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Claude Opus 5.5 vs GPT-6 Sol and Luna: Which AI Model Should CRE Investors Use?

By Avi Hacker, J.D. · 2026-09-22

What is the Claude Opus 5.5 vs GPT-6 Sol comparison? It is the model choice CRE teams now face after Anthropic released Claude Opus 5.5 on September 22, 2026, the same day OpenAI released GPT-6 Sol and GPT-6 Luna at lower prices. Opus 5.5 is Anthropic's new top Opus model, which Anthropic says performs at the level of Claude Fable 5.1 on most work for 40% less than Opus 5. GPT-6 Sol is OpenAI's cost-efficient workhorse and Luna its budget tier. For the full landscape, see our AI model comparison guide for CRE investors.

Key Takeaways

  • Claude Opus 5.5 costs $4 per million input tokens and $20 per million output tokens, twice GPT-6 Sol's list price of $2 and $10.
  • Cached input reads cost $0.20 per million tokens on both Opus 5.5 and GPT-6 Sol, so re-reading a cached lease costs the same on either model.
  • On AutomationBench, Anthropic reports Opus 5.5 at 40.0% and OpenAI reports GPT-6 Sol at 33.2%, with both vendors listing Opus 5 at 26.9%.
  • In an Anthropic research test, 16 of 18 Opus 5.5 reports cleared a quality bar that any invented figure would fail, which neither Fable 5.1 nor Opus 5 managed.
  • For most CRE firms the practical answer is routing: Luna for bulk extraction, Sol for everyday agent work, and Opus 5.5 for analysis that reaches an investment committee.

Claude Opus 5.5 vs GPT-6 Sol: The Pricing Side by Side

Here are the published API prices, per 1 million tokens:

  • Claude Opus 5.5: $4 input, $20 output, $0.20 cache reads, $5 cache writes. Anthropic says output is more than 30% faster than Opus 5.
  • GPT-6 Sol: $2 input, $10 output, with a 90% discount on cached input reads, which works out to $0.20.
  • GPT-6 Luna: $0.10 input, $0.50 output, with the same 90% cached-read discount.

Run those prices against a real document. Using a rough estimate of 40,000 input tokens for a 60-page commercial lease and 3,000 output tokens for a structured abstract:

  • Opus 5.5: about $0.16 of input plus $0.06 of output, roughly $0.22 per lease, or about $220 for a 1,000-lease portfolio.
  • GPT-6 Sol: roughly $0.11 per lease, or about $110 per 1,000 leases.
  • GPT-6 Luna: roughly half a cent per lease, or about $5.50 per 1,000 leases.

The gap narrows once a document is cached. Each cached re-read of that 40,000-token lease costs about $0.008 on either Opus 5.5 or Sol. An asset manager asking 20 follow-up questions pays roughly $0.35 in input on Opus 5.5, including the cache write, versus roughly $0.23 on Sol. List price also is not task cost: Anthropic says Opus 5.5 uses fewer tokens per task than Opus 5, which is how it arrives at a 40% cost reduction despite a 20% token price cut. We covered OpenAI's side of this in what GPT-6 Sol and Luna pricing means for CRE investors.

What the Benchmarks Actually Compare

Because the two launched the same day, neither vendor benchmarked the other's new model. Anthropic compared Opus 5.5 with GPT-6 Astra and GPT-5.6 Sol, not the new GPT-6 Sol, and OpenAI compared Sol and Luna with Opus 5 and Fable 5.1. Only a few head-to-head readings are possible, and all figures below are vendor-reported:

  • AutomationBench (business workflows across apps): Opus 5.5 scored 40.0% in a Zapier-run evaluation, while OpenAI reports GPT-6 Sol at 33.2% at xhigh effort for $0.27 per task. Both vendors list Opus 5 at 26.9%, which suggests a shared baseline. Anthropic notes its Opus 5.5 run counted safeguard interventions as failures.
  • FrontierCode v1.1 (mergeable code): Anthropic reports Opus 5.5 at 54.4% versus 50.3% for Fable 5.1. OpenAI says Sol matches Fable 5.1 at xhigh effort. Read together, Opus 5.5 appears ahead, but no single run tested both.
  • OSWorld 2.0 (computer use): not comparable. Anthropic lists Opus 5 at 74.0% while OpenAI's offline run shows Opus 5 at 60.3% at medium effort, so the setups differ.
  • GDPval-AA v2.1 (professional work across 44 occupations): Anthropic reports Opus 5.5 at 1846 Elo versus 1542 for GPT-6 Astra. OpenAI has not published a Sol score here.

