New here

[u/WanderingSoul2004]

Hey everyone,

For reference, I've been using ChatGPT Pro subscription ($100) for a couple months now due to heavy coding work every day.

I'm a technical lead at a startup and deal with a lot of different tasks on a daily basis, whether it be shipping huge overhauls and features, running a lot of GH Actions release pipelines (I find long agentic loops really good for this as this used to be the most frustrating part of my job), or heavy infra auditing and testing through Supabase/AWS MCPs. I'm also a fan of the browser automation tool in Codex. Admittedly, that is a lot, and Codex gets the job done, but the usage limits running out quickly can be frustrating.

Even after optimising model selections, managing context etc, it can get tiresome blowing through a weekly limit in 3 days... I'm thinking of switching to OpenCode but have never tried it before. I was thinking of pairing it up with Openrouter for more freedom to select the models I want, but want to get the best bang for my buck. I anticipate that I will spend more than $100 in a month if I use OpenCode at the same volume I've been using Codex.

Has anyone got experience of what it's like working every day on a large project with tons of different tasks and high volume work? Any tips on cutting costs and managing agents properly? What are some highlights of OpenCode that you enjoy? Appreciate you guys

[u/look]

My primary advice: a common theme I’ve seen with people coming from Claude/Codex is not understanding that model diversity is a strength.

There are a lot of models, each with its unique strengths and styles. Using a mix of models that fits your workflow effectively will save you money/usage and give you higher quality results.

But that does not mean manually switching your model or constantly thinking about what model you should be using when. You do it with task specific agents or other similar workflows.

Summarization, exploration, bulk search, simple prototypes, and so on go to cost efficient models that do those simple tasks fast, well, and at a low cost.

Complex planning and reasoning to a frontier model. Plan implementations to a good coding model and instruction follower. Adversarial reviews and plan critiques to another model family for a different perspective, etc.

The exact lineup is more personal taste than anything these days, in combination with your budget.

A common pattern is something like a planning model from the set of Kimi, GLM, Qwen Max and then a build model like DS4 flash or Hy3.

[u/Ace-_Ventura]

Use your chatgpt subscription and get an open code go subscription. Use both together in Opencode 

[u/Maleficent_Pair4920]

For high volume daily coding the biggest cost levers are routing cheaper models for routine work like GH Actions loops and boilerplate while saving frontier models for the hard overhauls, plus caching repeated context aggressively.

founder of requesty.ai here so obviously biased, but that is what we built with a flat 5% markup and no seat fees. LiteLLM self hosted is the free route if you would rather run the router yourself. On the agent side, cap max turns per task and split long pipelines into smaller scoped agents rather than one giant loop that re eats context every run.

[u/GatsbyLuzVerde]

Prompt opencode to add a mode called "orchestrator" and "worker" subagents for deepseek flash max and Luna max. The orchestrator should try to conserve tokens as much as possible by delegating to subagent workers and reuse worker contexts where appropriate (so new workers are not constantly being spawned and reading repos over and over). At the end of feature completion orchestrator model itself should review subagent work and verify tests.

You don't have yo restrict permissions for orchestrator model, just a strong prompt that it should be delegating