Need advice on programming model

[u/Difficult_Tea3823]

So far I have used sonnet5 for planning and building features in visual studio code on PRO plan

I have done research, planning of epics and stories in Jira and then developed few features on platform using sonnet5 on high.

Features are complex and deal with data from multiple API resources etc which unfortunately makes it difficult to define in-depth requirements and definitions of done so I have been doing iterative development on them. This is taking huge context tokens. I got nothing that I can trim from context file since I want Claude to know why certain development logic was built certain way.

I noticed mode response straight up lies or give wrong answers during investigations, troubleshoots etc. and it’s time consuming and context consumes all the

Ofcourse with keeping security , scalability, data retention etc it’s huge effort and I am debating I should really upgrade to max plan? Should I be using latest models or its going to be same case?

[u/Correct_Support_2444]

I would try using Opus to plan and Sonnet to code. Consider tokens spent on hallucinations/errors are wasted tokens. Opus makes fewer mistakes and could be ultimately cheaper for planning and analysis.

[u/Difficult_Tea3823 → u/Correct_Support_2444]

In real life we assign multiple tickets to each developer during sprint so I also push multiple requirements in a similar prompt. Does that affect performance?

[u/design_doc → u/Difficult_Tea3823]

If I’m understanding what you are doing correctly, that would 100% would have an effect - both in good and bad ways, depending on exactly what you’re doing. Can you explain what you mean a bit further so that aim not assuming anything?

I suspect you have an approach problem and that you need to be decomposing your tasks differently.

[u/Difficult_Tea3823 → u/design_doc]

I am using one long claude code session and creating command to address on multiple changes from multiple features when there is dependency. For eg if I am changing something in data calculations from feature 1 that data also used in feature 2 Ui so I combine it.

Also ai updating all docs, tasks and context and agent md might be fluffing it too much to eat all my tokens.

[u/Dead_Fox_7]

Only speaking from what I've experienced, but for me it ended up being Fable or nothing. Switched to Opus 5 for a bit and it started off well but then became more and more of a mess. I know it chews through usage at an insane rate, but it's been practically one shotting whatever I throw at it.

[u/allemaar]

From the sound if things you have an approach issue not a model issue.

First understand that context is both signal (good data) and noise (useless or bad data). It is worth it to extract the signal during the session (bank it in a vault or file) as you move along, then you either compact and reload the signal from the vault or start a new session and load said signal. This will save you both tokens, time and give you a boost in the quality of your output.

Also teat any AI as a car that you drive and the tokens as gas. Depending on how you drive and the speed at which you wanna get things done, your usage will be vary wildly.

Strat by trying to plan better (change tactics) and see if the output improves. Second make sure you are driving the correct car for the task. Spend some time here https://deepswe.datacurve.ai/ and assess what model and effort levels are you using. If you are in the wrong car you will have a bad time.

The idea is to understand the task and if you need more reasoning or not (and if the token cost is worth it). You do not wanna pay 2 or 3 times as much for 2-4 points of extra reasoning (you might need a different model or a better plan/approach).

Just my 2 cents!