Two new AI models have arrived, and apparently my subscription budget was supposed to train for this. My starting point: use Claude Opus 5.5 for demanding, complex work; use GPT-6 Sol for repeatable jobs where cost matters. If you already pay for one service, try its new model before adopting another monthly bill.
Both launched on September 22, 2026. Opus leads the independent comparison discussed below; Sol has lower standard API rates. But “smarter,” “cheaper” and “best for your work” are three different claims—and launch week tends to put them in a blender.
Checked September 23, 2026. This is my analysis of official documentation and independent evaluations, not a TPTK hands-on head-to-head test.
Start with GPT-6 Sol
Use it for repeated research, coding and multi-step Work or Codex tasks. Keep your current subscription and see where it actually struggles before shopping for another one.
Use Opus 5.5 for the hard deliverable
Artificial Analysis’s evaluation favors it for demanding professional tasks. Start here when a missing requirement would mean expensive rework, then check the result yourself.
Give each model the right job
Send high-volume, iteration-friendly jobs to Sol. Reserve Opus for tasks where a weak answer, missed requirement or poor final package costs more than the model premium.
What changes for your paid subscription?
According to OpenAI’s Work and Codex guide, GPT-6 Sol is available in ChatGPT Work and Codex for Plus, Pro, Business, Enterprise and Edu users, not in an ordinary Chat conversation. Work and Codex draw from the plan’s allowance; an API key is billed separately. Sol’s low API price does not lower your monthly ChatGPT fee, although efficiency can mean more work inside an allowance.
Anthropic’s launch announcement includes higher five-hour limits for Pro, Max, Team and seat-based Enterprise plans, plus a reset subscribers can save. Usage varies with prompts, files, tools and model settings, so check Settings → Usage instead of trusting somebody else’s message count.
That makes the first recommendation almost boring, which is usually a good sign. Use the capable model already in your paid workflow. Buy the other service only when a repeatable task shows a meaningful gap, not when launch-week scorecards give you subscription FOMO.
What the independent scores actually say
Artificial Analysis’s September 23 comparison gives Claude Opus 5.5 a score of 58 and GPT-6 Sol 48 on its Intelligence Index at their named max-effort configurations. On that same evaluation, reported cost per task is $5.98 for Opus, $1.06 for Sol.
Artificial Analysis tested Opus with Anthropic’s default fallback enabled. Its index combines 10 evaluations, mostly in English and text, covering coding, reasoning and tasks an AI carries out over several steps. These are scores and costs for that test setup—not prices for your next email or proof of a universal winner.
The separate Sol evaluation found a mixed release: some improvements, but weaker results on certain professional tasks where shorter answers omitted required elements. That is why I would check completeness before congratulating myself on a smaller bill.
There is another trap hiding in the word max. Anthropic’s technical guidance sets Opus 5.5’s default thinking effort to medium and says it thinks more per turn than Opus 5 at the same setting. Recheck old settings instead of automatically turning everything up to max. The same model can look very different when you change effort, tools and fallbacks. Max effort is a test configuration, not a law of nature or a guaranteed value setting.
Here is the apparent contradiction: Anthropic advertises 40% lower typical task cost than Opus 5 at default settings, while Artificial Analysis found roughly the same cost per task as Opus 5 at max effort. Different settings and workloads can produce both results. Neither claim says Opus is 40% cheaper than Sol.
The API math: half the rate is not automatically half the finished-job cost
The API is pay-as-you-go access used by apps and automations; tokens are the pieces of text the model processes. At standard rates, Opus 5.5 costs $4 per million input tokens and $20 per million output tokens. GPT-6 Sol costs $2 and $10. Cached input is $0.20 per million for both, but cache writes, long-context rules, fast modes and other modifiers differ.
Assume each job bills 20,000 uncached input tokens and 2,000 output tokens, including billed thinking tokens, with no tools or special pricing.
$0.12 per job
$12 for 100 jobs
$0.06 per job
$6 for 100 jobs
Because both base rates are exactly 2:1 here, Sol ties Opus on cost if it uses exactly twice as many billed tokens in that same input/output mix to produce each accepted result. That is a break-even calculation, not a claim that Sol really needs two tries. The only number that matters in production is cost per accepted job.
Caching and tools can move the total. OpenAI also applies higher rates above 272,000 input tokens, while batch and fast processing change the arithmetic. This example is deliberately boring enough to audit.