Anthropic itself cautions that at these capability levels, benchmark margins have become a less reliable guide to real-world differences. That is the right lens for CRE buyers.

The Result That Matters Most for CRE: Invented Figures

For underwriting and investor reporting, the most important claim in either launch is not a benchmark score. In an internal Anthropic test, models wrote a quarterly performance report on a company using only information they could find on a copy of the web, and an automated grader checked every figure and quote against sources. Any invented number failed the report. According to Anthropic's announcement, 16 of 18 Opus 5.5 reports cleared that bar, while neither Fable 5.1 nor Opus 5 cleared it in any attempt.

OpenAI makes a related claim: on its internal factuality evaluation, GPT-6 Sol makes about half as many mistakes as its predecessor, according to OpenAI's announcement. Both are vendor tests, but they point at the failure that costs CRE firms most. A market report that invents a submarket vacancy rate, or an IC memo that misstates DSCR, does more damage than a slow answer. If a property shows NOI of $1.5 million against $1.2 million of annual debt service, the DSCR is 1.25x, and a model that quietly computes 1.35x has changed the loan decision.

Which Model for Which CRE Workflow

  • GPT-6 Luna for bulk extraction: pulling rent, term, escalations, and options from every lease or estoppel in a portfolio, where the cost per document barely registers.
  • GPT-6 Sol for everyday agent work: OM screening, comp gathering, and multi-app workflows where volume is high and a reviewer checks the output.
  • Claude Opus 5.5 for high-stakes analysis: IC memos, waterfall reviews, market research with citations, and financial models where an invented figure is the costliest error.
  • Keep humans on the final numbers: no vendor claims zero errors. Verify NOI, cap rate, and DSCR inputs before anything reaches investors.

Our earlier ChatGPT vs Claude vs Gemini comparison for real estate analysis explains how to run a fair bake-off on your own documents. For personalized guidance on implementing these strategies, connect with The AI Consulting Network.

Availability

Anthropic says Claude Sonnet 5.5 and Claude Haiku 5.5 will follow in the coming weeks, and a fast mode for Opus 5.5 costs $8 input and $40 output per million tokens. Anthropic is also raising five-hour usage limits on Pro, Max, Team, and seat-based Enterprise plans. GPT-6 Sol and Luna are rolling out in ChatGPT Work and Codex for paid plans and are available in the API. 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: Is Claude Opus 5.5 better than GPT-6 Sol?

A: On the benchmarks both vendors report, Opus 5.5 scores higher, including 40.0% versus 33.2% on AutomationBench. GPT-6 Sol costs half as much per token, so the better choice depends on whether the task rewards accuracy or volume.

Q: How much does Claude Opus 5.5 cost compared to GPT-6 Sol?

A: Opus 5.5 costs $4 per million input tokens and $20 per million output tokens. GPT-6 Sol costs $2 and $10. Cached input reads cost $0.20 per million tokens on both.

Q: What is the cheapest model for lease abstraction?

A: GPT-6 Luna, at $0.10 per million input tokens and $0.50 per million output tokens, costs roughly half a cent per 60-page lease. Test field-level accuracy against leases you have already abstracted before relying on it.

Q: Should CRE firms use one model or several?

A: Most firms benefit from routing: a low-cost model for bulk extraction, a mid-tier model for everyday agents, and a premium model for analysis that reaches investors. The savings from routing usually outweigh any single model's benchmark lead.