Premium models are easier to evaluate affordably when you treat the experiment as a small, bounded sprint. Choose one useful task, control the amount of text you send, and decide what a better answer is worth before you start. This walkthrough takes you from an empty test sheet to a practical decision without turning exploration into an open-ended expense.
1. Set a spending ceiling before your first request
Choose an amount you are comfortable spending on the entire experiment. Split it into an initial test allowance and a reserve for checking promising results. A simple split is to use half for exploration and keep half for confirmation.
Sign up for SwiftRelay to start with the small gift credit available to new accounts. SwiftRelay bills per token, so track actual usage rather than treating your account balance as a target to exhaust. Check current model rates before sending requests; input and output can have different prices.
2. Pick one task with a checkable result
A broad instruction such as “help with my project” makes both cost and quality difficult to judge. Pick a task you already understand: explain a failing test, rewrite an awkward customer reply, or extract required fields from a document.
- Input: Prepare one short, representative example with sensitive information removed.
- Expected result: Write down what a correct answer must include.
- Failure conditions: Identify unacceptable outcomes, such as invented facts or a patch that changes unrelated behavior.
Keep the input fixed across the first round. Otherwise, you will not know whether an improvement came from the model or from a different prompt.
3. Connect one client without rebuilding your workflow
SwiftRelay works with Claude Code, Codex, Gemini CLI, and OpenAI-compatible SDKs. Use the client you already know instead of adding another tool just for this trial.
For an OpenAI-compatible SDK, configure the base URL as https://api.fastapirelay.com/v1. Claude Code and Gemini CLI take the bare host, https://api.fastapirelay.com. Follow your client's configuration instructions for authentication, and keep your API key in an environment variable or another appropriate secret store.
Start with a single short request to confirm that authentication and model selection work. Avoid launching an agent across a whole repository before checking this basic connection.
4. Test one premium candidate against a lighter baseline
Choose Claude Sonnet 5.5 as your premium candidate and Gemini 2.5 Flash as a lighter baseline, after checking their current rates and availability. The purpose is not to declare a universal winner. It is to discover whether the premium candidate improves your specific task enough to justify its cost.
Send the same task instructions and source material to each. Ask for the same response format and a similar output limit. Record the model, prompt version, token usage, billed cost, and whether the answer passed your checklist.
5. Cut unnecessary tokens before repeating the trial
Review what you actually sent. Remove repeated instructions, irrelevant files, old conversation history, and examples that do not help the task. For coding requests, supply the relevant function and error message before considering a larger repository context.
Set an output limit appropriate to the job. Ask for a focused patch or a short explanation when that is all you need. Keep enough room for a complete answer: a truncated response can waste the request and require another attempt. Where your client permits it, bound agent turns and tool calls too.
6. Spend the reserve on confirmation, then stop
Use your remaining allowance to test the promising setup on a few fresh examples. Check correctness, missing details, and the editing effort required before the result is usable. Treat a successful first answer as a lead, not proof.
Finish with a simple routing rule: use the lower-cost option for tasks it handles reliably, and reserve the premium model for work where its improvement matters. Save the successful prompt and spending record. Your next experiment can then begin with evidence instead of another unstructured round of requests.