When Claude Opus 5.5 earns the premium
- One complex deliverable carries real downside. A missed requirement in an executive analysis, codebase task or multi-file package creates expensive rework.
- Your work resembles the evidence. Opus’s current advantage is most relevant to multi-step coding, professional analysis and long, structured tasks—not every quick question.
- You can measure acceptance. A higher token bill is rational when the model produces more usable first submissions against your checklist.
Opus 5.5 uses adaptive thinking by default and cannot simply switch thinking off. That can help on hard jobs and can waste money on chores a cheaper tier can finish. Our Claude Opus 5 API migration guide covers the previous generation; recheck every 5.5 behavior against current Anthropic documentation.
When GPT-6 Sol is the practical default
- You iterate by design. Research passes, code revisions, structured extraction and repeatable agent tasks benefit from a lower cost per attempt.
- You already live in Work or Codex. Sol fits that product workflow without adding another subscription.
- Your team can verify outputs cheaply. A lower model bill wins when the acceptance rate stays high and review does not become the hidden invoice.
For more detail on availability, Luna and the GPT-6 pricing structure, see our GPT-6 Sol and Luna explainer. Luna is the volume option for focused work that does not need Sol. Astra is the OpenAI step-up when the task deserves more capability and the added cost or plan constraints make sense.
Copy this three-task test before you change subscriptions
I have not run this test for this article. It is a short protocol you can use with work you are allowed to share.
- Choose three real tasks: one routine, one difficult and one with a strict final format.
- Give each model the same source material, instructions, tools and acceptance checklist. Record the model, effort setting and date.
- Score factual errors, missed requirements, edits needed, elapsed time and total billed usage. Do not grade “vibes” while ignoring a broken deliverable.
- Pick a default only after comparing cost per accepted result. Keep the other model as an escalation route if that produces a better business outcome.
This is also the right way to think about an AI agent for a small business: match the model to the workflow, the cost of failure and the amount of checking a person still has to do.
How I put this comparison together
I started with Matt Wolfe’s video about the two launches, then checked the access rules and prices against OpenAI and Anthropic documentation and the benchmark claims against Artificial Analysis. The video includes creator commentary and demonstrations; those do not establish how either model will perform on your work. The $12/$6 example above is arithmetic with stated assumptions, not a measured performance result.
Spotted a changed price or access rule? Send me a correction with the source.
My recommendation
Keep the subscription you already have and check whether its new model can handle your real work before paying for another. For a Claude user facing a serious, complex deliverable, Opus 5.5 is the evidence-backed first choice. For a ChatGPT Work/Codex user doing repeat work, Sol is the sensible default. For API teams, use Sol broadly, escalate selected high-value jobs to Opus, and let cost per accepted result—not model fandom—decide the routing rule.
Frequently asked questions
Is Claude Opus 5.5 better than GPT-6 Sol?
Opus 5.5 leads the current Artificial Analysis max-effort index, especially in several agentic and knowledge-work measures. That supports choosing it for demanding deliverables similar to the evaluation. It does not prove Opus wins every prompt, workflow or effort setting.
Is GPT-6 Sol available in normal ChatGPT chats?
No. OpenAI says GPT-6 Sol and Luna are available in ChatGPT Work and Codex, with access depending on plan, workspace settings and rollout. They are separate from the models selectable in ordinary Chat.
Does Sol’s lower API price make a ChatGPT subscription cheaper?
No. API billing and ChatGPT subscription billing are separate. A more efficient model can help you get more work from your included allowance. That benefit matters even though the monthly fee stays the same; it is not a promise of a fixed number of extra messages.
Should I always use max effort for Opus 5.5?
No. Anthropic defaults Opus 5.5 to medium effort and recommends recalibrating effort for your workload. Higher effort can use more thinking, time and budget. Test the lowest setting that reliably meets your acceptance checklist.
Sources
- Anthropic: Introducing Claude Opus 5.5
- Anthropic: What’s new in Claude Opus 5.5
- Anthropic: Claude Platform pricing
- Anthropic: Usage-limit guidance
- OpenAI: Introducing GPT-6 Sol and Luna
- OpenAI: GPT-6 Sol model and pricing
- OpenAI: ChatGPT Work and Codex access
- Artificial Analysis: Opus 5.5 versus GPT-6 Sol
- Artificial Analysis: Intelligence Index methodology
- Artificial Analysis: GPT-6 Sol and Luna evaluation