Last week, I was speaking about a major architecture revamp of an existing application into a more futuristic landscape, when a colleague asked:

“Is this all AI generated?”

I paused for a moment. Not because the question was unexpected—but because it revealed something deeper.

Somewhere along the way, we’ve started treating AI as a monolithic capability. As if there’s a single system, a single model, a single “magic box” that can handle everything we throw at it. But the reality is very different.

Behind every meaningful AI system are a series of decisions—what model to use, when to use it, and more importantly, when not to.

That conversation made me realize something –

We’re not struggling with AI adoption anymore. We’re struggling with AI decision-making.

Are we choosing the right LLM for the task—or just defaulting to what’s available?

Why “One Model for Everything” Fails

Each model is designed with specific strengths and weaknesses. When you use a large, expensive model for a simple task, you’re not just overspending—you’re also missing out on faster, more efficient solutions.

Using the same AI model for every task is inefficient:

• Overkill for simple tasks: You pay premium prices for tasks a lightweight model can handle.
• Not enough for complex tasks: Simpler models miss nuance and critical details.
• Wasted resources: You burn budget and compute on the wrong tool.

Not All AI Tasks (or Models) Are Created Equal

I have used various AI models, from different providers like OpenAI , Anthropic , and I can say I follow below mental model to choose the right model for the right task.

Rule of thumb:

• Large = capability
• Small = efficiency
• Medium = balance

A lot of time when I propose this mental model, i get asked the question – How can i be sure? How do i know which model is best for my task? 

To solve this, I decided to run a real-world experiment. I took 3 engineering tasks of varying complexity and ran them through multiple models from OpenAI, Anthropic, and Google. I tracked token usage, calculated costs, and evaluated the quality of the outputs.

Real-World Model Comparison: What the Data Shows

I ran 9 API calls to compare models from OpenAI, Anthropic, and Google on real engineering tasks. Results were logged, tracked, and verified.

The Experiment: How I Did It

I built a Python framework to test 3 tasks across multiple models from different providers. The script tracked token usage, calculated costs, and exported results to a CSV.

Here is the core part of the script:

def calculate_cost(prompt_tokens, completion_tokens, model):
    """
    Calculate the cost of a task based on token usage and model pricing.
    """
    return round(
        (prompt_tokens / 1000 * model["input_price"]) +
        (completion_tokens / 1000 * model["output_price"]),
        6
    )
# Example models and tasks
models = [
    {"name": "<MODEL_NAME_PLACEHOLDER>", "id": "<MODEL_ID_PLACEHOLDER>", "input_price": "<MODEL_INPUT_PRICE>", "output_price":"<MODEL_OUTPUT_PRICE>"},
    {"name": "<MODEL_NAME_PLACEHOLDER>", "id": "<MODEL_ID_PLACEHOLDER>", "input_price": "<MODEL_INPUT_PRICE>", "output_price": "<MODEL_OUTPUT_PRICE>"},
    {"name": "<MODEL_NAME_PLACEHOLDER>", "id": "<MODEL_ID_PLACEHOLDER>", "input_price": "<MODEL_INPUT_PRICE>", "output_price": "<MODEL_OUTPUT_PRICE>"}
]
tasks = [
    {"type": "Low", "name": "Log Classification", "prompt": "Classify the following log as INFO, WARNING, or ERROR:\n\n'Database connection timeout after 30 seconds'"},
    {"type": "Medium", "name": "Code Refactoring", "prompt": "Refactor this Python code to improve readability and performance:\n\nfor i in range(len(items)):\n    print(items[i])"},
    {"type": "High", "name": "Backend Service", "prompt": """Write a Python service that:
- Consumes messages from Kafka
- Processes JSON data
- Stores results in MongoDB
- Handles retries and logging"""}
]
# Run tasks across models

After running the script for all these different tasks, executed by all the models i.e Small , Medium and Large, below data is what I got.

This data is based on real API calls, with costs derived from token usage and model-specific pricing, as outlined in official documentation from OpenAI and comparable resources from providers such as Anthropic.

The Patterns: Cost vs. Quality

Low Complexity Tasks

• GPT-4o Mini: $0.0002/task (Quality: 2/5)
• GPT-4o: $0.0002/task (Quality: 2.5/5, slightly better)
• GPT-5: $0.0003/task (Quality: 3/5, best quality, higher cost)

Verdict: Use GPT-4o Mini for cost savings.

Medium Complexity Tasks

• GPT-4o Mini: $0.0003/task (Quality: 3/5)
• Claude 3 Haiku: $0.0003/task (Quality: 3.5/5, better quality, same cost)
• GPT-5: $0.0004/task (Quality: 4/5, best quality, slightly higher cost)

Verdict: Claude 3 Haiku offers the best balance of cost and quality.

High Complexity Tasks

• GPT-4o Mini: $0.002/task (Quality: 4/5)
• Claude 3 Opus: $0.0025/task (Quality: 4.5/5, better quality, slightly higher cost)
• Gemini 1.5 Pro: $0.003/task (Quality: 5/5, premium quality, highest cost)

Verdict: Use Gemini 1.5 Pro for critical tasks where quality is paramount.

Note: The quality of the outputs was evaluated based on relevance, accuracy, and completeness, with a simple rating system (1-5) for each task.

Final Thoughts

• Small models are fast and cheap but limited.
• Medium models balance cost and quality for most tasks.
• Large models excel in complex tasks but are expensive.

Pro tip: Match the model to the task for better results, faster, and cheaper.