2032L2-26-X-0001 AIMAS RFI Table 1.docx

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Request for Information (RFI) for Artificial Intelligence (AI) Model Access Services
Solicitation number
2032L2-26-X-0001
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This is a Request for Information (RFI) Table 1 document that provides an AI tokens calculation reference for the Department of the Treasury's anticipated acquisition of Artificial Intelligence Model Access Services (solicitation 2032L2-26-X-0001). The table demonstrates sample token counting methodology with examples including text strings ("Hello unbelievable The quick brown fox. don't 2025 1,234,567 !!!") and a Python code snippet for flagging large transactions, with approximate input token counts and explanatory breakdowns. The document serves as a template or reference tool to illustrate how token consumption will be measured and calculated for AI model access services.

This supporting document is part of a broader RFI issued by the Department of the Treasury in accordance with Federal Acquisition Regulation Part 10 for market research and acquisition planning purposes. The RFI seeks industry feedback to refine the scope and structure of an anticipated streamlined solicitation for accessing Frontier AI company tokens, with a projected release in March 2026. The acquisition is aligned with recent Executive Orders and Office of Management and Budget memoranda directing federal agencies to accelerate AI adoption and drive efficient AI procurement, with the primary objective of establishing direct contractual relationships with frontier AI and Large Language Model developers that maximize flexibility, speed, and adaptability for rapidly evolving AI capabilities.

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2032L2-26-X-0001 Table 1

AI Tokens Calculation

Text
Approx. Input Token Count
Explanation/Breakdown

Hello unbelievable

The quick brown fox.

don’t

1,234,567 import pandas as pd def flag_large_transactions(file_path, threshold):

df = pd.read_csv(file_path) flagged = df[df["transaction_amount"] > threshold] summary = { "total_transactions": len(df), "flagged_transactions": len(flagged), "total_flagged_amount": flagged["transaction_amount"].sum() return summary results = flag_large_transactions("transactions.csv", 10000) print(results)

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