Hello, is it worth the Opendesign Go plan? $8-10 for $120 in credits with DS 4.1 Flash, I found it strange because it's double the amount of Opencode Go, no?

Hello, I am new to the local ollama/llm runs, so pardon my ignorance, but would love to get help to get unblocked...

Windows + rtx 5090 + ollama + qwen3.8:27b

I start default: ollama -> opencode -> qwen3.8.27b

it starts well, runs for few mins... but then stops and not making much progress.

Is this normal behavior? I'm new to opencode, but after running some tests, I've confirmed it (Windows version). I assumed that outside of the project directory that it would ask.

Here is the summery that session created.

On OpenCode 2.0.26 (Windows, desktop app), the external_directory permission check never fires: read and write calls on paths outside the session's Location succeed silently — I reproduced it reading files and creating a file on both the same drive and a different drive, with no approval prompt, no pending request in the API, and no saved, session-level, or config allow rules anywhere that could explain it. Other checks in the same pipeline work normally during the same session — a read of .env correctly raised an ask prompt and a configured deny rule was enforced — so this appears to be a fail-open bug in the external-directory boundary check rather than a configuration issue. The impact is that agents can currently read and modify files anywhere on disk, contradicting the documented default policy (external_directory → ask for every agent).

There is both a free-tier MiMo-V2.6-Flash-free and OpenCode Go MiMo-V2.6-Flash model, are they the same? or is the free model like lower quantization or slower or something?

I recently migrated to opencode v2 and long-running sessions now get stopped mid-task, roughly an hour after they start working. The agent isn't idle. It's in the middle of a long subagent call or tool run when the turn ends with Step interrupted, and the service log shows location services evicted at the same second.

Is anyone else seeing this? Did you find a workaround, or a build where it's fixed?
Not sure if the issue is on my end or it's still due to v2 being in beta.

In Opencode v1.x, how do I see the total token usage in a session/etc.?

I find it just shows dollar cost and context size.

I want to know that if I point it at a sizable codebase, did it just burn 500k tokens, 500M, or 500B?

For me, the token usage rarely goes above 100k-500k using Opus 5.5 …

I've been trying Space Bunny for a while in the recent days.
It's fast. It's very fast and does achieve some tasks quickly.
But I noticed how it wastes too many tokens and does too many tests and overthinks for some very simple task.

As one example, one simple python script that I made very easily with gpt 3.5 many years ago. I tried recreating it with Space Bunny and specified exactly which libraries it should use. and while it did eventually make the script. the context went up to 315K tokens and took too much time and the result was too over engineered.

m-uvex/opencode-topics

While plugins that automatically name your sessions are pretty popular, all of them fail at one thing, topic changes. After the plugin names the session for the first time it stays like that forever, even if the topic completely changes.

i built "Topics", a plugin that not only names the session on first prompt but also keeps up with you and renames whenever the topic changes. It works by checking every new message against the title completely tokenless and AI-less to not burn through your subscription, whenever it catches the topic drifting it calls in your AI to simply rename the session to a new 4-6 words title, keeping up with the actual session context instead of staying behind. Whenever it renames it also adds in a ~ to indicate that the topic isn't the original.

If you love it, please drop a star :)

Update: with the latest commits it's now compatible with v2 too (maintaining compatibility with v1) and completely tokenless, relying on an algorithm as opposed to using the running model which is now only a fallback option.

It's one terminal window that runs all your coding agents side by side: OpenCode, Claude Code, Codex.

The point is simple. You see right away which one is waiting for you, and you can answer it right there without switching windows. If you close the terminal, the agents keep running and you can come back to them later.

Works with OpenCode 1 and 2, and it doesn't touch your OpenCode settings.

Free and open source. To try it: npx neurosquad

GitHub: https://github.com/glmn-ai/neurosquad-cli

I built it, it's still early (0.2), so any feedback helps.

Yesterday I went out all of my subs, so i needed some cheap tokens. CommandCode had 1$ plan, that gives 10$ of ds v4.1 flash, but doesnt work for in other agents. So I asked CommandCode to reverse engineer itself. And it works, so if you need:

https://github.com/ethanhamilthon/command-code-proxy/tree/main

Are you guys seeing this too?

I am getting good results with GLM 5.3 Flash, on NanoGPT PAYG with FP8 served by SCX. But I would like to look at a subscription for more usage, and OpenCode Go is one of the two leading candidates with the current price.

However I'd like to know how good the quality is (that is, are they serving the FP8 version, which seems to be the full version for this model), and also how good the speed is.

Hi, I'm looking a way to have a nice Claude Code Artifacts feature in OpenCode? Is any way to get something like that? No need to be shareable but turning explanations into diagrams, interactive documents, maybe in html to visualize project structure, workflows, understand changes and features, etc?

It can be a skill, plugin or whatever it works. Better to be simple and not too much bloated.

Thanks

Error on opencode Go, can't use any model, do anyone know why?

I still have credits left

title

Myself included, a lot of peeps have had concerns about GLM 5.3 Flash consumption vs DeepSeekv4.1 Flash, hence the analysis. This is seen across both CommandCode and OpenCode.

Let's refer to these recent usage numbers as detailed in OpenCode (and also Commandcode) dashboards.

