AI Model Comparison for CRE Investors (2026 Guide)

What is AI model comparison for CRE? AI model comparison for CRE is the practice of testing current AI models against the work commercial real estate investors actually do, such as lease abstraction, offering memorandum screening, underwriting support, and investment committee memos, then routing each task to the model that handles it accurately at the lowest cost per completed task. This guide was updated on September 22, 2026, the day Anthropic released Claude Opus 5.5 and OpenAI released GPT-6 Sol and GPT-6 Luna. For the broader software landscape, see our complete guide to AI tools for real estate investors.

Key Takeaways

No single model wins every CRE task in September 2026, so route bulk extraction, everyday analysis, and investment committee work to different price tiers instead of standardizing on one.
Claude Opus 5.5 lists at $4 input and $20 output per million tokens, and Anthropic says it performs at the level of Claude Fable 5.1 on most work.
OpenAI's GPT-6 Luna costs $0.10 input and $0.50 output per million tokens, which puts a 60-page lease abstract at roughly half a cent.
List price is not task cost: tokens per task, tokenizer differences, reasoning effort, and prompt caching can move the real bill more than the rate card does.
Gemini 3.1 Pro is still a preview model; stable Gemini 3.8 Flash costs an introductory $0.75 input and $3.75 output per million tokens through 2026.
Test every candidate on 20 documents you have already reviewed by hand, and score invented figures and formula errors, not just speed or benchmark rank.

The Current AI Model Lineup for CRE Investors

The March 2026 version of this guide recommended GPT-5 and Claude Opus 4.6. Both are now predecessors. Between September 1 and September 22, 2026, Anthropic, Google, and OpenAI shipped at least six new models. All prices below are API list prices per 1 million tokens, input first and output second, as of September 22, 2026.

Anthropic (Claude)

Claude Fable 5.1: $10 and $50, with cache reads at $0.25. Released September 1, 2026, it is Anthropic's top generally available model, which Anthropic describes as its most capable model for ambitious coding projects. Claude Mythos 5.1 is the same model with different safeguards, available only through trusted access programs.
Claude Opus 5.5: $4 and $20, with cache reads at $0.20. Released September 22, 2026. Anthropic says it performs at the level of Fable 5.1 on most work and costs 40% less to run than Opus 5, which listed at $5 and $25.
Claude Sonnet 5: $2 and $10. According to Anthropic's pricing documentation, that launch price is now the standard price.
Claude Haiku 4.5: $1 and $5, the budget tier for now. Anthropic says Claude Sonnet 5.5 and Claude Haiku 5.5 will follow in the coming weeks.

OpenAI (GPT)

GPT-6 Astra: $10 and $50, with cached input at $1, per OpenAI's model documentation. OpenAI's best model, announced September 3, 2026, launched first through a gated enterprise program, with paid ChatGPT plans, AWS Bedrock, and Microsoft Azure to follow. Its context window is 1,050,000 tokens. We covered its computer use in what GPT-6 Astra means for CRE underwriting.
GPT-6 Sol: $2 and $10, with cached input at $0.20. That is half the price of GPT-5.6 Sol, and OpenAI says Sol makes about half as many factual mistakes as its predecessor on an internal evaluation.
GPT-6 Luna: $0.10 and $0.50, with cached input at $0.01. The low-cost tier, and the obvious candidate for high-volume extraction.

Google (Gemini)

Gemini 3.1 Pro: $2 and $12 for prompts up to 200,000 tokens, rising to $4 and $18 above that. Google's API documentation still labels it a preview model, and it has no free tier.
Gemini 3.8 Flash: stable, released September 2, 2026, and described by Google as its most intelligent Flash model. Introductory pricing of $0.75 and $3.75 runs through December 31, 2026, then doubles to $1.50 and $7.50, according to Google's Gemini API pricing page. It also powers Gemini in Google Sheets.
Gemini 3.1 Flash-Lite: $0.25 and $1.50 for text, image, and video input.

