$5 Chatbot Budget: Sonnet or GPT-6.1 Sol?

01 / Give the chatbot $5 before giving it a favorite

With a tiny budget, choosing between Claude Sonnet 5.5 and GPT-6.1 Sol starts with a spending plan. Set aside $1 to test each model and keep $3 for the winner. That $5 is a suggested experiment budget, not a quoted package price or a promise about how many messages you will get.

Neither model earns the job through its name. Your chatbot needs useful answers at a cost you can sustain. Start with one narrow task, such as answering questions about a small shop’s delivery policy. A general assistant is harder to evaluate because almost any answer can seem acceptable.

02 / Check four token prices before sending a message

Look up the current input, output, cache-read, and cache-write prices for both models. Record the pricing units as well: a rate per million tokens must be divided by 1,000,000 when calculating a small request. Do not assume that the model with cheaper input produces the cheaper conversation.

CheapRelay provides access to both through one key, with no monthly fee or subscription. Billing follows real token usage, including cache reads and writes. That makes a small comparison practical, but cached context still needs a place in your calculations.

For an uncached request, estimate cost as input tokens multiplied by the input rate, plus output tokens multiplied by the output rate, using matching units. When caching applies, account for the relevant token categories at their respective rates. Use billing records to check your estimate against actual charges.

03 / Send the same 20 questions to both models

Write 20 questions before testing. Include 10 common customer questions, five vague requests, and five questions the chatbot should decline or send to a human. For a delivery assistant, include an ordinary shipping question, a missing order number, and a demand for a refund outside the supplied policy.

Give both models the same policy text and instructions. Ask for short answers and tell them to admit when the policy does not contain the answer. Keep the output limit equal where supported. Model behavior and available settings can differ, so record the settings you actually use.

• ■ Mark whether each answer follows the supplied policy.
• ■ Mark whether the customer would need another message to understand it.
• ■ Record token usage and actual cost.
• ■ Flag invented promises, missing details, and unnecessary length.

As a planning example, 20 requests averaging 600 input tokens and 200 output tokens would consume 12,000 input tokens and 4,000 output tokens per model. Testing both would total 24,000 input and 8,000 output tokens. These are assumed workloads, not measured results or price estimates. Check whether they fit your $1 allocation before running the batch.

04 / Count the cost of getting a usable answer

A cheap reply can become expensive if it causes retries or needs correction. Divide each model’s total test spend by its number of usable answers. Define usable beforehand: accurate, understandable, and safe to show a customer without editing.

Suppose one model costs less per reply but repeatedly invents delivery dates. It has failed this chatbot’s main requirement. If both pass, prefer the one with the lower cost per usable answer. Treat a close result cautiously: 20 questions can reveal obvious problems, but they cannot establish a universal winner.

05 / Spend the remaining $3 on realistic conversations

Use your reserve to test the selected model across several turns. Conversation history can add input tokens on every request. Keep only the history the task needs, and measure usage as conversations grow. Set an application spending limit and check charges during testing; an output limit alone does not cap total spending.

CheapRelay offers OpenAI-compatible routes under /v1 at https://api.cheapapirelay.com, so an OpenAI SDK can provide a shared integration for the comparison. Verify the exact model identifiers and supported parameters before running it. Sign up for CheapRelay when you are ready to fund the small test. Choose the model that passes your chatbot’s checks within your measured budget.