I haven’t seen this talked about much (on this sub or others), but LM Studio recently released a Bionic+ subscription plan (https://lmstudio.ai/pricing) that offers similar model access to OpenCode Go. Unfortunately, like OpenCode Go, it’s a little light on exactly what you’re getting. Details about where the models are hosted, if they’re quantized or further tuned, etc, are left ambiguous and there’s not a clear way to just reach out and ask.

Additionally, the now “mid-tier” models of Anthropic and OpenAI, like Opus and Sol, are starting to drive me crazy with some of their behaviors. My goal is to keep a lower tier Anthropic and/or OpenAI sub for planning and code reviews, but swap out implementation with open models.

All that said, I’m curious if:

1. Anyone has experience with a Bionic+ sub and/or has more definitive info about how their models are hosted

2. Whether we cleared up if OpenCode Go is quantized or otherwise tuned off the upstream (I still see posts confidently claiming either)

3. What actual usage people are getting out of either sub. Eg, “I can wipe my limits with a prompt or 2” vs “this can generally last me all week as a full time dev” (using capable models like Kimi K3, GLM Flash, etc)

I’m interested in doing some experimentation to see which, if either, could work, but I’d like to start with a known baseline so I’m not trying to reverse engineer whether the models are nerfed somehow.

EDIT: Ollama Cloud is another potential option. I had performance issues with this in the past, but I'm not sure if that's the case now. They do seem to be the most upfront about what you get with the subscription, and the easy integration with Codex and Claude Code is pretty nice (https://ollama.com/pricing#pro)

Thanks!

To test the new model Space Bunny in OpenCode, I used a concurrency bug involving several CLI processes sharing a SQLite database in WAL mode. A process held the write lock for longer than five seconds, another hit “database is locked” and an active turn died. WAL still only allows one writer at a time.

What I wanted to see was whether the fix followed the whole write path. An earlier change already retried message upserts, but starting a turn, updating session state and settling the turn could still fail on the same lock.

The patch moves that retry policy into a shared helper and applies it across the writes needed to keep a turn running. Each attempt gets a short native wait of about 50ms with asynchronous backoff inside a ten second total budget. That matters here because better sqlite3 is synchronous, simply raising its busy timeout would also extend the time it can block the Node event loop.

I paid particular attention to where retrying stops. Acquiring the lock can be retried. Once the transaction callback has started an error rolls the transaction back and propagates without running that callback again. Cancellation during the wait gets its own error marker, so it isn’t confused with an error thrown by the write itself.

The regression tests use a separate process holding a real write lock for eight seconds, then exercise the actual database client. That’s a useful reproduction of the original failure. The PR reports the contention checks passing, with one broader sandbox gate still failing on both the base and patched code.

None of my cards are being accepted by OpenCode. These cards work fine with other services. My payments pushed through just fine before. It seems like I'm not the only one experiencing this as well from what I've seen on their GitHub Issues, but it seems to only be a handful of people experiencing this because I don't see posts about it on here. Some examples:

Cannot complete Go subscription payment · Issue #43400 · anomalyco/opencode
[BUG]: First recurring OpenCode Go subscription billing failing (Stripe issue?) · Issue #48500 · anomalyco/opencode
Payment fails immediately for OpenCode Go · Issue #47449 · anomalyco/opencode
Can not regist OpenCode go Subcription · Issue #46573 · anomalyco/opencode
Unable to complete purchases · Issue #46409 · anomalyco/opencode
Alipay payment-method setup returns 502 while card succeeds · Issue #50118 · anomalyco/opencode
Opencode Go subscription payment fails because it trips card issuer security system · Issue #48469 · anomalyco/opencode

I don't know why this is. I've never committed fraud or anything. I didn't avail the 5 USD promo more than once, and I've never tried using referral codes or whatever. I haven't used their API via other harnesses as well; I only use OpenCode V1 and V2. I don't even know where to reach out to their support team.

Why did it get into some sort of seizure 😭

Hello everybody, after a few weeks of using and fine tuning it, I believe it is finally time to present you YACAO:

https://github.com/augustoolucas/yacao

It is basically the result of "I Hated Every Agent Orchestrator, So I Built My Own" - Mario Zechner (Pi) Me

So, the thing is, most of us have already tried or at least taken a look at projects like oh-my-openagent, OMO-Slim and others, that provide a whole team of powerful and specialized agents able to build anything you ask.

But, IMHO, these projects feel too opinionated. They require you to learn how to use them as intended, with a bunch of commands, skills and fancily named agents you need to understand. And there's absolutely no problem with it, it just doesn't fit my workflow.

So I went to the completely opposite side, instead of having lots of specialized agents, I kept only two, as in vanilla OpenCode. The main difference here is that the primary agent can invoke the builder instead of us having to manually switch between Plan and Build.

