How should I compare cheap and premium LLMs before choosing one?

Build a small test set

• Collect representative requests from the job you actually need, including difficult and failed cases.
• Write the expected answer or a simple pass/fail rubric for each request.
• Keep private data out unless your policies allow sending it to the selected provider.

Measure what users notice

• Record correctness, format errors, latency, refusals and retries.
• Calculate cost per accepted result. A cheaper model that needs repeated attempts can cost more in practice.
• Treat public benchmarks as context, not a replacement for your own task results.

Make the decision explicit

• Use a smaller model for routine work when it meets your threshold.
• Reserve premium models for difficult requests or final review.
• Recheck the decision when prices, prompts or enabled models change.

Example

from openai import OpenAI

client = OpenAI(api_key="YOUR_TOKENREX_KEY", base_url="https://tokenrex.ai/api/public")

resp = client.chat.completions.create(
    model="claude-sonnet-5",
    messages=[{"role": "user", "content": "Return JSON with the invoice number, total and due date"}],
    max_tokens=150,
)
print(resp.choices[0].message.content)

Questions

How many test prompts do I need?

Start with 20 to 50 representative requests, then expand around failures.

Can I compare models with one key?

Yes. Use the same TokenRex key and change only the enabled model ID.