GPT-6 Sol vs Claude Opus 5.5: Which One Costs Less in 2026?

I have a habit that annoys my friends. Whenever a new AI model launches, I skip the announcement video and scroll straight to the pricing table. The benchmarks are exciting for about ten minutes. The price is what decides whether I actually use the thing for the next six months.

This week gave me a lot to scroll through. On September 22, Anthropic released Claude Opus 5.5, and roughly 90 minutes later OpenAI launched GPT-6 Sol and GPT-6 Luna. Both companies cut prices in the same breath. If you build with these APIs, or you are a student trying to keep a side project alive on a tiny budget, this is the most useful launch day in a while.

I have not run these models head to head myself yet, so I am not going to pretend to have hands-on verdicts. What I can do is lay out the numbers from the official announcements and the press coverage, do the maths properly, and tell you what I would do in your position.

The short version

• Cheapest per token: GPT-6 Luna at $0.10 input and $0.50 output per million tokens.
• Best mid-range value on paper: GPT-6 Sol at $2 input and $10 output, exactly half the price of Opus 5.5.
• Anthropic's new flagship: Claude Opus 5.5 at $4 input and $20 output, down 20% from Opus 5.
• The catch: list price is not the same as what you pay per finished task. Caching and token usage change the picture a lot.

What actually launched on September 22

Claude Opus 5.5

Anthropic positions Opus 5.5 as a model that performs at roughly the level of its more expensive Fable 5.1 on most work, while costing less than the Opus 5 it replaces. It has a 1M-token context window, produces output more than 30% faster than Opus 5, and Anthropic says typical workloads cost about 40% less overall because it needs fewer tokens to finish a job. There is also a fast mode that runs up to 2.5 times quicker, priced at $8 input and $40 output per million tokens.

GPT-6 Sol and GPT-6 Luna

These two sit below GPT-6 Astra, the flagship OpenAI released earlier this month. Sol is meant for harder everyday work like coding, code review and data analysis. Luna is the high-volume option for jobs with a clear goal, such as summarising documents, pulling information out of text, or answering quick questions. Both are API models, so there are no weights to download and self-host. OpenAI told VentureBeat that these prices are permanent, not an introductory offer, which matters because the older GPT-5.6 prices were promotional.

API price comparison (per 1 million tokens)

Prices were checked on September 24, 2026. They change often, so confirm on the official pricing pages before you commit to a budget.

What a real month costs

Price tables are hard to feel, so here is a simple example. Imagine a small project that uses 10 million input tokens and 2 million output tokens in a month. That is roughly a content tool that summarises, tags and reviews drafts all day. These numbers are uncached, which is the worst case.

To see it in rupees, multiply the dollar figure by the current USD to INR rate. The point is the ratio. Luna is a rounding error next to the others, and Fable 5.1 costs a hundred times more for the same token volume. Whether that gap is worth it depends completely on what you are asking the model to do.

Caching is the part people skip

If you only remember one thing from this post, make it this. Agents and coding tools resend the same big chunk of context again and again: your system prompt, your files, your instructions. Most of the bill in those workflows comes from re-reading that context, not from the new words the model writes.

Both companies attacked this. Anthropic dropped Opus 5.5 cache reads from $0.50 to $0.20 per million tokens and cut cache writes from $6.25 to $5. OpenAI offers a 90% discount on cached reads for GPT-6 and says it can keep cached context alive even when you change reasoning effort or toggle tools. With caching on, the raw price gap between Sol and Opus 5.5 gets much smaller, because their cached read rates end up in the same place.

The practical lesson: structure your prompts so the long, unchanging part comes first and stays identical between calls. That single habit can matter more than which model you pick.

Read the benchmarks with a pinch of salt

Both companies published charts showing their model winning. That is what launch charts are for. A few things worth keeping in mind:

• They mostly use different benchmarks. Anthropic highlights Terminal-Bench 4.0 and FrontierCode. OpenAI highlights AutomationBench, DeepSWE and Agents' Last Exam. You cannot line those numbers up against each other.
• Anthropic itself says that at this capability level, a few points on a benchmark rarely translate into a noticeable difference in real use.
• Some independent coverage, like heise, points out that the GPT-6 Sol and Luna release is mainly a price story and that the jump in raw performance is small.
• Cheap per token is not always cheap per task. A model that needs more retries, or writes far more tokens to finish, can cost more in the end.

