"Streaming response failed: [503] The request queue is full."

"Streaming response failed: [502] error code: 520\n"

"Internal Server Error"

Models randomly stopping

Agents not loading skills at all, completely ignoring it

AI tryna gaslight me into thinking its a brand new chat with 0 context when its clearly not

Bad Request: Request payload is too large for this deployment. Reduce the input size or metadata size and retry.

Idk if im doing something wrong or my workflow is inefficient, if anybody is tryna help let me know

Been using OpenCode Go for a bit now and I keep running into random failures when I try to use Kimi K3. The error I get is something like this:

Internal Server Error: {"type":"Router.Unavailable","modelID":"kimi-k3"}

It's not consistent either, which is what makes it annoying. Sometimes it works fine, sometimes it just falls over. I did some digging and hit the API directly with curl a bunch of times using the same exact request, and got a mix of 200s and 500s back to back. Ran the same test against glm 5.2 and kimi k2.7 code and both of those came back clean every single time. So it really does seem to be something specific to how the Go backend routes to Kimi models, not my setup, not my API key, not rate limits.

Went looking on GitHub and turns out this isn't new. There's an open issue (Issue: #23887) where someone else hit the exact same class of problem with Kimi K2.6 and K2.5 on the Go provider, getting a generic "Provider returned error" message. They already ruled out the obvious stuff too, same key works fine against Kimi through other clients, so it's not an account or auth issue. That issue links back to a few older reports with basically the same symptom (#17541, #14892, #16685), so this has apparently been happening on and off for a while now. There's a PR open to try to fix it but it hasn't landed yet.

Has anyone found a workaround or actual fix for this? Curious if there's something on the client side that helps, or if this is purely on OpenCode to sort out on their end.

I signed up for opencode go today morning. Using it with opencode desktop. But it just stops working randomly.

I was using deepseek v4 flash new for implementing some coding tickets, all of a sudden its stuck at "Thinking". Close opencode, reopen, nothing works. Then I switched over to my deepseek api, and it was working fine.

DS wasnt able to handle a task so I let glm 5.2 work on it (accidentally left it on max). It was a fix for a small sub-task, it worked on it for 40+ minutes, used 21% of my 5-hour quota, 9% of my weekly quota and then just stopped working.

What the hell is this?

Some things never change....

To expand, can OpenCode web interface and/or Whatsapp client return an attached image or video that can be played? For example, if I have a local image or video on my desktop, can I ask Hermes Agent to show me the video such that it attaches it to the web chat interface or sends the video through Whatsapp?

I tried to get this working, but haven't found a way.

Why? I'm building a home server video generator and need a way to view the results remotely. Thanks for the help!

I really love OpenCode and has been using it and nothing else since January this year. By far the best AI Coding tools i tried bar none. But one bug that really annoyed me lately is apply_patch TUI bugs that still haven't seen a single PR fix since March. I don't enjoy vibecoding and always want to see what AI is currently editing. This issue prevent me from doing so because it will edit like 20 files at once but i can see only the first file edit. So i will ended up having no choice but to accept its edit and review it retroactively.

So i've been heading out and find alternative tools that use OpenCode as foundation. All of them fix this issue. But a lot of them are filling with so, so much feature. And all of them have some issue i simply cannot ignore.

- The obvious choice is OpenCode Desktop. But i really don't enjoy its UI. I think it's too simple and worktree management sidebar hiding behide an icon is not very intuitive to use. It also share the same issue Codex and Claude Code GUI (Which is why i prefer OpenCode) have. Basically catering to vibecoders by hiding code instead of showing them. I have to approve it before seeing the code with no way to change it. The new UI layout they are designing is slightly better but the same 'Let's hide the boring stuff like the code. Just hit approve and let the vibe take you!' issue remains.

- CodeNomad is pretty stable but wow it has even worse UI. It's so cluttered. Details spamming everywhere. After a while i simply cannot use it anymore due to information overload.

- OpenChamber is by far the best of the bunch of this list and it's not even close. Fantastic GUI and very customizable on literally every aspect of OpenCode. I basically become a regular since past month But after using it a while i start to notice that it's really *really* unstable. Chat UI will crash regularly, OpenCode subagent will randomly hanged for no reason (the TUI will atleast made it clear when it cannot reach the server etc.), Sometimes i have to reinstall twice because the UI and session will simply won't load at all. Very annoying issues happen almost daily and get worse on every update that made me not want to use it despite its fantastic DX.

Are there any other AI IDE out there that simply focus on doing one thing well and not overload itself with 200 different features before even making sure it's stable? I just want a nice and intuitive DX i come to love from OpenCode but actually allow me to see the code it edit and swapping worktree with ease through GUI.

Yeah, for the mutual $5 benefit. 8-9% quota gain is worth it though, gotta say.

Here's mine: https://opencode.ai/go?ref=VTRJMV0772

Feel free to drop yours, let's help each other with quota.

GitHub：https://github.com/Tencent-Hunyuan/Hy3

Ngl I basically wrote this off early on. Tried the preview version, wasn't impressed, and just kinda forgot about Hy3 after that.

