Model Cost Comparison: choose AI models around your workload

Choosing an AI model involves two different questions: can it do the job, and what will it cost at your expected volume? A benchmark can help with the first. A price per million tokens only begins to answer the second.

We built modelcostcomparison.com at Lazige so you can compare AI model costs around the workload you actually run. Describe what you are building, explore available benchmark measurements, and compare estimated token costs using your own assumptions.

The result is a starting point for evaluation: a shortlist you can test, with costs you can inspect.

Start with the application you are building

A support assistant, a coding copilot, and a document-search system can use the same model very differently. A support assistant might handle many short exchanges. A coding copilot might receive a large code context. A search assistant might spend much of its input budget on retrieved documents.

On the homepage, Find my models lets you describe that application in ordinary language. For example:

I’m building a support assistant for around 50,000 requests per month. Most answers should be short. Help me compare suitable models and explain the cost assumptions.

The advisor combines a filtered catalogue of eligible offers with available benchmark information to prepare a recommendation. Its shortlist can update the calculator and the related benchmark view, so you can inspect the assumptions behind the suggestion.

The request is a starting brief. Before you rely on the estimate, replace any inferred usage assumptions with measurements from your application.

Compare one workload across different offers

You can also open the calculator directly. Set the monthly request volume, average tokens per request, the input and output split, and the expected cached-input share.

Consider this hypothetical workload:

• 50,000 requests per month
• 800 input tokens per request
• 200 output tokens per request
• no cached input in the initial estimate

That produces 40 million input tokens and 10 million output tokens per month. For an offer with input rate I and output rate O, both quoted in dollars per million tokens, the token subtotal is 40 × I + 10 × O.

This makes the comparison concrete. You can then change the volume, shorten the output, or explore caching assumptions and see how the estimate changes. The arithmetic describes a planning scenario, not a measured customer outcome.

Compare exact offers as well as model names. A provider may publish separate rates for different service tiers or billing channels. Two rows can refer to the same underlying model under different conditions.

Use benchmarks to build a test shortlist

Model Cost Comparison also presents benchmark information from Artificial Analysis. Where measurements are available, you can explore intelligence scores, benchmark cost per task, and output speed on the benchmarks view.

These measurements answer different questions. A benchmark’s cost per task describes that benchmark workload. The calculator estimates token spending for the workload you entered. They should not be treated as interchangeable numbers.

Use the benchmark view to decide which models deserve a closer look. Then test those candidates on representative examples from your own application: difficult support questions, real retrieval contexts, or code changes with known acceptance criteria. Missing benchmark data is a gap in evidence, not proof that a model is unsuitable.

Check the evidence behind the estimate

The pricing catalogue records offer details and review information. Benchmark data has its own source and snapshot date. A scheduled data refresh should not be mistaken for a new primary-source price review.

The methodology explains the scope and limitations. Coverage is a reviewed subset of the market, and the downloadable data can contain records that are not eligible for recommendations.

Token estimates also leave out charges that may matter to your application, including tool use, search, storage, and taxes. Check the provider’s own terms and billing details before you commit a budget.

Bring the comparison into your development workflow

The website is one way to work with the catalogue. The developer documentation also describes the public interfaces, including an MCP server that compatible AI assistants can use to retrieve pricing and calculate token costs.

You can work through a model comparison in your development environment, then return to the website for the wider context. Compare AI model costs in Cursor and Claude walks through that connection. The MCP tools support pricing and cost analysis. A lower-priced result does not establish better task performance.

Build a shortlist you can defend

Start with a realistic workload, inspect the cost assumptions, and use available benchmarks to choose candidates for testing. Record the offer and the review date alongside your estimate so the decision can be revisited as the application changes.