Pick the Right Model for Your Hackathon Weekend

Start with the demo you need by Sunday

Before choosing a model, write one sentence describing what a judge or friend should be able to do with your finished project. A study assistant might turn lecture notes into five useful questions. A campus event tool might answer questions using a supplied schedule. Keep that outcome small enough to demonstrate in two minutes.

Next, separate the AI work into two categories: help you need while building, and requests your application will make during the demo. These do not have to use the same model. You are choosing tools for specific jobs, not declaring a permanent favorite.

Turn your weekend budget into a test plan

Choose an amount you can comfortably spend before making requests. If your weekend allowance is $10, you might reserve $2 for comparisons, $5 for building, and $3 for rehearsal and the live demo. These are spending envelopes, not estimates of what any particular model will cost.

With MyRelay, one person can use one key and one pay-as-you-go budget across supported models, while seeing each request's tokens and cost. Create a MyRelay account to keep your weekend experiments visible in one place. Treat your allowance as a limit you actively monitor unless you have confirmed that an automatic spending control is available and enabled.

Choose two candidates instead of browsing all night

Start with one candidate for routine work and one alternative for tasks that need more careful reasoning. For example, you could compare Gemini 2.5 Flash and Claude Sonnet 5.5 on your own workload. Treat the names as a shortlist, not a guarantee about speed, quality, or price.

Check the current pricing before testing, including both input and output charges. Also confirm that the candidate supports any feature your application actually requires. A model that answers ordinary questions well is not automatically the right choice for structured responses or tool use.

Build a tiny test set from real project inputs

Prepare five examples before you start comparing answers. Include two ordinary requests, one ambiguous request, one longer input, and one case where the correct behavior is to admit that information is missing. Use invented or public data rather than private student records or credentials.

Send the same instructions and inputs to both candidates. For each response, record whether it was correct, usable without editing, and fast enough for your demo. Note the request cost too. If an answer fails, count the retries needed to obtain something usable. A cheap first attempt can become expensive when it repeatedly needs repair.

Pick the lowest-cost option that clears your bar

Define success in plain language: the answer must use the supplied facts, follow the requested format, and avoid inventing missing details. Choose the less expensive candidate if it meets that standard consistently. Do not pay more merely because another answer sounds more polished.

If neither candidate passes, first tighten your instructions or reduce the task's scope. Asking for five questions grounded in a short passage is easier to verify than asking for a complete personalized learning system. Better task boundaries can save more money than another round of model shopping.

Escalate difficult tasks without upgrading everything

Keep routine requests on your chosen default. Use the alternative only when your tests show that it handles a particular failure better. During development, that might mean reserving it for a stubborn bug rather than every small edit. In the application, a simple validation check can identify responses that need another attempt.

Set a retry limit. Endless automatic retries can drain a small budget and still produce an unreliable result. When a request cannot be completed, a clear message is better than a loop you cannot afford.

Rehearse once, then stop changing the setup

Before the demo, run the exact user journey several times and check the recorded costs. Shorten unnecessary context, trim oversized responses, and keep a clearly identified prerecorded example available if live access fails.

Then freeze your choice. The right hackathon model is the one that completes your small, tested workflow within your budget. Spend the remaining hours making that workflow understandable, dependable, and easy to show.