Evaluating Model ROI: The Intelligence/Price Audit

In Module 14.3, we learned how to route queries. But how do we know if our routing is actually working? What if we are routing "Logic" to a cheap model, but that model is failing 50% of the time?

If a cheap model fails, the user is unhappy, and you end up paying for a "Retry" on a more expensive model anyway. This is a Negative ROI scenario.

In this lesson, we learn how to audit your model selections. We will explore Model Comparison Frameworks, Cost-Weighted Accuracy, and the "Efficiency Threshold."

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1. The Cost-Weighted Accuracy (CWA) Metric

Standard Accuracy is: Correct / Total. Cost-Weighted Accuracy is: (Accuracy) / (Cost per 1,000 queries).

Example:

• Model A (GPT-4o): 98% Correct | $30.00 / 1M tokens.
• Model B (GPT-4o mini): 94% Correct | $0.15 / 1M tokens.

Model B is 200x cheaper, but only 4% less accurate. In most production scenarios (unless you are performing brain surgery or space navigation), Model B has a much higher ROI.

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2. The "Quality Floor" Audit

You must define a Quality Floor for every feature in your app.

• "Translations must be 90% accurate."
• "Code must compile 80% of the time."

If a cheap model falls below the "Floor," you cannot use it for that task, no matter how many tokens it saves. Efficiency at the expense of functionality is a failure.

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3. Implementation: The A/B Evaluation (Python)

To find the ROI, you must run the same "Eval Dataset" through your models simultaneously.

Python Code: Performance Auditor

def run_eval_comparison(test_queries, ground_truth):
    models = ["gpt-4o", "gpt-4o-mini", "gemini-1.5-flash"]
    
    results = {}
    for m in models:
        # Run test...
        accuracy = run_test_suite(m, test_queries, ground_truth)
        cost = get_current_model_pricing(m)
        
        # CALCULATE ROI
        roi_score = accuracy / (cost + 0.00001) 
        results[m] = {"acc": accuracy, "roi": roi_score}
        
    return results

# Analysis:
# If 'gpt-4o-mini' has ROI > GPT-4o, switch immediately.

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4. The "Model Decay" Tracker

Models get updated. Prices change. A model that was "Too Expensive" last month might have received a 50% price cut today. Efficiency ROI is a moving target. You should perform a "Price/Performance Audit" every quarter to ensure you are still using the optimized fleet.

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5. Token Efficiency and "Human Review" Costs

If a cheap model produces 5% more errors, those errors have a Human Cost. If an engineer has to spend 10 minutes fixing a hallucinated bug from a cheap model, those 10 minutes are worth $20.00 in salary.

• The Calculation: If 10 minutes of human time > Total Token Savings for 1,000 queries, then the "Cheap" model is actually more expensive for the company.

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6. Summary and Key Takeaways

1. ROI = Accuracy / Cost: Look for the "Value Peak," not just the lowest price.
2. Quality Floor: Efficiency is only valid if the output meets the minimum viable standard.
3. Price/Performance Drift: Audit your choices quarterly as market prices drop.
4. Factor in the Human: The most expensive "Token" in your system is actually an hour of your developer's life.

In the next lesson, Future-Proofing for Declining Token Prices, we look at چگونه to prepare for a world where tokens are "Too Cheap to Meter."

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Exercise: The ROI Spreadsheet

1. Take a task: "Identify names in a 1-page document."
2. Estimate the cost of doing this 1 million times with your "Favorite" expert model.
3. Estimate the cost with a "Flash" model.
4. Determine the 'Human Subsidy' limit:
   • How many errors can the Flash model make before it's cheaper to just use the Expert model and save on human debugging time?
   • (Result: Usually, you can afford a LOT of errors if the cost difference is 100x).

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Congratulations on completing Module 14 Lesson 4! You are now an ROI auditor.