Claude Opus 5.5 vs GPT-6 Sol: Which Is Better for Your Business?

Claude Opus 5.5 and GPT-6 Sol arrived on the same day, September 22, 2026. Both are pitched as stronger, more affordable ways to put AI to work. For a business choosing between them, the useful question is simple: which model can complete your work to a standard you would approve?

Price gives GPT-6 Sol an immediate advantage. Anthropic lists Opus 5.5 at $4 per million input tokens and $20 per million output tokens. OpenAI lists Sol at $2 and $10. At enough volume, that difference matters. But a business also pays for review, corrections, delays and the consequences of mistakes. The model bill is only one line in the cost of a finished job. Anthropic’s Opus 5.5 announcement and OpenAI’s Sol announcement set out the launch prices.

Opus leads on an independent index

Artificial Analysis has tested both new models on the same version of its Intelligence Index. At maximum effort, Opus 5.5 scored 58 and GPT-6 Sol scored 48. Opus was evaluated with its default fallback behavior enabled. The average cost per task on that index went the other way: $5.98 for Opus and $1.06 for Sol. This is a substantial capability and cost tradeoff within that test suite.

The companies’ own launch charts are less direct for this particular matchup. OpenAI compares Sol with the earlier Opus 5; Anthropic compares Opus 5.5 with the earlier GPT-5.6 Sol. Both comparisons show what a new release improves, but neither alone answers what happens when today’s two models receive the same assignment. A business should read each result for the task and setup it actually measures.

Opus’s higher index score is a reason to test it on demanding work. Sol’s lower task cost is a reason to test it wherever volume matters. Neither figure tells a finance leader whether a forecast is sound, or an engineering leader whether a code change is ready to merge.

The business pays for the review, too

Consider a research report built from several conflicting sources. A model might write a polished answer and miss the contradiction that changes the conclusion. A person then has to trace the sources, repair the argument and explain why the first version looked finished. An apparently cheap run has created expensive review work.

The same issue appears in software. An agent can produce a clean-looking pull request that passes a narrow test while changing behavior elsewhere in the product. The engineering team pays for the investigation and the second pass. For marketing, the cost might be an editor rebuilding a draft whose claims do not match its sources. The point is not that one of today’s models always makes these errors. It is that those are the costs a useful comparison must count.

That is why I want a clear assignment and a checkable result before judging either model. As I argued in AI Agents Will Turn Prompting Into a Management Skill, getting useful work from an agent starts with explaining what the result is for and how you will recognize a good one. A confident completion message cannot do that judging for you.

Give each model a real job

Opus 5.5 deserves a serious trial when the assignment is ambiguous, spans many steps or makes recovery costly. Think of a codebase migration, a complex investigation or a report that has to reconcile competing evidence. Its stronger independent index result makes it a credible candidate for that work. The business still needs to check whether the advantage appears in its own workflow.

GPT-6 Sol has a compelling case for work with clear inputs and reliable checks: transforming structured material, preparing drafts from an approved source packet, or making bounded code changes with tests. Its lower price creates room to use it frequently and iterate. Whether it is the cheaper option in practice depends on how often the output clears review.

Both models also need the right business context. A support answer can be factually fluent and still describe a retired policy. A product draft can be well written and aimed at the wrong customer. The model choice will not supply decisions the company has left out of the assignment. I wrote about that continuing responsibility in AI Agents Will Make Business Context an Operating Asset.

Benchmarks only go so far

This is the part I feel most strongly about. Benchmarks help you narrow the field. Then you have to put your own business’s work through the models and feel how each one behaves against it. A leaderboard cannot show you every hesitation, missed assumption or useful question that appears in your particular workflow.

A one-person business can replay a few recent tasks that consumed the owner’s time. A startup can give both models the same product bug, customer research packet or launch brief. A larger company can test representative assignments from engineering, support, finance and marketing, with the people who own those processes judging the results. Give each model the same material and a clear definition of done. Watch where it asks for clarification, where it acts too soon, what it overlooks and how much work people do after it says the task is complete.

Record the model cost, but also record first-pass acceptance, correction time and the cost of reaching an approved result. A model that saves an expert from rebuilding a difficult deliverable can justify a higher price. A cheaper model that reliably passes the same checks can be the better choice at scale. Different teams inside the same company may reach different answers.

Today, Opus 5.5 has the stronger result on Artificial Analysis’s index, and GPT-6 Sol is markedly cheaper on its measured tasks. That is enough evidence to start a useful comparison. Your business’s own work is where you find out which model deserves the job.