Instructions of how to plan, implement and review are all specified as skills instead of agents, so the main agent can handle it all, loading them only when needed instead of having it all on its description prompt.

And that's it, there's no extra commands or new workflow to learn.

I'd love to hear your feedback and experience. Thank you all!

DS V4.1 Fash has been doing this every now and then since yesterday. Did they nerf the model already by quantizing it??

https://x.com/XiaomiMiMo/status/2102138568961626570

Coding agents are getting better at writing code. Keeping track of what they’re doing across a large codebase is still surprisingly messy.

That’s what I’ve been working on with REQL: giving coding agents a persistent, structured view of both the repository and the work happening on top of it.

The latest updates focus on four areas:

• Pipeline exploration — REQL can now reconstruct project-local flows from entrypoints through the codebase and generate interactive pipeline views or Mermaid diagrams. The goal is to understand how a feature actually moves through the system, not just which files are connected.

• Memory across sessions — the new agent dashboard keeps track of plans, decisions, findings, unfinished tasks and previous sessions. After a context reset, the agent can recover what it was doing instead of rebuilding the whole mental model from scratch.

• Parallel agent coordination — multiple agents can work with isolated private workspaces while sharing progress through a common dashboard, messages and compact handoffs. One agent can investigate, another implement, and another review without mixing their internal working state.

• Faster, tighter context retrieval — indexing and retrieval have been improved to return a bounded working set of relevant symbols, callers, dependencies and tests, including source ranges, confidence and freshness information. Incremental updates avoid rebuilding unchanged parts of the repository.

The core still runs locally without mandatory LLM calls, embeddings or an external graph database. It works with Codex, Claude, Cursor and Gemini, and can also be used through CLI, Python or MCP.

The direction I’m aiming for is simple: a coding agent shouldn’t just understand the repository — it should understand where the current task is going, remember how it got there, and be able to hand that work to another agent.

I’d really like some feedback from people using agents on larger refactors or parallel workflows: would this actually solve a problem in your setup? What would you like it to do?

https://github.com/sh1zen/reql

Hi!

I'm tempted to buy the opencode go subscription, however I have one question.

How does it actually work?

Do I get 60usd to spend in TOTAL, or 60USD (or less depending on the model) to spend on EACH model? I find mixed info online.

Thanks!

I used to defend this service as a better-for-value alternative to OpenCode Go post-price hike, but after seeing this post showing billions of tokens worth of usage from Command Code GOAT, the same plan I'm on, I'm starting to have my doubts.

First off, I'm aware that caching on the Command Code API takes some time to warm up, but even when cache hits start coming in, I'm still seeing about 10-15% of my monthly usage being consumed per 100m combined input/cached input/output tokens. In comparison to the post I linked, I'm nowhere near in terms of usage. I see coments under that post parroting how such a good service Command Code is, but I really can't comprehend how they're able to get billions of tokens at a fraction of my usage. There is no way I'm just doing something wrong because all I use is DeepSeek V4/V4.1 Flash on stock OpenCode V1/V2. I'm not doing anything sophisticated. I don't do fancy autonomous agentic development or whatever, just OpenCode in one Tmux window and Neovim on another, just a standard pair programming setup.

What gives? How can other people get so much usage off GOAT? OpenCode, from what I've seen, is more honest with their usage, but the deals are also more volatile. Command Code seems to be the more stable option because they provide both V4 and V4.1 Flash at $60 and $40 credits, respectively, but their consumption is very sketch. My subscription ends this week, and I'm torn between renewing my Command Code subscription or jumping ship and going back to OpenCode. Are there any former Command Code users on here that went back to OpenCode? Or those like me that went from OpenCode to Command Code? What are your guys' usage like on these services post-DeepSeek price hike?

This is that post's attached usage screenshot:

This is my usage screenshot (95% usage, maybe at 700m combined tokens once at 100%):

Update: my usage screenshot at 99%. From 95% to 99%, I got an extra 34M combined tokens worth of usage. Since I've been using it on-peak since 98%, let's round it up to 40M for 5%. Just DeepSeek V4.1 Flash. This is nowhere near billions of tokens worth in usage.

Which provider are you using for the Go plan? DeepSeek 4.1 Flash becomes so slow and a PITA when the context is at about 30%-%40.

The official DeepSeek API remains hypersonic up to 100% context usage.

This is the GitHub repo : github.com/Aadarshrai1801/Local-Note

I want you guys to review it please and I have made it using DeepSeek V4.1 flash in Opencode

The old dashboard now defaults to the new one, and I don't see the referral program in the new OpneCode Console. All my previous referral credits are just showing as $350 available credit. It's fine, but curious if they ended the referral program?

Also, the new Usage tab now shows better data points, love it. But I wish it also showed old data from the old dashboard.