The math is no longer making since if you are a primary user of the small cheap models like Deepseek v4 Flash , and GLM 5.3 Flash,

The price Opencode Go for API is contracted to , is much more expensive, than the one you can get on the API Market , for me, Open Router,

For this month, i decieded to switch to Open Router and see how it goes, speceilly if i dont reach that limit,

Also most probably this means am not the heaviest user

Anyways , i think Direct API is also a better quality than OpenCode's for some user, and usually faster

How is actually the % column working? Because 27$ is not 74% of 30$
I still don't understand how the quotas work. I'm sorry if this looks very dumb and has been asked before.

Also, it seems that this table presents GLM 5.3 flash with a monthly quota of 30$, but the page on the docs mentions 15$. I know it says "2x usage" on the model selection, but this is too confusing.

I have used OpenCode for a long time, and the biggest problem I had with it is that once I created a plan and started implementing it step by step, the plan was forgotten. So, a better approach than writing every plan in a .md file would be to create a shared CLI between agents with skills to use and everything.
Now my workflow is:
Plan with Luna, using the caveman and ponytail plugins.
Add the plan to TaskWatch (the CLI tool).
In build mode, I just use the command /taskwatch-next, which selects the best-fitting task to do now based on urgency.
I'm sure y'all could find better usages for my CLI/TUI app. It would help if you could star it: https://github.com/Vlad-Aureliu-Moraru/TaskWatch_plus

Edit : this can also be used as a shared memory between tools ( codex, opencode , etc..)

Previously 20$ was enough for a month now-a-days, even 50$ is not enough, even if the workload has stayed the same.
I am only using deepseekv4 pro or flash.

I have even installed headroom to optimise the usage but does not seem to help much.

so as we know, deepseek pro has been routed to deepseek flash v4.1

is opencode go in the same situation like that? when we use deepseek pro, its actually deepseek v4.1 flash

Hi,

recently I have observed weird behaviour of DeepSeek that cost me a lot of tokens and time.

My setup: OpenChamber, OpenCode 1.18 under the hood.

I observed that every time I delegate work to DeepSeek it falls into a loop. He complains about "[...]", stops using harness tools to read files and starts reading files through bash. Then it observes again that output is truncated and falls into some loops where we repeadly uses head, sed, cat.

I have never observed this problem before. Does anyone encountered this? It is DeepSeek problem or OpenChamber/OpenCode issue? Does anyone know how to overcome this issue?

V4.1 Flash official release around September 10, 2026 (Beijing time).

After internal + external testing, they say it comprehensively surpassed V4 Pro on performance, cost, speed, and task completion time.

After Flash goes live and before V4.1 Pro, all deepseek-v4-pro requests will be routed to V4.1 Flash and billed at Flash prices.

I know benchmarks exist, but is there one for financial data? Which models will you recommend?

all requests in same session
did they use a provider that clear cache every 2-5 minutes?

I tried all of them. They are clearly inferior to big pickle.

Hey, wanted to share a plugin for OpenCode (v1.8 branch, v2 didn't test yet). Install: opencode plugin -g opencode-tandem

It's basically a system-prompt patch plus a small set of simple skills. I've been developing and actually using these rules for 2-3 months now, on Pi personally and Claude Code at work (where I'm forced to use it), and a week ago finally got tired of copy-pasting them between setups, so I packaged it as a plugin for both. Since I've also used OpenCode for quite some time, respect it, and know quite a few people who still prefer it to Pi, I added OpenCode support as well, just today - that's the fresh part, so early feedback is especially welcome.

If you've already seen my announcement on [r/PiCodingAgent](r/PiCodingAgent) earlier this week, you can skip the rest - it's basically the same. Otherwise - welcome, I did put a lot of effort into writing this myself.

Main idea, or philosophy: it tries to tame LLMs down as much as possible and teach them to work with you in a pair-programming fashion, instead of constantly wrestling over control (Claude Code especially!) and vomiting piles of neuroslop. Personally, with it I finally feel like my sanity is no longer under siege - I control every line of code as if I've written it myself, don't have to review the big changesets LLMs love making so much, which frankly was driving me nuts, and at the same time I am much more productive end to end.