Search and Alternative Options

Perplexity's API offers web-grounded answers with built-in citations, and its Sonar Pro model lists at $3 and $15 plus a per-request search fee. xAI calls Grok 4.7 its most capable model, at $2 and $6 for prompts under 200,000 tokens, with a 500,000-token context window. Both belong in a bake-off, especially for research that must cite its sources.

Why List Price Is Not Per-Task Cost

A rate card tells you what a token costs. It does not tell you how many tokens a model burns to finish your task. Four factors move the real number:

Tokens per task: Anthropic cut Opus 5.5's token price 20% versus Opus 5 but says typical workloads cost 40% less, which implies fewer tokens per task. OpenAI reports GPT-6 Sol at $0.27 per task on AutomationBench, the kind of per-task figure worth asking every vendor for.
Tokenizers: the same lease is not the same token count everywhere. Anthropic's documentation says Claude 4.7 and later models use a tokenizer that produces approximately 30% more tokens for the same text than earlier Claude models.
Output and reasoning: output costs five times input on most of these models, and reasoning models bill their thinking as output. Google's price list, for example, counts thinking tokens as output tokens.
Caching: analysts ask many questions of one document. Cached re-reads cost $0.20 per million tokens on Opus 5.5 and Sol, $0.25 on Fable 5.1, $1 on Astra, and $0.01 on Luna.

Here is what that means for one 60-page commercial lease, using a rough estimate of 40,000 input tokens and 3,000 output tokens for a structured abstract, before caching or batch discounts:

GPT-6 Luna: about $0.004 of input plus $0.0015 of output, roughly half a cent.
Gemini 3.8 Flash at the introductory rate: about $0.03 plus $0.011, roughly 4 cents.
GPT-6 Sol: $0.08 plus $0.03, about 11 cents.
Claude Opus 5.5: $0.16 plus $0.06, about 22 cents.
Claude Fable 5.1 or GPT-6 Astra: $0.40 plus $0.15, about 55 cents.

Across a 1,000-lease portfolio, that range runs from roughly $5.50 to $550, and Anthropic's and OpenAI's batch APIs cut those figures in half for work that can wait. The spread is real, but so is the cost of one invented rent figure in a lender package. That is why the cheapest tier should handle volume and the premium tier should handle judgment. For the OpenAI side in more depth, see what GPT-6 Sol and Luna pricing means for CRE investors.

Routing CRE Workflows to Model Tiers

The lesson from our earlier comparisons still holds: combine models, but decide the routing on purpose. Here is how the September 2026 lineup maps to common CRE workflows.

Bulk lease and document extraction: budget tier. GPT-6 Luna, Gemini 3.1 Flash-Lite, and Claude Haiku 4.5 are the candidates to test for pulling rent, term, escalations, options, and CAM provisions from every lease or estoppel in a portfolio. Route only the exceptions, like unusual co-tenancy or exclusive-use clauses, up a tier. Our lease abstraction tools guide covers the pipeline.
OM screening: mid tier. GPT-6 Sol, Claude Sonnet 5, or Gemini 3.8 Flash can score every inbound offering memorandum against your buy box. Volume is high, a person reviews the shortlist, and speed matters more than prose.
Underwriting support: mid tier for extraction and assumption checks, premium tier for model review. The math has to be right. Cap rate is NOI divided by price, and NOI excludes debt service, capital expenditures, depreciation, and income taxes. A property with $1.8 million of NOI priced at $30 million trades at a 6.0% cap rate. With $1.44 million of annual debt service, its DSCR is $1.8 million divided by $1.44 million, or 1.25x. A model that subtracts debt service before computing the cap rate, or inverts the DSCR ratio, fails regardless of its benchmark rank.
IC memos: premium tier. Claude Opus 5.5 is a strong default. In an Anthropic research test where any invented figure or quote failed the report, 16 of 18 Opus 5.5 reports cleared the bar, while neither Fable 5.1 nor Opus 5 cleared it. Reserve Fable 5.1 or GPT-6 Astra for the hardest problems, such as a contested lease interpretation or a multi-tier joint venture waterfall.
Market research with citations: search-grounded tools first. Perplexity's citation-backed answers, or Gemini with Grounding with Google Search at 5,000 free requests a month and then $14 per 1,000, gather the sourced facts, and a premium model synthesizes them. Every vacancy rate, cap rate, and sale comp still gets checked against its source.
Investor reporting: mid tier with caching. Cache the reporting template, T-12, and rent roll once, let GPT-6 Sol or Claude Sonnet 5 draft the quarterly narrative, and have an analyst confirm every number before it reaches limited partners.

