All that keeps jumping out at me is how they've set it to refuse giving users thinking tokens and prompts for full reasoning in output. Just drives me further away; I may not stop using Claude completely for now, but I'll be moving even more of my primary workload to Chinese providers. That's where openness and freedom is now at.
What Chinese models/providers are you using for this? I'm hitting Claude's weekly limits much sooner than I used to with roughly the same workload, so I'm interested in trying alternatives, especially ones with strong coding/agentic performance.
In my experience that tactic works well if the codebase is limited in size, or well maintained and separated. Otherwise I do notice a difference also letting fable do the execution, not just the planning for complex tasks.
In my experience it never works well on any real work. In fact, I'd go the opposite, plan with the dumb model and execute with the smart model because at least the model writing the code and solving the emergent problems is capable.
In my experience (and I've been trying this a bunch): smart planner + dumb executor produces worse code with higher spend than simply using the smart planner to do both.
It's easy to understand why:
- If the planner has truly thought the issue through, properly designed the solution, solved all of the emergent problems, then the final "write" of the code is just a few more output tokens.
- If the planner has NOT truly planned the issue completely, then you're letting a substantially dumber and less capable model make significant decisions, and trusting its problem solving, without having a better model check it.
If you're highly cost conscious (paying for your own tokens and not making any money) then you have no choice but to trade your time and effort for tricks like this to save money by lowering the quality of your output.
But if your employer is paying for tokens: just use the smarter model. You save your time preventing re-work and reducing code review, you save your employer money (primarily from the cost of your own labor and reduced rework), and you get a better output every time (Opus 5.5 mogs Deepseek 4.1 flash in every single way except cost).
The cost is 20-40x less for Deepseek Flash v4.1. If you are just comparing to Sonnet or you aren't paying (your case) then your advice makes perfect sense.
I also agree that its a big mistake to have a flash model implement without a strong model reviewing.
I have Opus plan, Deepseek implement the code, and then review with Opus [1]. In this workflow I am saving a lot of money by having Deepseek do the implementation. Note that the review back-and-forth is fully automated [2], so it doesn't take any extra attention from me.
Deepseek Flash v4.1 is only "40X cheaper" if you do not account for the time of the engineer reading the output. If Opus 5.5 high requires 1/2 of the actual engineer time, and the engineer costs $100-$200/hr, then Deepseek v4.1 is actually the more expensive model to use.
I have tried your workflow many times, and simply letting Opus do the implementation costs much less than wasting hundreds of millions of tokens letting deepseek and opus go back and forth and back and forth. And bonus, my project finishes in 5 minutes instead of 20.
I tried the experiment of reviewing vs. not reviewing with frontier models. I consistently found that reviewing by a model with independent context finds important issues when changes are non-trivial- certainly the definition of non-trivial is getting raise as the models get better.
I do have an /implement-simple workflow to skip the planning phase, but even that doesn't skip the review.
Are you doing your own intensive reviews of the model code? Can you share the prompts you are using as I have?
My bar for what models produce without human intervention is much lower defect than what a human would produce. The human interaction is mostly to guide the design and then the review burden is very low. I suspect your bar for what agents produce is lower- you are taking more of the review burden. I also suspect that you are measuring time more than actual cost since your employer is paying and that you are comparing to Sonnet rather than DeepSeek (DeepSeek 4.1 again is 20-40x cheaper than Sonnet). You mention hundreds of millions of tokens (my reviews don't use that much), but even that costs ~$1 on the DeepSeek side.
I think you are taking exactly the right approach at your employer given the cost is free and you only have access to Anthropic models.
skeledrew · · focus · HN ↗
rajeevk · · focus · HN ↗
arcanemachiner · · focus · HN ↗
A common tactic is to used a big brain model like Opus for planning and reviewing, and a cheaper model for execution.
lukan · · focus · HN ↗
criley2 · · focus · HN ↗
In my experience (and I've been trying this a bunch): smart planner + dumb executor produces worse code with higher spend than simply using the smart planner to do both.
It's easy to understand why:
- If the planner has truly thought the issue through, properly designed the solution, solved all of the emergent problems, then the final "write" of the code is just a few more output tokens.
- If the planner has NOT truly planned the issue completely, then you're letting a substantially dumber and less capable model make significant decisions, and trusting its problem solving, without having a better model check it.
If you're highly cost conscious (paying for your own tokens and not making any money) then you have no choice but to trade your time and effort for tricks like this to save money by lowering the quality of your output.
But if your employer is paying for tokens: just use the smarter model. You save your time preventing re-work and reducing code review, you save your employer money (primarily from the cost of your own labor and reduced rework), and you get a better output every time (Opus 5.5 mogs Deepseek 4.1 flash in every single way except cost).
gregwebs · · focus · HN ↗
I also agree that its a big mistake to have a flash model implement without a strong model reviewing.
I have Opus plan, Deepseek implement the code, and then review with Opus [1]. In this workflow I am saving a lot of money by having Deepseek do the implementation. Note that the review back-and-forth is fully automated [2], so it doesn't take any extra attention from me.
criley2 · · focus · HN ↗
I have tried your workflow many times, and simply letting Opus do the implementation costs much less than wasting hundreds of millions of tokens letting deepseek and opus go back and forth and back and forth. And bonus, my project finishes in 5 minutes instead of 20.
gregwebs · · focus · HN ↗
I do have an /implement-simple workflow to skip the planning phase, but even that doesn't skip the review.
Are you doing your own intensive reviews of the model code? Can you share the prompts you are using as I have?
My bar for what models produce without human intervention is much lower defect than what a human would produce. The human interaction is mostly to guide the design and then the review burden is very low. I suspect your bar for what agents produce is lower- you are taking more of the review burden. I also suspect that you are measuring time more than actual cost since your employer is paying and that you are comparing to Sonnet rather than DeepSeek (DeepSeek 4.1 again is 20-40x cheaper than Sonnet). You mention hundreds of millions of tokens (my reviews don't use that much), but even that costs ~$1 on the DeepSeek side.
I think you are taking exactly the right approach at your employer given the cost is free and you only have access to Anthropic models.
[deleted] · · focus · HN ↗
[deleted]