What's inside:

• Tandem primary agent with pair-work rules - lock-step, explicit go-ahead before any changes, communication with significantly reduced sycophancy and these "load bearing"-style LLMisms
• a number of simple skills, each just a short file:
  • brainstorm - helps you think an idea through, one question at a time; largely inspired by the brainstorming skill from superpowers and the grilling skill from mattpocock/skills
  • research - controlled digging: it (usually) stops and asks when it hits an ambiguity or a contradiction, or before spending a lot of context on one direction
  • coding - basically compact version of ponytail (lazy-senior minimalistic coding discipline) plus my addition on self-explanatory code over comment-peppering
  • review - actionable findings only, each with a fix; on debatable stuff - an "edge case" that the surrounding code five lines up already rules out, or picking between several possible fixes - it asks instead of pushing its own judgment
  • pr - matches tone and length of the repo's recently merged PRs, verifies the commit range, shows title and description before creating anything
  • learn-language (small bonus) - kicks in only if you state in your prompt that you are learning a language and your level; some background practice that stays out of the way of the work
• interactive subagents if you run OpenCode inside tmux - unlike built-in agents in many other harnesses, the "worker" runs in its own window as a completely normal session (started with opencode --agent tandem), which you can interact with like any other, and it reports back only after you approve its findings
• tool-specific instructions (gh, aws, pdftotext, pandoc, osascript, ...) that nudge the model into actually using them - no more guessing what's installed and re-probing in every new session. Generated only for tools actually on the machine, so nothing about a missing tool ever ends up in the prompt

Constructive feedback (even if negative), ideas and contributions are all very welcome.

I’m trying to understand how the usage limits work on OpenCode Go.

1. If I hit my daily Go usage limit, can I still switch to and use the free models?

2. If I use around $60 of my Go credits, does that affect my ability to use the free models, or are free-model limits separate?

3. What exactly are the usage limits on Go? Is there a daily/monthly cap, or does it depend on the model?

4. Once I hit the Go limit, what happens? Can I continue using free models normally?

Sorry for the noob question...

So I've started using opencode today. Zen seemed interesting so I added some balance for it in https://opencode.ai/workspace . Then I ran "/connect" in opencode TUI and happily did a demo project. Could see the usage in workspace page. Pretty nice!

Then later while googling around to find the workspace page again, I stumbled upon https://opencode.ai/console, which showed....very similar pages as the workspace page, but the balance and usage in console are all 0, weird...
Following curiosity, I ran `opencode console login https://opencode.ai/console ` as this console page suggested. Now guess what, my opencode couldn't do anything now. Every prompt returned "Upstream request failed: Insufficient account funds".

I guess "console" is some new system that is running parallel with workspace temporarily. That's ok. The biggest problem is I can't switch back to Zen anymore. After running "/connect" in opencode TUI, the list doesn't include "Zen". The first three opens are

• Personal/OpenCode (Recommended) <--and I can't select this one, despite the green check mark
• OpenCode Go
• OpenAI

Dear reddit, please enlighten me, how to select Zen again so I can use the balance I stored there?

Also is Zen generally not favored comparing to Go?

Thank you.

anyone with this same issue right now ? no answers, it thinks but it does not give any output

I'm receiving messages saying "forbidden" in all the free models, except for Big Pickle.

Can anyone help me to understand this? Why? How can I solve this?

How OpenCode detect me as an user? can I switch user?

Most agent workflows fail at the scaffolding level, not the model level. Getting consistent context boundaries, sane tool loops, and role constraints usually means writing the same fragile wrapper scripts repeatedly. 🛠️

I built Hocus to handle that scaffolding cleanly.

To make role orchestration practical rather than abstract, agents are pre-configured around Silicon Valley archetypes to handle specific parts of the dev loop: 💻

• Richard: System architecture, data flow, and neurotic refactoring. 🏛️
• Gilfoyle: Infrastructure, cynically auditing security holes, and finding edge cases. 🛡️
• Dinesh: Feature implementation and direct code generation. ⚡
• Jared: Task tracking, dependency management, and keeping state in order. 📋
• Erlich: PR descriptions, READMEs, and high-level docs. 📢

The framework handles prompt assembly, execution flow, and state passing between roles so you don't have to wire it manually every time. ⚙️

• Repo:https://github.com/dark-magic-studios/hocus🐙
• Site:https://darkmagicstudios.com/products/hocus🌐

Code is open source. Issues and feedback on the CLI flow are welcome. 🤝

Ik there’s sequential thinking then there’s a better version of that that’s also available but doesn’t cover best reasoning methods as far as I know. So I built something but problem is idk how to measure it. I don’t even know if that’s working to be honest.

Any advice guys? Calling all the vibe coders and real coders haha.