Most firms do not need every vendor. One premium model for judgment, one budget model for volume, and a search-grounded tool for research cover these workflows. Our model-routing cost playbook shows how to set the escalation rules, and our head-to-head on Claude Opus 5.5 vs GPT-6 Sol and Luna compares the two newest releases in detail. If you're ready to transform your underwriting process with AI, The AI Consulting Network specializes in exactly this.

How to Evaluate AI Models for CRE Tasks

Vendor benchmarks are a starting point, not a verdict. Every benchmark figure in this guide is vendor-reported, and Anthropic itself says that at these capability levels, benchmark margins have become a less reliable guide to real-world differences. On AutomationBench, a business-workflow test, Anthropic reports Opus 5.5 at 40.0% against 41.4% for GPT-6 Astra, and OpenAI reports GPT-6 Sol at 33.2%. Gaps that size say little about how each model handles your rent roll. A two-week bake-off on your own documents answers the question better than any leaderboard:

Build a gold set: pick 20 leases, OMs, or T-12s you have already reviewed by hand, including a few messy scans and heavily amended leases. Your manual answers become the reference answers.
Hold the prompt constant: save one prompt per task in a shared library that specifies the property type, the output format, and the metrics to calculate, then run it unchanged on every model.
Score what costs money: field-level accuracy, invented figures, formula errors such as an inverted cap rate or DSCR, and cost per completed task rather than cost per token.
Measure review time: a cheaper model that doubles analyst review can cost more than a premium model, because human time is now the expensive input.
Protect the data: read each vendor's data terms before uploading sensitive deal documents. Google's pricing page, for example, says Gemini free-tier content is used to improve its products, while paid-tier content is not.
Re-test on release: keep the gold set and re-run it whenever a candidate model ships. At the September 2026 pace, a quarterly review is the minimum.

Keep a simple scorecard per workflow so the routing decision rests on your results, not on launch-day headlines. CRE investors looking for hands-on AI implementation support can reach out to Avi Hacker, J.D. at The AI Consulting Network.

Frequently Asked Questions

Q: Which AI model is best for CRE underwriting in 2026?

A: No single model wins every step. As of September 22, 2026, Claude Opus 5.5 is a strong default for model review and IC memos, GPT-6 Sol or Claude Sonnet 5 handles everyday extraction and screening, and Claude Fable 5.1 or GPT-6 Astra covers the hardest cases. Verify NOI, cap rate, and DSCR on your own deals before relying on any of them.

Q: Is ChatGPT or Claude better for commercial real estate?

A: Both vendors now offer a full price ladder, so the better question is which tier fits each task. GPT-6 Luna is the lowest-cost option in either lineup for bulk extraction, while Anthropic reports that Opus 5.5 avoided invented figures in 16 of 18 research reports in its own testing. Test both on documents you have already reviewed.

Q: Should CRE investors still use Google Gemini?

A: Yes, for the right jobs. Gemini 3.8 Flash is a stable, low-cost workhorse at an introductory $0.75 input and $3.75 output per million tokens through December 31, 2026, and Grounding with Google Search makes it useful for sourced market research. Gemini 3.1 Pro remains a preview model, so treat it as a candidate to test rather than a production default.

Q: How much does it cost to run AI on a lease portfolio?

A: At rough estimates of 40,000 input and 3,000 output tokens per 60-page lease, list prices range from about half a cent per lease on GPT-6 Luna to about 55 cents on Claude Fable 5.1 or GPT-6 Astra. Your real cost depends on actual token counts, caching, and batch discounts. For personalized guidance on implementing these strategies, connect with The AI Consulting Network.

AI Model Comparison for CRE Investors: Complete 2026 Guide