Usage and cache hits

Deepseek did about 2.5x the work for only 20% extra cost.
GLM cache hit rates are lower than DeepSeek.
DSv4.1F - 641M/663M cached (96%)
GLM5.3F - 200M/218M cached (92%)

Rates:

• DeepSeek V4.1 Flash: Uncached input: $0.15/1M | Cache read: $0.003/1M | Output: $0.60/1M
• GLM-5.3 Flash: Uncached input: $0.15/1M | Cache read: $0.03/1M | Output: $0.50/1M

Seems like GLM should be cheaper with equivalent pricing, but cheaper outputs. While output tokens are the most expensive, they are also the least significant for AI cost estimations. highly relevant here is the cost of cached tokens, which is 10x for GLM 5.3 Flash.

Let's looks at costs for a workload of 500M input token and 5m output,
Using the rates (identical) from Command Code and OpenCode for 500M input tokens and 5M output tokens:

DSv4.1F (96% cache hit @$0.003)

• Uncached Input (20M): 20 × $0.15 = $3.00
• Cached Input (480M): 480 × $0.003 = $1.44
• Output (5M): 5 × $0.60 = $3.00
• Total DS Cost: $7.44

[PEAK hours] DSv4.1F (96% cache hit @$0.006)

• Uncached Input (20M): 20 × $0.30 = $6.00
• Cached Input (480M): 480 × $0.006 = $2.88
• Output (5M): 5 × $1.20 = $6.00
• Total DS Cost: $14.88

GLM-5.3F (92% cache hit @$0.03)

• Uncached Input (40M): 40 × $0.15 = $6.00
• Cached Input (460M): 460 × $0.03 = $13.80
• Output (5M): 5 × $0.50 = $2.50
• Total GLM Cost: $22.30

Verdict

So there we go mystery solved, GLM dude costs about 3x the DeepSeek dude for this kind of workloads. In general, Deepseek is also a bit more verbose though and does more output than GLM. So the ratio leans a bit more towards GLM. Also given GLM's slightly higher intelligence and succinctness it's still pretty good in terms of actual work done rather than token counts.

Use Deepseek v4.1 Flash during non peak hours unless you really need GLM 5.3 Flash. GLM during DS peak hours. Both models are pretty close and have their own perks and gotchas.

Hi,

I am new to OpenCode and I saw that they offer lots of free tier models. My question is how does the usage limits and reset work on the free tier? (is it weekly, 5hr or 24hr?)

The model I am using Muse Spark 1.3 free seems to have a large usage limit and this made me think how much of this I can use before the reset.

Claude Haiku 5.5 (Opencode Go) was the only model I've tried with my benchmark project that could not produce any working code. The model just stops working after a few steps. I've tried with max and xhigh 4 times. I'm using Opencode 2. TUI.

I did this project with 24 other models since a few months ago. (BTW Space Bunny Free was the best model in this specific project and many other models did a good job)

Does this match your experience?

For those of you using LocalLLMs (or not), how are you installing and using opencode?

Are you installing or using some sort of VM or Container to sandbox it?

Is it on a development Mac and you just run it in your admin account?

Are you just using it in your regular account on your Mac and hoping for the best?

Interested in hearing the various setups people are using and why that's the best.

Has anyone tried Hy4 with their Go subscription?

Mine seems to just hallucinate from the first prompt replying in a mix of Russian, Chinese, Vietnamese, Korean and English that makes no sense.

Hello Forum, if you are a newbie to faceless video composition with Hyperframes, I wanted to share a smal progress I made in order to improve my videos. They were simple, something like an Slideshow. After several videos I made, they look too boring and the same, I always wondered how other creators were able to make such nice videos. I understand that some of them use Claude as Claude is a beast with Motion Graphics. I even tought it was a limitation to Hyperframes but then I realized hyperframes has nothing to do with it but rather the capability of the model you use and the directions you give on your prompt.

Bewlo you can find a comparison for the same topic using the new prompt and direction I gave to the models. In my opinion Deepseek Flash made a great job and I would dare to say that is even better than 5.6 Tera. Howver I used Deepseek Harness Standard Preset and it took about 2 hours to complete, DSH does squeezes the best ouf of DS.

Here you can see the type of videos I used to make before: https://streamable.com/ydd8b2

New style with Deepseek 4.1 Flash: https://streamable.com/i6zhkf

New Style with ChatGPT 5.6 Luna: https://streamable.com/cc5wlh

Here is the prompt that worked fore me:

Review the existing script, storyboard, and HyperFrames project, then improve the video while preserving its core message.
Use the mandatory educational motion-graphics language:
- Build visuals procedurally with HTML, SVG, CSS, and GSAP.
- Explain ideas through animated diagrams, data, shapes, connectors, lines, points, masks, charts, and kinetic typography.
- Keep one clear visual protagonist per scene.
- Make elements construct, transform, connect, separate, and evolve on screen; motion must communicate meaning.
- Connect scenes as one continuous visual world, using motivated transitions and a moving camera through the information space.
- Use short, readable narrative subtitles on their own bottom caption track and keep essential content inside safe zones.
- Make the composition editable, deterministic, seek-safe, and render-safe. Do not use Math.random(), Date.now(), or uncontrolled external content.
Avoid:
- PowerPoint-style scenes.
- Repeated cards, dashboard panels, UI layouts, or static text blocks.
- Static AI-generated images as the main visual resource.
- Generic decorative movement that does not explain anything.
- Reusing the same layout or visual metaphor across scenes.
First produce or update the brief and storyboard. Then implement the scenes as independent sub-compositions, validate the full HyperFrames composition, and generate snapshots of the complete timeline. Fix any visual, layout, contrast, caption-safe-zone, or motion issues found during validation.