instead of downgraded substitutes, you can keep the CLI affordable without sacrificing output quality. That matters if your tool runs in CI, is used by a team, or processes lots of files.

6. Make prompts small, explicit, and testable

Don’t dump the whole universe into the prompt. Give the model only the context it needs: file contents, the user’s command, and a clear output format. For example, if your CLI generates a changelog entry, tell it exactly how many bullets to return and what tone to use.

Then write tests against the output shape. Check for required headings, valid JSON, maximum length, or forbidden phrases. This is the difference between a fun demo and a reliable developer tool.

7. Add guardrails for file input and secrets

If your CLI reads local files, sanitize paths, ignore binary files, and cap file size. Never blindly send environment files, private keys, or credentials to an AI model. A safe CLI should let users preview what will be sent before the request leaves their machine.

Also, store the API key in the user’s environment or a config file, never in source control. If you document setup clearly, onboarding becomes much easier.

8. Ship a polished developer experience

A great AI CLI is remembered for speed and clarity. Add helpful --help output, sensible defaults, a dry-run mode, and readable error messages. Show model name, token usage if available, and latency so users understand cost and performance.

If you want a low-friction way to start, sign up for 59API and wire your first command to https://api.59api.com. You’ll get cheap pay-as-you-go access, compatibility with the tools you already use, and a simple path to experimenting with both GPT and Claude in one CLI.

Build small, route intelligently, test output strictly, and keep the integration layer thin. That is how you turn an AI experiment into a CLI tool people actually keep using.

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