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AI agent for ai consultants

Model Selection Bake-Off Agent

Choose a model with evidence on quality, cost and speed for the real task

DecisionsIT and development
See how it worksGet access
Model Selection Bake-Off Agent: what goes in, what the agent does and what you get

What it does

Teams choose a model because it feels smart in a demo, then discover it is slow or expensive on the real workload. This agent runs a fair contest. It takes the team's own test set and candidate models, runs each, grades the answers with a rubric and a second grader for spot checks, and records cost per task and time per task. It also tries prompt adjustments per model, since a prompt tuned for one model can unfairly handicap another. It then ranks the models by the team's rules, such as quality first, then cost under a limit, then speed. The owner approves the choice. Edge case: the cheapest model wins on average but fails a rare critical case, so the agent applies the must-pass rule.

How it works

Follow the arrows from top to bottom. The orange dashed arrow is the loop: when a check fails, the agent goes back and tries again.

Start and resultWhat it doesA check on its own workWaits for your OKGoes back and retries
Yes, continueYes, continueApprovedNoNo 1 STARTS WHEN Model choice needed 2 USES A TOOL Run the test set on each candidate model 3 USES A TOOL Grade answers with the rubric and spot-check with asecond grader 4 USES A TOOL Record cost and time per task 5 CHECKS THE RESULT Do the two graders agree on at least 90% of cases? If not: clarify the rubric and regrade. Back to step 3. 6 DOES Tune the prompt for each model and rerun the weakestcases 7 DOES Apply must-pass and limit rules to disqualify models 8 DOES Rank the remaining models by the ranking rules 9 CHECKS THE RESULT Is the top model's lead larger than the run-to-runnoise? If not: run the test set again with more samples andrecompute. Back to step 2. 10 YOU APPROVE Owner approves the choice 11 RESULT Bake-off report with the ranking and trade-offs
Read the steps as a list
  1. Model choice needed
  2. Run the test set on each candidate model
  3. Grade answers with the rubric and spot-check with a second grader
  4. Record cost and time per task
  5. Do the two graders agree on at least 90% of cases?If not: clarify the rubric and regrade. Back to step 3.
  6. Tune the prompt for each model and rerun the weakest cases
  7. Apply must-pass and limit rules to disqualify models
  8. Rank the remaining models by the ranking rules
  9. Is the top model's lead larger than the run-to-run noise?If not: run the test set again with more samples and recompute. Back to step 2.
  10. Owner approves the choiceThe agent waits here for your OK.
  11. Bake-off report with the ranking and trade-offs

How it decides

It disqualifies any model that fails the must-pass cases or limits, then ranks the rest by the stated priority of quality, cost and speed.

  • Disqualify any model that fails a must-pass case
  • Disqualify any model over the cost or latency limit
  • Require grader agreement of at least 90%
  • Treat differences under 2 points as a tie and use cost to decide

Make it yours

Every agent is a starting point. You choose these settings for your own situation.

  • Candidate models
  • Must-pass cases
  • Ranking priority
  • Cost and latency limits
  • Runs per case (default 3)

What keeps you in control

It always asks you first

  • Owner approves the final model choice
  • Finance approves a cost increase

Hard limits

  • Never use the test set to tune the prompt for only one model
  • Never send customer data to a model the team has not approved

It stops when

  • Done: a model is chosen with the report saved
  • Stop: no model meets the limits

Set it up

We guide you through the set-up, step by step

Members get the full set-up guide for this agent. No technical skills needed: you copy, paste and upload.

10 minto set it up in your AI
5 AIsChatGPT, Claude, Copilot, Gemini, Grok
  • One set of instructions to paste into your AI, with the clicks for ChatGPT, Claude, Microsoft 365 Copilot, Gemini and Grok
  • The agent then walks you through connecting your own data, one source at a time
  • A downloadable copy with the flow chart, the rules and the full guide
Get access to this agent

An example run

What happensFour models were run on 300 support cases. Model B scored highest at 91.2 but cost 3 times Model C at 89.8. Graders agreed on 84% at first, so the check failed. The rubric was clarified and agreement reached 93%. Model C then failed a must-pass refund-policy case, which disqualified it. Model A scored 90.9 at 40% of B's cost. The lead was inside the noise, so the owner chose A.

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