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Build a GPT and Claude CLI for Less: Cost Guide

Start With a Token Budget, Not a Model Choice

A GPT and Claude command-line tool can become expensive when every command sends full project files, long chat histories, and unrestricted output requests. Before writing the CLI, estimate costs in tokens. Your monthly model cost equals input tokens multiplied by the input rate plus output tokens multiplied by the output rate. Use the current per-million-token rates from your provider dashboard, because model pricing can change.

For a concrete planning example, assume 1,000 monthly CLI invocations. If each request sends 1,200 input tokens and receives 350 output tokens, the tool consumes 1.2 million input tokens and 350,000 output tokens. At an illustrative rate of $3 per million input tokens and $15 per million output tokens, that workload costs $8.85 per month: $3.60 for input and $5.25 for output. This calculation gives you a baseline before you add agent loops, tools, or repository context.

Use the Right Model for Each CLI Command

The biggest saving usually comes from routing, not from shaving a few words from a prompt. Split commands by difficulty. Use lower-cost fast models such as Claude Haiku or available lightweight GPT options for tasks including command explanations, commit-message drafts, JSON cleanup, file naming, and simple shell error summaries. Route normal coding questions, code review, and multi-file edits to Claude Sonnet or a capable GPT model. Reserve Claude Opus for difficult architecture decisions, subtle debugging, and high-risk refactors where a stronger answer can prevent expensive retries.

For example, let 950 requests use the $3/$15 illustrative model above, while 50 complex requests use an illustrative $15 per million input and $75 per million output model. If complex requests average 5,000 input and 1,000 output tokens, the routed workload costs about $16.35 monthly. Sending every request to the expensive model would cost about $49.54 under the same assumptions. That is roughly a 67% reduction without lowering quality for the work that actually needs the strongest model.

Keep Context Small and Intentional

CLI tools often waste more money on context than on answers. Do not automatically attach an entire repository, terminal transcript, or previous conversation. Send the current file, the relevant error output, and a compact task description first. Set practical limits: include no more than 80 lines of logs by default, truncate files to the relevant function or 300 lines, and cap initial output at 300 to 600 tokens for routine commands.

When a user needs a follow-up, summarize older context into 150 to 300 tokens instead of replaying several complete responses. A simple local cache also helps: cache deterministic requests such as explain-this-error, generate-gitignore, or format-this-JSON for 24 hours. Cache keys should include the command name, normalized input, model name, and tool version so stale results do not create confusing behavior.

Prevent Retry Loops and Surprise Output Costs

Agent-style CLI workflows can multiply spending quickly. Put a hard ceiling on iterations. A sensible default is three model-tool cycles, with an explicit --max-steps option for advanced users. Set a per-command token ceiling, such as 4,000 total output tokens, and stop with a useful message when the budget is reached. Ask the model to return structured, concise results: a short diagnosis, numbered actions, and a patch only when requested.

Also validate tool inputs before making an API call. If a required file path is missing or a shell command fails locally, show that error directly instead of asking a model to diagnose incomplete data. Log estimated input tokens, output tokens, selected model, latency, and command name. A weekly report quickly reveals whether one command or one oversized prompt is driving most of the bill.

Configure a Compatible, Pay-as-You-Go API Layer

59API is a practical choice for a cost-conscious CLI because it provides pay-as-you-go access to Claude models including Opus, Sonnet, Haiku, and Fable, plus GPT models, through one relay. Configure your client with the API base URL https://api.59api.com, store the API key in an environment variable rather than source control, and use the model IDs listed in the 59API dashboard. Its compatibility with OpenAI SDK workflows, Claude Code, and Codex means you can keep a familiar client architecture instead of maintaining separate integrations for each model family.

For developers who need native official-quality model behavior without a downgrade, 59API can be especially attractive as a low-cost relay. Check its current model rates before setting production budgets, then apply the routing rules above. If you are building the tool now, consider signing up for 59API and use its referral rebate to reduce ongoing experimentation costs.

Ship With Cost Controls Enabled by Default

Add a --model option, but make automatic routing the default. Display a dry-run estimate before large repository operations, provide a --budget-usd limit for batch jobs, and require confirmation when a command exceeds that limit. These small controls make your CLI predictable for individual developers and safe for teams. The result is a tool that uses GPT and Claude where they add value, while keeping token usage measurable, intentional, and affordable.

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