Lease Abstract → NER Calculation
Extract economic terms from a full industrial lease, then calculate Net Effective Rent.
What This Benchmark Tests
Can a model read a real ~1M SF, single-tenant, NNN industrial lease and produce the numbers a CRE analyst would produce — premises size, term, base rent, escalations, free rent, TI, LL work, commissions — and then correctly compute Net Effective Rent using the methodology provided? Two prompt conditions test whether models need explicit methodology hand-holding or can reason about NPV conventions on their own.
The Prompt
The Prompt
The exact system + user prompt every model runs against. Both prompt variants shown.
Reference Documents
Industrial Lease (NNN)
~1M SF single-tenant · full lease with Addendum 1 rent schedule
Task Structure
- 1
Extract lease economics
Premises, term, base rent, escalations, free rent, TI, LL work, commissions.
- 2
Calculate Net Effective Rent
NPV of monthly cash flows over the full term, annualized to $/SF/yr.
How It's Scored
Nine numeric fields are compared to a hand-validated answer key. Extraction fields (premises, term, rent schedule) must match exactly. NER is scored with a tolerance for legitimate convention differences (annual vs. monthly, end vs. beginning of period). Workflow score weights extraction and calculation 50/50.
See full methodology →Results Snapshot (Top 5)
Detailed prompt
Full methodology spelled out — discount rate, monthly cash flow mechanics, annualization.
| Model | Score | Notes |
|---|---|---|
| Gemini 3.1 Pro | 100.0% | — |
| Claude Opus 4.6 | 98.7% | — |
| GPT-5 Mini | 98.2% | — |
| Claude Sonnet 4.6 | 97.9% | — |
| Claude Haiku 4.5 | 95.0% | — |
Less detailed prompt
Methodology unlocked — the model decides which NPV convention to apply.
| Model | Score | Notes |
|---|---|---|
| GPT-5 | 93.5% | — |
| Claude Sonnet 4.6 | 78.3% | — |
| Gemini 3.1 Pro | 77.5% | — |
| GPT-5 Mini | 73.9% | — |
| Claude Opus 4.6 | 68.4% | — |
Run it yourself
Pick a model, run the benchmark, and see where it holds up and where it breaks.