How to compare AI models and budget for a real workflow

A model choice is easier to defend when it is tied to a task and a review rule. Start with the current GetRun model catalog, then compare a few eligible models on the same inputs. The aim is to find a useful quality and cost balance for your workflow, not to declare one model best at every task.

Filter for the request you actually need

Write down the input type and required API behavior before comparing prices. A text extraction task, an image operation, and a client that requires a particular protocol have different constraints. Check the model catalog and the relevant API guide; use an exact public model identifier rather than a display name guessed from another provider.

Confirm that the model is available to the Key you will use. If a model or a previous session is no longer available, choose an available model and verify the setup with a small request. Treat a failed request as a configuration or service issue to investigate, not as proof that the model has low answer quality.

Build a small test set with a review rubric

Choose a few ordinary examples and at least one difficult case from the actual workflow. For a project brief, include a note with a missing date and a pair of conflicting statements. For structured extraction, include an absent field. Decide the expected behavior before you read the model outputs.

Use the same inputs and review criteria for each candidate. Record whether required facts were retained, unsupported facts were introduced, and the requested output format was followed. If repeated runs vary, include that variation in the decision instead of selecting only the most impressive sample.

Correctness: does the result match the source?
Completeness: are the required fields or decisions present?
Uncertainty: are missing or conflicting facts represented honestly?
Usability: can the result be consumed without manual restructuring?

Measure the whole task's usage

Check the live model page for current prices and units. Input, output, and cache-related usage can have separate prices; other operations may use a different billing unit. Do not infer the full cost from the headline model name or from one token rate.

After each test, inspect the console's usage record and compare the recorded model, operation, token counts, and actual fee. An agent task can involve several model calls, so the cost of one response is not the cost of the entire workflow. Every retry is a new call and may add usage and fees.

Use hypothetical numbers only to explain arithmetic: if the recorded total for one complete test is 0.02 in your chosen accounting currency, 100 comparable tasks would start at 2.00 before variation and retries. This is not a GetRun price quote. Replace it with your measured total and use consistent units.

Make a decision and define a review trigger

Keep a simple record with the task version, model identifier, review outcome, actual total cost, and any manual corrections. Choose the model that passes your required checks with an acceptable total cost. A cheaper response that needs extensive rewriting may be a poor choice for that particular task.

Repeat the comparison when the task, prompt, model availability, or price changes materially. Start a new workflow with a small batch and review its usage before expanding it. For unexpected failures, consult the documented error and retry behavior rather than repeatedly resending the same request.

Keep the original test inputs and review rules stable.
Count failed attempts and retries when estimating the workflow budget.
Record the date of a price check instead of embedding a permanent price in a tutorial.

A compact comparison record

Task: extract project decisions from three notes.
Candidate: exact model identifier from the catalog.
Checks: source accuracy, missing fields, conflicting dates, output format.
Result: pass/fail plus the corrections needed.
Usage: total recorded cost across all calls, including retries.
Decision: keep, revise the task, or test another eligible model.