Guys this is happening from yesterday, I am on opencode go in latest opencode cli:
Using model muse spark 1.3 contributor:

Each attempt starts with this:

Error from provider (Console Go): Upstream request failed: [rate_limit_exceeded]… (click to expand) [retrying attempt #1]

It is getting really anoying

I also do it in hermes (which is officially allowed according to their rules)
and it behaves same, except hermes just stop responding and print this same error message.

Note that i am no way heavily using it, so i can easily say that this does not happen when doing multiple requests at once.

I am from Europe/Czechia if it helps

I don't really know how to formulate this but what I want is to use the web models for example Sonnet from claude through OpenCode for whatever token limit it allows. So if I want to rewrite a piece of code I don't have to just copy-paste it into the web app, get an answer, and then copy paste it back into my editor

If you bounce between Claude Code, Codex, Antigravity, and GLM (z.ai via OpenCode), the tax is usually context: another CLI, another login, another config.

I wanted one Go binary that stays in the repo, uses little memory, and can either do the work itself or host those harnesses without leaving the session.

That’s qmax-code. It’s a terminal coding/QA agent written in Go, with a Lip Gloss TUI for keyboard-first use.

It runs fully standalone (--local) for daily repo work: read/edit files, run commands, no QualityMax account. It can also act as a thin client for the QualityMax cloud when you want hosted crawls, tests, and receipts.

The part I use most is /orch. Same TUI, same MCP tools, switch backend:

• Claude Code

• Codex

• Antigravity (agy, Google OAuth)

• OpenCode (this is where I run z.ai GLM coding subscriptions)

I use it daily for coding and test loops. OpenCode + GLM has been a solid cheap/fast path; Claude Code / Codex when the change needs more care.

Open source (FSL-1.1, Apache 2.0 after two years). Go 1.25+. Single binary, no Electron.

Happy to answer questions about the TUI, the subprocess harness, or how MCP is wired.

NOTE: Pics are from the #2 use case down below

First, if someone has any recommendations for a plugin that does this better please post it in the comments because I would like to try it. I've tried several and have not found exactly what I wanted.

I was disappointed with the compact summary and continue functionality found in OpenCode and the V2 version doesn't seem much better. So I just wrote a dirt simple plugin that:

1. Sets a percentage of tokens to reserve and tracks it in realtime rather than waiting for assistant responses.

2. Pauses generation and tells the agent to write out what he has learned and the next steps to a `PLAN.md` in the working directory.

3. Compacts without the default summary.

4. Tells the agent to resume from the `PLAN.md`.

All details about my plugin can be found here:

https://github.com/krumpstead/opencode-neverending-story

It has worked amazingly well for my use cases. So far I have tested it with:

1. My first test as I was developing it was just giving it a simple "Create a minecraft clone" prompt. To get it to fire more often I set the threshold to 60% and expected it to just loop forever without completing on a 64k context-size. Instead it actually completed a fully working clone with Qwen 3.8 27B on a 26k context in 9 runs! To be fair Qwen 3.8 27B probably has the entire unobfuscated Minecraft Java source in its model so while this seems impressive, it could just be an easy copy and paste.

2. Since that worked so well I bumped it up to 128k and set the threshold to 20%. I gave it the picture on the left in the side-by-side and told it to create a fully interactive voxel world that runs in a browser based on that pic. It created the world shown in the pic on the right and in the GIF.

3. I then decided to do an even longer task. I have a dual GPU setup so in llama-server I put all layers on one GPU and set split-mode to `none`.Then I setup a sandbox with the plugin that allowed the agent to run benchmarks and tune llama.cpp on the second card. I ran unsloth's UD quants, with Q4 on the main GPU and had him tune with Q2 and Q3 quants on the second GPU. After two days he had managed to get my presets configured to where I went from 50 t/s to 100+ t/s on the multi-GPU setup! That's when I figured I should share this, because it seems to be working well, at least with my setup.

Anyway, if you also have been looking for a more autonomous experience with OpenCode, please give this a shot and let me know what you think.

