The Scenario
You've been handed a project: evaluate LLM options for a bulk scraping pipeline. Your manager wants a workbook showing every model Parsera supports — provider, model name, cost per input token, cost per output token, context window — so the team can compare options side by side before committing to one. You know Parsera exposes this data. You don't know exactly where it lives in their documentation, and you don't want to spend 45 minutes cross-referencing spec pages and typing numbers into cells.
The bad version:
- Open the Parsera documentation, navigate to the LLM specs section, read the first model entry, type provider into A2, model name into B2, input cost into C2, output cost into D2, context window into E2
- Scroll to the next model, type again
- Realize the docs are paginated or the format changes between providers, so you have to re-read before each entry
- Get 11 models in and wonder if you're even looking at the complete list
This is reference data. It exists in a structured source. It belongs in a workbook, not in a manually assembled table that took an hour to build.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Excel workbook. It reads the workbook and through its Parsera integration can fetch the full LLM specs catalogue and build a comparison table directly in the workbook — headers and all.
Pull the full Parsera LLM specs list into Excel and sort by input cost ascending so I can find the cheapest model options for a bulk scraping project
What You Get
- Row 1 gets headers: Provider, Model Name, Input Price per Token, Output Price per Token, Context Window
- Every subsequent row gets one model's data populated across those five columns
- The table arrives pre-sorted by input cost so the most economical options are immediately visible at the top
What If the Data Is Not Quite Ready
Leadership wants the data on a specific worksheet
Pull the full Parsera LLM specs list and write it into the 'Model Comparison' worksheet with columns for provider, model, input cost, output cost, and context window. Create the worksheet if it doesn't exist
You need to flag models above a certain cost threshold
Fetch all LLM specs from Parsera and populate the comparison table. After populating, flag any model in column F where the input price per token exceeds 0.00001 so the team knows which ones to avoid for high-volume work
You want to sort by context window to find the models best suited for long documents
Fetch all Parsera LLM specs and build the comparison table with provider, model name, input cost, output cost, and context window. Sort the results by context window descending so the models with the largest context appear first
One prompt: fetch, populate, add a calculated cost column, and highlight the best options
Fetch all Parsera LLM specs and build the comparison table with provider, model name, input cost, output cost, and context window. Add a column G that calculates total cost for 1 million tokens (500K input + 500K output). Highlight the three rows with the lowest total cost
The pattern: fetch and analysis in one ask, not two separate steps.
Try It
Get the 7-day free trial of SheetXAI and open a blank Excel workbook or any workbook where you need a model comparison table. Ask SheetXAI to fetch the Parsera LLM specs and build the table for you. For related work, see how to list all your Parsera agents for an audit or fetch the proxy country list.