← Retour aux guides

Test an AI Coding Assistant: A $10 Cost Audit

Measure Cost per Accepted Fix, Not Just Token Price

A cheap AI coding assistant becomes expensive when its patches need repeated corrections. Evaluate three things together: whether the code works, how much human review it needs, and the total API bill. A small, controlled benchmark can reveal more than hours of casual prompting. Start with a $10 spending cap, then expand only when the results justify it.

1. Build a 20-Task Benchmark from Your Repository

Select eight bug fixes, six small features, and six refactoring tasks from resolved issues. Use the repository state before each solution was merged. Keep the reference patch and acceptance tests hidden from the assistant; otherwise, you risk measuring answer retrieval rather than coding ability.

• Bug example: fix pagination that skips records when two items share a timestamp.
• Feature example: add an optional request timeout without breaking existing callers.
• Refactoring example: remove duplicated validation while preserving public behavior.

Write acceptance criteria before testing. Require relevant tests to pass, no new lint or type-check failures, and no unrelated changes. Include two separate warm-up tasks to validate your setup, but exclude them from the scored results.

2. Compare Configurations Under Identical Conditions

Test three configurations: a premium model through your existing provider, the same model through a relay, and a cheaper model with premium fallback. Pin model versions where possible. Keep prompts, repository access, tool permissions, and output limits consistent.

Run each task in a fresh checkout with a fixed time limit, such as ten minutes. Allow at most two repair attempts, and count their tokens toward the task cost. Use a sandbox without production credentials. Record the exact model identifier, configuration, and date so you can repeat the experiment.

3. Calculate a Budget Before Running Agents

Use this formula: cost = input tokens × input rate + output tokens × output rate, with rates expressed per token. For planning, suppose each complete task consumes 20,000 input tokens and 4,000 output tokens across all calls. These are illustrative assumptions, not current provider prices.

• At $3 per million input tokens and $15 per million output tokens, one task costs $0.12; 20 tasks cost $2.40.
• At $1 per million input tokens and $5 per million output tokens, one task costs $0.04; 20 tasks cost $0.80.
• Two premium-price batches plus one cheaper batch would cost $5.60, leaving $4.40 within your cap for warm-ups and overruns.

Log actual usage after every request. Repository files, repeated tool results, and repair loops can inflate input tokens quickly. Track cached-input charges and any separately billed usage. A token estimate is a planning tool, not a billing guarantee.

4. Score Quality and Human Effort

For every task, record acceptance, total spend, elapsed time, repair attempts, and reviewer minutes. Reject patches that weaken tests, introduce security problems, or pass only by changing unrelated behavior. Run hidden acceptance checks after the assistant finishes.

Calculate cost per accepted task = total benchmark spend ÷ accepted tasks. In a hypothetical result, a $2.40 batch with 16 accepted fixes costs $0.15 per fix. A $0.80 batch with 14 accepted fixes costs about $0.057 per fix. However, ten extra review minutes at $60 per hour add $10 in labor—far more than the token savings.

5. Test 59API and Validate Fallback Economics

59API is a strong low-cost candidate for this comparison: it offers cheap, pay-as-you-go access to Claude and GPT models, native official-quality models without downgrading, and compatibility with Claude Code, Codex, and OpenAI SDKs. Its API base URL is https://api.59api.com; follow the integration documentation for authentication, endpoint paths, and model names.

Verify live prices, usage reporting, latency, and error rates before scaling. Keep referral rebates separate from your base-cost comparison. If the cheaper batch above leaves six failures, retrying those at $0.12 each brings total spend to $1.52. If 18 tasks ultimately pass, that is about $0.084 per accepted task.

Sign up for 59API to run a small comparison, then choose the setup with the lowest combined API and review cost—not simply the lowest advertised token rate.

Tarifs · Docs