My rule: trust benchmarks to tell you a model is in the right league, then test it on your own work. Ten of your own real tasks will tell you more than any leaderboard.

Which one should you pick?

If you are a student or building a side project

Start with GPT-6 Luna for anything simple, like summaries, extraction and quick Q&A. At these prices you can experiment freely without worrying about a surprise bill. Move up to Sol only when Luna starts failing on your tasks.

If you are shipping a product with real users

Run Sol and Opus 5.5 side by side on a sample of your actual traffic. Measure cost per completed task, not cost per token. Turn caching on before you compare, otherwise you are comparing the wrong numbers.

If you run long coding or research agents

This is where Opus 5.5 is aimed, and where the fast mode and cheaper cache reads help. If your agents run for hours, test it against Sol on a real repository. For a broader look at coding tools, see my earlier post on Claude Code vs Cursor vs OpenCode.

What I would do this week

Honestly? I would not switch everything on day one. New models often ship with rough edges, and Anthropic has said Claude Sonnet 5.5 and Haiku 5.5 are coming within a few weeks. If you are mid-evaluation, waiting could save you from doing the work twice.

What I would do right now is pick one real task, run it on Luna, Sol and Opus 5.5, write down the cost and how much editing the output needed, and set a calendar reminder to redo the test when the 5.5 Sonnet lands. It takes an evening and it will save you money for months.

The bigger story is that prices keep falling while quality keeps rising. I wrote about that trend earlier in LLM Trends 2026, and this week is a very clear example of it. If you want a wider map of tools beyond APIs, my guide to the best AI tools in 2026 covers every category.

Frequently asked questions

Is GPT-6 Sol cheaper than Claude Opus 5.5?

Yes, on list price. GPT-6 Sol costs $2 per million input tokens and $10 per million output tokens, which is exactly half of Opus 5.5 at $4 and $20. With caching enabled the gap narrows, and the real cost per task depends on how many tokens and retries each model needs.

How much does GPT-6 Luna cost?

GPT-6 Luna costs $0.10 per million input tokens and $0.50 per million output tokens. OpenAI cut it from the GPT-5.6 Luna rates of $0.20 and $1.20. Cached input reads are discounted by 90%.

How much does Claude Opus 5.5 cost?

Claude Opus 5.5 costs $4 per million input tokens and $20 per million output tokens, a 20% cut from Opus 5. Cache reads are $0.20 per million tokens and cache writes are $5. A fast mode is available at $8 input and $40 output.

Is Claude Opus 5.5 better than GPT-6 Sol?

It depends on the task. Each company reports wins on its own chosen benchmarks, and the tests are not the same, so there is no clean head-to-head. Opus 5.5 is aimed at long-running coding agents and knowledge work, while Sol targets everyday professional tasks at lower cost. Test both on your own workload.

Are the GPT-6 Sol and Luna prices permanent?

OpenAI told VentureBeat that the Sol and Luna prices are permanent and not promotional. It is still smart to review pricing regularly, because some other providers use teaser rates that rise later.

What is prompt caching and why does it matter?

Prompt caching stores the repeated part of your prompt, such as a long system prompt or a set of files, so you pay a much lower rate when it is reused. For agents and coding tools that resend the same context on every call, it can cut a large share of the bill.

Which is the cheapest OpenAI or Anthropic model for a beginner project?

GPT-6 Luna is the cheapest of the models compared here. Anthropic has said Haiku 5.5, its fastest and cheapest tier, is coming within a few weeks, so it may be worth checking that before you settle on a provider.

Can I self-host GPT-6 Sol or Luna?

No. Both are API-only models with no downloadable weights. If you need to run a model on your own machine, you will need an open-weight model instead.

Sources and where to verify

• OpenAI: Introducing GPT-6 Sol and Luna
• VentureBeat: Claude Opus 5.5 release and pricing
• TechCrunch: OpenAI launches GPT-6 Sol and Luna
• The New Stack: Opus 5.5 release details

Last checked: September 24, 2026. AI pricing moves fast, so always confirm current rates on the official pricing pages.