/ THE SHORT ANSWER
See the method. Keep the context.
The visual companion
Credit: Original dotSuper research graphic based on cited primary sources.
Reuse: dotSuper original artwork. No third-party product image or logo reproduced.
Read the diagram: The right comparison unit is a completed, accepted task.
CLAUDE 5.5
GPT-6 ASTRA / SOL / LUNA
Compare accepted work, not hype
Thumbnail credit and reuse
Credit: Original dotSuper research graphic.
Reuse: dotSuper original artwork. No third-party product image or logo reproduced.
- 01Match the model to a defined task and decision risk.
- 02Base token prices are not total task costs.
- 03Run a fair pilot with common inputs and review rubrics.
/ dotSuper point of view
Compare the models on the same workflow, permissions, output quality, total task cost and review time. Published API prices describe one part of cost; vendor benchmarks and product modes are not a single head-to-head test.
Start with the job, not the leaderboard
Opus 5.5 is positioned for difficult agentic and knowledge work.
OpenAI positions Astra for the hardest end-to-end tasks, Sol for complex coding and agentic workflows, and Luna for focused high-volume work.
These are vendor descriptions, not a universal ranking.
The popular question “Which model is best?”
is too broad to buy against.
A better brief says exactly what the model must produce, the records it may read, the systems it may change, the acceptable error rate and what a reviewer needs to inspect.
Price is a starting point
OpenAI’s model catalog lists GPT-6 Sol at $2 input and $10 output, and Luna at $0.10 input and $0.50 output per million tokens for standard short-context text use.
Astra is priced above Sol in OpenAI’s catalog.
Tool calls, caching, processing tier, long context and retries can change a task’s actual bill.
On a hypothetical task using one million input and 100,000 output tokens, those base rates produce about $6 for Opus 5.5, $3 for Sol and $0.15 for Luna.
This illustration excludes cache use, tool charges and failed attempts.
It says nothing about quality or how many tasks each model can complete.
Read benchmark tables carefully
The company notes differences in harness, reasoning effort, safeguards and which scores were supplied by another vendor.
Its own release also says narrow benchmark margins are a less reliable guide to real-world difference at this level.
Treat a result as evidence about that test setup, not as a purchasing verdict.
If a vendor reports a large code migration or automated office task, ask what starting condition, tool access, review and acceptance process made the result possible.
Your operations environment may differ substantially.
A fair selection exercise
Record correct completion, source traceability, time to human approval, rework and total cost per accepted result.
Run the same prompt, tools and access policy where possible.
When products have distinct capabilities, describe those differences openly rather than forcing a false apples-to-apples score.
Route routine work to the lowest-cost model that meets your quality threshold.
Escalate ambiguous or high-impact cases to a stronger model or a person.
That policy is usually more useful than declaring one model the company-wide winner.
Where dotSuper can help
We can help a team see where an AI assistant saves effort and where it adds new review work before the organisation commits to a platform.
What this page cannot conclude
- 01Current as of 23 September 2026. Availability and pricing can change. Vendor benchmark results are attributed to their publishers.
- 02Examples describe a proposed evaluation, not a dotSuper customer result or independent model benchmark.
- 03Choose data handling, permissions and human review to fit the actual work and jurisdiction.
Sources
- 01Claude Opus 5.5 launch and evaluation notesAnthropic · accessed Sep 23, 2026
- 02GPT-6 Sol model guideOpenAI · accessed Sep 23, 2026
- 03GPT-6 Luna model guideOpenAI · accessed Sep 23, 2026
- 04OpenAI model catalogOpenAI · accessed Sep 23, 2026
- 05GPT-6 Astra safety overviewOpenAI · accessed Sep 23, 2026
Our editorial standard · Found an error? Send a correction with its source.
/ CITE OR SHARE THIS GUIDE
Make the evidence easy to verify.
When you reference this guide, link to its canonical URL. That gives readers one stable place for the evidence, limitations and future updates.
dotSuper Research Desk. (September 23, 2026). Claude 5.5 vs GPT-6: Which Fits?. dotSuper. https://dotsuper.net/feeds/market-intelligence/claude-opus-5-5-vs-gpt-6-models