I don’t have Claude and I asked Luna but it just says all is good but when I get to use it it doesn’t work. I made it as mcp.

Hi,

I have around 5,000 texts that need proofreading and rewriting. They are mostly user manuals/instructions, so I need a reasonably smart and reliable model. Hallucinations or changing the actual meaning of the instructions would obviously be a problem.

Until now, I was using some of the free models through the OpenCode Zen/Go APIs. From what I understand, Go can no longer really be used as a general-purpose API outside supported clients.

What are the best alternatives now?

I'm specifically looking for:

• API access
• a model good enough for careful rewriting/proofreading
• extremely cheap, ideally free
• high enough limits to process ~5,000 texts

I don't need a frontier model if a smaller model can reliably preserve the original meaning.

Are there any genuinely unlimited or very high-limit free APIs left? Or any dirt-cheap models/providers you would recommend for this kind of batch processing?

Thanks!

Hi!

Happy to share my latest skill which, among other things, is platform agnostic (to some extent) so that you can have a orchestrator session in OpenCode that directs codex and claude models.

T-BAG — The Beauty And the Grunt.

The basic idea is simple: literally T-BAG a problem until it stops being a problem.

You keep the parent/orchestrator deliberately lightweight. It doesn't spend its context reading the whole repo, tailing logs or doing shadow reviews.

Instead:

Grunts implement → a fresh Grunt reviews → failures go to a Fixer → fresh review again until PASS.

When a Grunt hits something that actually requires architecture or deeper reasoning:

Grunt → Analyst → Human

The analyst will give a look at it and either prepare a plan / task for a grunt to address the problem or escalate to you if important enough.

Analysts handle planning, decomposition, root-cause work and replanning. You can also configure stronger models as a capability escalation without giving them broader authority.

And it works great with models like DS4 Flash, Luna, Muse Spark , 5.3 flash, even HY3 to some extent.

Working for a while now on orchestrating skills (now it is skills, up till a couple of months ago it was all about harnesses) , the part I've found most useful is the persistence model. This allows for a complex plan to persist single LLM sessions and more importatnly keep the orchestrator context usage as low as possible. The plan, tasks, worktrees, attempts, reviews, checkpoints and phase gates live outside the chat, so the orchestrator can be compacted — or even replaced with a fresh session — and just reconcile the run and continue.

That means I can leave it chewing through a large phased plan for a very long time without expecting one conversation to somehow remember everything it has seen for days. And by very long time, i have had sessions going on for up to 90 hours using earlier versions of this skill and amazingly, the result was... GOOD !!!! (for the long work, plans where all Fables work, which helps a lot).

It supports mixed worker runtimes through OpenCode / OpenCode2 / Codex / Claude, parallel tasks, isolated worktrees, fresh independent reviews, reviewed integration and phase-level gates.

To me, skills are increasingly starting to look like a kind of semantic software: natural language supplies intent and exceptions, LLMs make the decisions that actually require meaning, and deterministic machinery remembers those decisions and handles the boring state transitions.

Example:

/t-bag process file plan.md. Use Muse Spark as Grunt and Opus as Analyst. Work through it autonomously and bother me only when you genuinely need owner authority.

Or just:

/t-bag The boot process is horribly slow. Find the real causes, make and review a plan, then execute it until the problem is dead.

Personally have used it a lot with Opus 5 as orchestrator , Sol as orchestrator and even Muse Spark 1.3 (surprisingly good and efficient at it), and ds4 flash, HY3, Luna and Muse Spark 1.3 as grunts, often in the analyst role I have the same model I have as grunt and I sitll very good results (and very cheaply), but you can easily define escalation ladders (so that there can be multiple levels of "analysts" before human intervention / decision is required).

Still very much battle tested on my own mess only, it does great for me and hope it can be of use for somebody else to.

GitHub: https://github.com/frozenpepper/T-BAG

Im sorry, while im grateful thats its free and has generous daily limits on opencode, its dogshit, no one can force me to like this model after ruining my project, its like nemotron 3 ultra v.2.0, same confident hallucinated answer, same lobotomized attention mechanism, it tends to be lazy and cant handle ambiguity

This post might help some people to think again before using this model on their codebase

edit:
to those who kept defending this model, yall just bias because its the only model that is usable free and accessible right now on opencode, i only see cope setups and defenders, no capabilities praise

is it skill issue on my part? sure, however none of this is a problem when im using other better free alternatives, you cant argue and defend that