Which AI coding model has the best cost per task?

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What you are probably seeing

You notice that two coding tasks that look the same in the chat window generate wildly different bill amounts.

Your monthly spend spikes not because you wrote more code but because the model is asking for extra context every time you prompt it.

Why it happens

Different models have different efficiency baselines and some consume more input tokens just to understand the same request.

Your workflow probably sends the entire codebase or history with each prompt which drives up input costs faster than output costs.

What changed recently

Step by step

1. Run one identical boilerplate coding task on two different models to create a fair baseline for comparison.

2. Record the exact input and output token counts from the developer dashboard or API logs for each run.

3. Divide the total cost of each run by the number of completed tasks to get a clear per-task metric.

4. Trim your conversation context by removing older chat history before starting the next coding session.

5. Switch your daily driver to the model with the lowest cost-per-task for routine work and reserve the expensive one for complex problems.

Ways to go further

Better fit: Vibe Coding Basics For when you are new to using AI for coding and need to understand the basic workflow of prompt and output. Open the free course on Alison ↗

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