[nl]

In practice I don't think any harness (happy to be corrected here!) uses the lesser capability models for writing code. The cost trade-offs are rarely worth it.

They are often used for reading code though.

To expand on this, while the "big model to write a plan, small model to write the specific code" idea is quite common it trips up on edge cases.

In theory the flow works like this:

- small fast models read lots of code, and pass details to the large model to write a plan

- large model takes those details and writes a detailed plan

- medium models write the code

The issue happens when the medium model hits something that the plan didn't take into account (which happens a lot - the big model didn't actually read the code). Then it has to either guess, or pass back to the large model.

If it guesses, the plan usually starts to fall to bits.

If it passes back to the large model, inevitable the large model has to start reading lots of code. In that case you are paying the expensive tokens to read so you might as well have it write the code too (many less tokens are written than are read)

It might be possible to get this to work, but I haven't seen anyone who has tried agentic work with frontier models be satisfied with this hybrid setup.

I'd note that Amp (mentioned above) is probably the leader in using multiple providers in a coding agent but still uses frontier models to write code.

[whatshisface]

> In practice I don't think any harness (happy to be corrected here!) uses the lesser capability models for writing code. The cost trade-offs are rarely worth it.

That's not something I understand very well. The less expensive models will quite happily chug away at tasks, if the codebase is well-structured (small files help a lot) and your instructions are clear. In contrast, I've never seen a large model turn bad instructions (instructions that would cause a human to think before starting) into a result I liked. You can run small models almost 10-100x as long for the same price in dollars, which covers a lot of correction and adjustment.

Why does everyone say the trade-offs are rarely worth it?

[nl]

> In contrast, I've never seen a large model turn bad instructions (instructions that would cause a human to think before starting) into a result I liked

I think the distinction is here.

I expect my agent to build from product level descriptions. This might include specific special cases that I call out, but will rarely highlight existing special cases or edge cases - they already exist in the code, and I'd expect a programmer to make sure that behavior continues to work.

If a feature hits lots of these edge cases, the weaker model that is reading the code (aka Haiku) won't understand their significance, and will report back to the planning model incomplete or incorrect information.

The planning model (Opus - which hasn't actually seen the code remember!) will build a plan that is incorrect or incomplete and delegate coding to the mid level model (Sonnet) which will do it's best to make things work, without understanding the overall picture.

This is how you end up with slop - for example Sonnet reimplements things that already exist because it found one of the edge cases, but Opus had never known about it because Haiku didn't understand it.

It's possible that the new "agent teams" feature in Claude code can help with this. That keeps each agent alive with its context so they can ask each other things, but I haven't tried that enough to be sure - let alone with the specific model mix like this.

In your case, you are giving the Sonnet model specific instructions for what to implement mindlessly. I'd expect that to work well!

But that's not the same as the agentic workflow many other are using.

[sanderjd]

Great info, thanks!