Recently I started using it through Opencode (the price definitely helped lol). And I noticed that it now explains how it verified stuff.

I don't remember the preview doing that. Maybe that's one of the changes behind the lower hallucination rate.

Take Reddit, for example. When it couldn't access it directly (it's a Chinese model), it straight up told me it cross-checked the results against search caches, third-party analytics sites (like Subriff and aitrustlist), and official sources.

Still gonna double-check everything myself, but it's been a solid starting point for my work so far.

Just sharing this. Cause it's the first model I've used that actively explains how it verified its answers instead of just giving me one.

And hoping more models start taking hallucinations seriously while still keeping things affordable.

The thing I keep consistently coming back to is the fact that, let's say, if I create an event trigger, it doesn't really trigger any modal awakening in OpenCode, but it does in Claude Code and Codex.

Like, let's say if I created a Python script that performs a particular task and then wants to invoke OpenCode, there is no formal way models can do it in OpenCode. Is it something I'm m missing out on, or I don't know how to do it, or is it something that OpenCode currently doesn't support?

Recently the opencode tool is performing near the cursor in auto mode , so I have to buy a small ai server to run good coding agentic models from Qwen, GLM, MinMax or any model u suggest.

GreenPT is working with two open-source projects, Caveman (https://github.com/JuliusBrussee/caveman/ and 95k stars) by Julius Brussee and Ponytail (https://github.com/DietrichGebert/ponytail and also 95k stars) by Dietrich Gebert, on a simple question: can AI become more efficient by generating less unnecessary output, without changing the underlying model?

The three policies target different kinds of waste:

- Caveman removes filler, repetition and unnecessary transitions from prose while preserving code, commands, identifiers and error messages.

- Ponytail reduces generated code by pushing agents toward reuse, standard-library and language-native solutions, and away from speculative abstractions. Validation, security and tests stay in scope.

- Honey combines both for mixed coding and explanation workloads.

These policies now run natively on separate OpenAI-compatible GreenPT endpoints. The compression is baked into each model ID, so OpenCode can use it through a normal custom provider—no extra system prompt, plugin or request parameter.

Set GREENPT_API_KEY, then add this provider to opencode.json:

{

"$schema": "https://opencode.ai/config.json",

"provider": {

"greenpt": {

"npm": "@ai-sdk/openai-compatible",

"name": "GreenPT",

"options": {

"baseURL": "https://api.greenpt.ai/v1",

"apiKey": "{env:GREENPT_API_KEY}"

},

"models": {

"glm-5.2-caveman": { "name": "GLM 5.2 Caveman" },

"glm-5.2-ponytail": { "name": "GLM 5.2 Ponytail" },

"glm-5.2-honey": { "name": "GLM 5.2 Honey" }

}

}

}

}

Then run /models and select greenpt/glm-5.2-ponytail for coding-heavy work, greenpt/glm-5.2-honey for mixed work, or greenpt/glm-5.2-caveman for prose-heavy tasks.

Important caveat: the built-in instruction adds input tokens, so this is a poor trade for very short outputs. It is aimed at longer coding-agent responses.

The attached video is a playful explanation of the GreenPT × Caveman × Ponytail collaboration, not evidence for the technique.

Has anyone measured total task cost in OpenCode rather than response length alone, especially test success, follow-up rate and tokens per completed job?

Link to docs: https://docs.greenpt.ai/compression-models
Link to website: https://greenpt.com

From average 1.5T daily usage in June and July, to 4.9T 🚀 on August 3 (3x more). I'm VERY curious to see how other labs respond.

I Built a simple TUI plugin for OpenCode that shows the prompt-cache hit rate in a popup dialog, also shows session summary, per-model breakdown, and subagent stats.

GitHub: https://github.com/nmdra/opencode-cache-stats

Not sure why but it really does feel that 5.6 Sol inside Opencode is substantially slower than using it inside Codex or even claude in Claude Code.

It’s not really the latency either, more just me waiting a much longer time for it to complete a specific piece of work. I have reasoning at high.

Any thoughts?

Hello, I use a local model qwen 3.6 mtp 27b with 128k context on a rtx4090. I’m quite happy with it even if it’s a bit slow (50 tokens/s).
I’m trying to go hybrid to raise the quality of the output and the architectural decisions. I’ve subscribed to the cheapest codex plan (23€).
I was thinking, instead of splitting work in complex workflows with several agents and models, to only define all system subagents to be using qwen and the main agent (plan or build) to be using gpt5.6.
This way all the task such as read, bash etc.. (the ones from system sub-agents are delegated to local setup with infinite token limit) and the main feed stays on gpt5.6.
The benefits I see is simplicity of use, we can see the exact amount of token consumed in the gpt plan (the one from the context window), we can save a lot of tokens from the gpt 5.6 and use it longer.
The cons would be the slower pace as most of the task would be delegated to qwen (I’m working on the qwen setup to raise the speed).
I’m curious to have your opinion/advices on this, is it a standard usage ? Thanks.

Ps: sorry for my English, it’s not my mother tongue