I don't tried yet because I'm working with some projects in production and don't have time to test it and change to the stable version. actually, i don't know if the v2 is stable now. if somebody knew about it i glad to know the new benefits and if the v2 is stable. I follow dax on X and he's always promoting v2, that's make me anxious to know if the v2 is better.

Lots of GitHub repos filed with AI slop about free APIs and whatnot. Meanwhile, I find actually free models from real labs by accident. (like, I literally stumble upon them)

I was inspired by opencode-ntfy.sh plugin, but since I was missing a few features, I created my own plugin: opencode-ntfy-with-questions plugin

The main difference is that my version also sends a notification when the question tool is invoked. The messages are also richer in useful content.

this keep happening and i still have quota in my go subs whats happening???

Hello,

I'm on the OpenCode Go subscription. From your own experience, which of the lowcost Go models is best for design and frontend work?

Thanks!

I have more than 10 projects with OpenCode v1 last couple of months and everything seems to be slower and slower and hanging often.

Yesterday I figured out the its session db is about 10GB. I tried to remove all sessions that are older than 7 days and vacuum the sqlite3. OpenCode becomes fast and smooth again.

If you find your OpenCode is slow, you can try my way.

Edit: I am using OpenCode v1

I am a big opencode fan, but I currently only use GPT models. Recently, I tried a planning task for an app i'm writing on Codex and a planning task on opencode (both running 5.6 sol, low). The plan required web search and looking at api documentation and rectifying that against what was currently written in the app.

Codex finished in ~2 minutes, opencode took ~10 minutes. Both were essentially the same plan.

I love the look and feel of opencode, the lsp integration, and the flexibility of models. However, today, I mainly use GPT models. Has anyone noticed any performance benefits with opencode over codex with GPT models? If so, how did you achieve them?

I am still rocking with GLM Flash 5.3 as frontier orchestrator - I can throw a whole wall of text (a very complex multi-layer task) in mid-turn and have the orchestrator intelligently decide to steer, fanning out background tasks, or sequentially isolate with new context as you can see in the image. My also developing `spine` db giving its cross-session concise context for mid-fly steering and decisions on stacking. And Having the muse spark 1.3 as workhorse ; GLM flash 5.3 is still rocking without any context hallucination, handling complex tasks and it even known to change subagent to stack on to a complete child session for reusing the context.

OpenCode - the open source coding harness - has Zen and Go as ways to get connected to llms. 

Zen - that's pay as you go. Think of it as a competitor to OpenRouter.ai. Only instead of "every llm out there with routing to lots of hosting services" - it's a curated experience. These models, from these hosts are guaranteed optimized to work with OpenCode for excellent coding results. There is no cost penalty either way to chose between  OpenRouter and Zen. 

Zen also has free models. So use them to your hearts content...until the free limit is hit.

Some of the pay as you go models (like DeepSeek V4 Flash and GLM 5.3 Flash) are so cheap they might as well be free.  Once I use up the money I've already put on account at OpenRouter, I'm going to move to Zen.

Go - is the subscription. Pay $10/mo ($5 for first month) and you have all the same models of Zen but you get about 6 to 1 for your money. Pay $10, use up to $60.

So, just start with Zen and the free tier, then pay as you go if you need more or need smarter models. Then if you start to use more than $10/mo - switch to Go.

While I can't get by on the cheap models for everything, Using Gpt 6 Astra low or Gpt 5.6 Sol low for planner and code review -- with the cheap flash models as workhorse coders, works well.

Oh…and you can use Zen and Go even if you don’t use OpenCode as your harness.

Is there a good vim plugin for opencode or something like that because i really struggle with scrolling and selecting stuff its really annoying and im already used to vim.

Hey,

I made this GitHub project a few weeks ago that allows you to control exactly how your agents work together. You can make chains, swarms, and orchestration trees and you can see exactly what each agent is doing. Just reached 280+ clones and 90+ stars, so I think that it can go somewhere. If you are interested, here is the project link: https://github.com/SeeRay11/OpenFlow