GPT-6 vs Claude Opus 5.5: Which AI Projects Now Pay Off for SMBs

What launched on September 22, 2026?

On September 22, 2026, Anthropic released Claude Opus 5.5 at $4 per million input tokens and $20 per million output tokens. About 90 minutes later, OpenAI released GPT-6 Sol ($2/$10) and GPT-6 Luna ($0.10/$0.50), roughly half the price of the GPT-5.6 versions. For most small businesses, the better tiers now cost what cheaper ones did a month ago.

What does 'half price' mean for your actual bill?

Cost per task is (input tokens x input price) + (output tokens x output price). Price cuts matter most for jobs with lots of input, high volume, or many steps.

Which model fits which job?

Send each task to the cheapest model that meets your accuracy bar on your own test cases. Most businesses end up using two tiers, not one.

Five SMB projects that now pay back, and two that still don't

A project pays back when the hours it saves are worth more than the build, the running cost and the time spent reviewing its output.

• Intake summaries. Law firms, dental practices and home-service companies can turn calls and web forms into a structured summary with urgency flags before anyone opens the file. Luna or Sol cover this at cents per hundred.
• Document extraction. Invoices, claim forms, leases and purchase orders go into your accounting or practice software as fields, with a person checking anything the model marks as low confidence.
• Support triage. The model tags, routes and drafts first replies for your inbox or help desk. Staff edit and send. High volume and short outputs make this a good fit for the cheapest tier.
• Sales and service call notes into the CRM. Transcripts become next steps, deal fields and follow-up tasks. The main work here is the integration, not the model fees.
• Back-office agents with sign-off. Examples include reconciling Shopify orders against payouts, chasing missing paperwork, or entering data into a supplier portal. These jobs need many steps and a lot of context, so they gained the most from the price drop and from Opus 5.5's computer-use results.

Already running an AI feature? Re-benchmark before you switch

Re-benchmarking means running your current prompts and your own past cases through the new models, then comparing accuracy, speed and cost per task against what you run today.

• Build a test set of 50 to 100 real past cases with known correct answers, including the messy ones.
• Score each candidate on accuracy, on whether it follows your output format (valid JSON, required fields present), on response time and on actual cost per task from the usage logs.
• Try more than one setting. On Opus 5.5, low or medium effort may meet your bar at a lower cost than high.
• Run the winner in shadow mode next to the current model for a week or two before switching traffic.
• Keep the old configuration one flag away so you can roll back the same day.

How do you avoid getting locked into one AI provider?

Build so the model is a setting, not a foundation. Prompts, schemas and routing should live in your code, where swapping providers is a configuration change.

• One internal interface for model calls, so the rest of the app never talks to a vendor SDK directly.
• Prompts and model choices in versioned configuration, not scattered through the code.
• Schema validation on every structured output. If a response fails, retry it or send it to a person.
• Prompt caching for the parts that don't change, like instructions, policies and reference documents. Opus 5.5 cache reads cost $0.20 per million tokens against $4 for regular input, and OpenAI bills cached input at a 90% discount.
• A fallback model from the other provider for timeouts and outages. Anthropic says Opus 5.5 is also available through Amazon Web Services, Google Cloud and Microsoft Azure, which helps if you already run on one of them.

Governance basics: data, review and logs

Governance for a small team comes down to three things: know where your data goes, decide which outputs a person must approve, and keep a record of what the model did.

• Data handling: which provider and region processes the data, how long they keep it under your plan's terms, and whether you need a signed agreement (such as a HIPAA business associate agreement) before sending patient or client records.
• Minimize inputs: remove identifiers the task doesn't need before a document reaches the model.
• Human review: set confidence thresholds, and name who approves anything that goes to a customer or changes money, records or appointments.
• Logging: store the input reference, model version, output and reviewer for each task, so you can audit mistakes and re-run tests later.
• Agent permissions: give computer-use and back-office agents the narrowest access that still works, and require approval for anything irreversible.

When to bring in an AI development partner

Bring in a partner when the model is the easy part and the hard parts are the integration, the test set, cost controls and the review workflow.

Frequently asked questions