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@tjrobertson52

2026-05-29

AI workflows should keep data structured enough that the team can swap models when quality, cost, or task fit changes.

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8 and it has had a mixed response. 6.

7, and that's where a lot of the criticism seems to stem from. 7 also had a mixed response. 6 for most work.

7 has been overly sterile, takes everything literally instead of applying common sense judgment, and tends to use a lot more tokens.

8 is getting a lot of those same criticisms, but it's also really impressive in some areas. Anthropic only released the benchmarks where it's performing very well, but it does perform well on the benchmarks. Notably, it's the most honest and least lazy model currently.

They specifically designed it to be effective on long running tasks. And so far, that seems like where it really shines.

8 might be the best model available right now. You also have complete control over how much effort it puts in. You can choose between low, medium, high, extra, or max.

And inside Cloud Code there's an extra setting called Ultra Code.

In my early testing, if the setting is anything below high, the results are unreliable. 6. 6 for now.

6. It was already removed from the desktop app. But hopefully Anthropic notice the response from the community and decide to keep it around for now.

8 is also not going to be the smartest model for very long. In that same post, Anthropic announced that a new class of model will be coming out in the coming weeks. And of course we know this to be the methods class.

And of course we can expect methods to be incredibly good at coding. And also incredibly expensive.

It seems like Anthropic is currently all in on creating the most powerful models for software engineering. And it makes sense. That's where all their money is coming from.

And it lets them use the models internally to develop their own product. It seems like they may be leaving the door wide open for ChatGPT to come in and make Codex the go to product for knowledge work.

For now, I think it's just important to remain agile. Make sure your data is structured in a way that you can easily swap out the model as needed.

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AI workflows should keep data structured enough that the team can swap models when quality, cost, or task fit changes.

Model Routing · asserts

  • Design AI workflows around portable structured data and repeatable prompts instead of assuming one model will stay best for every public-facing task.

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What is this source mainly about?

AI workflows should keep data structured enough that the team can swap models when quality, cost, or task fit changes.

What should an operator take from it?

Design AI workflows around portable structured data and repeatable prompts instead of assuming one model will stay best for every public-facing task.

What public evidence supports the record?

For now, I think it's just important to remain agile. Make sure your data is structured in a way that you can easily swap out the model as needed.

How is this source attributed?

Base2026 attributes this public source record to @tjrobertson52, published 2026-05-29, with the original platform link and correction/removal path kept on the page.