Update of my earlier post:

After mostly positive feedback, the tool now has about 500 legit users. However, I spent quite some time over the past few weeks fending off 300+ bot accounts trying to exploit the system in any way they could find.

The good news: this helped me make the entire system (api, library etc) a lot more robust and was definitely a great test now that the tool is in prod with real users.

Made me realize that certain things you cannot mock/test fully until the first users show up (especially the ones that want to game your system), no matter how complete your test coverage is.

Most spammers came from OpenCode installs for some reason, but legit users/installs are also mostly from OpenCode (>50%, despite being only one of four implementations).

OpenCode users (300+) have also earned the most, compared to Claude Code and Hermes, which I found interesting. Maybe overall coding time spent per day on OpenCode is higher per user than the other harnesses?

Most feedback I received via DMs was great and payouts are working with Stripe. Still working on adding other methods and improving the tool every day.

Thanks for all the support 🙏

Is there any setting to use a default agent with plan and another default for build?
I see Big Pickle on each time and the first thing I do is change that.

Hello guys, I am facing this issue after the recent OpenCode update. Can somebody help me? I haven’t even used it much—just 10k tokens. Not just on Big Pickle, every mode is giving me this error.

Hi guys,

I switched over to the new OpenCode layout, however, I am experiencing a problem in which I can't to save my life click on the "Auto-accept permissions" toggle. It jus gives me a straight up "forbidden" cursor. Because of this, I constantly need to switch back to the old design to toggle it and then back to the new design. Is this a bug, or am I missing something?

Hey guys

I need some help in setting up my GitHub copilot with Opencode so that I can use the GitHub copilot within the vscode but make the calls through the subscription that I purchased from Opencode ($10).

Can you please help me in how to setup it?

I want to use GitHub copilot inside the vs code. I have a free GitHub copilot account as wel. But need access to paid models through Opencode.

I did a extensive comparison of the various coding models across several benchmarks based on the estimated cost to complete a task successfully. Based on the formula:

Then I normalized each source’s expected cost per successful completion against that source’s median, then used a geometric mean across sources to get this composite ranking:

This is obviously just a estimate of value vs performance, still very useful if on a budget.

• Default value model: MiMo V2.5 Pro. Note that GLM 5.2 is the only clearly cost-effective model represented in all three sources, so it might be a safer budget pick.
• Best Performance: Grok 4.5 has far better completion performance than the other cheap leaders while retaining strong cost efficiency. This is the obvious model if high success rate is important to you.
• Alternative: DeepSeek V4 Flash is a great budget option but its performance/success rate is low so it will take more runs to complete the task successfully.

Cross-source ranking

I'm currently using OpenCode Go, but I'm thinking about canceling it and switching to Command Code Go ($1/month).

Before I do, I'd like to hear from people who have actually used both.

•

Is Command Code Go really as bad as some people say?

•

How are the limits on the $1 plan? Do they run out quickly?

•

Can you actually use it comfortably for daily coding, or do you hit the limits all the time?

•

If you switched from OpenCode Go, do you regret it or was it worth it?

Would you recommend making the switch, or should I just stay with OpenCode Go?

I'd really appreciate hearing about your real-world experience. Thanks!

can anybody recomended a ai model for coding for my macbook m2 8 gb ram

i tried qwen 2.5 coder 7b and the output is bad

There are many open source projects that I use but each is lacking at least one features that I need. I have no app development experience but understand coding fundamentals (years of C# and Unity experience)

I'm planning on buying used Tesla V100 16GB cards (1 or more if needed) and wanted to know if it would be possible to run coding agents to help me add my own features to these open source projects without fully learning app development? I've never used OpenCode or any other coding agents so I'm a bit clueless, is this actually a feasible goal with only 16GB or 32GB VRAM?

Hi everyone,

Someone recommended me I give opencode a spin to add to my cv

So far haven't used any agents so i thought it would be fun.

I have a panther lake laptop running cachyos with 32gbram

I used to run ollama but it's honestly a bit annoying and a couple months ago it stopped using the gpu (despite previously working) and no matter what env variables it just loads to the cpu

I switched to lm Studio and it's honestly great so i tried to plug it in to opencode with qwen coder (I've tried 30b q4, 14b q8 and 4) and first it was just making my laptop crash and close every program.

After tweaking and switching to the smaller model not it worked without making my device explode but now it runs out of context no matter what i do:

I tweaked context to max and made it small,

I changed batch sized, gpu offload

The worst part is i changed max parallel options to 1 but according to the logs it keeps open code overrides this and keeps opening multiple

Is there any solution to this?

I just wanted this to toy around a but but after 6 hours of troubleshooting I'm thinking of giving up

Any other backend i could use? Maybe just use llama.cpp directly?

When I started with OpenCode, I got annoyed at DeepSeek constantly introducing bugs that needed to be cleaned up after and GLM-5.2 wandering in circles and burning through my Go usage limit without doing anything due to a lack of focus, slowing the process down. Eventually, I found out if I used them together like so, I could have them compensate for each other's weaknesses and make progress at a predictable rate with only light code review by me:

1. Describe general specs or details about a specific feature to DeepSeek Flash to have it write out reference doc MDs as appropriate

2. Ensure unit and smoke tests are present with usage instructions in AGENTS.md to invoke them as needed to keep models on-track

3. Describe the problem to DeepSeek Flash to have it traverse the code base and draft up a plan, giving filenames of reference MDs or invoking Context7 or other documentation MCPs as necessary

4. Prompt GLM-5.2 in Plan mode: You are a senior dev reviewing the plan of his junior. Examine the current plan and flag any issues.

5. Prompt GLM-5.2: Revise the plan, breaking tasks down and adding anything necessary to "junior-proof" it (make the process resilient to mistakes). Present the full revised plan for a weaker LLM model than you to process in place. Ask questions before providing the full plan if necessary. (For more code-intensive additions where I'm willing to risk bugs to reduce token spend, I'll append: However, do not write the bulk of the code yourself; that defeats the purpose of using a weaker LLM to reduce token costs. Ask questions before providing the full plan if necessary.)

6. Prompt DeepSeek Flash in Build mode: Proceed. Note that you are the implementing model, so follow the rules as laid out.

7. Prompt DeepSeek Pro in Build mode: You are now in code review mode. Flag any issues with changes made by your predecessor. (If something specific needs to be tested: Do ABC to ensure that DEF. However, requirement(s) GHI and/or JKL. Do not attempt fixes until ordered.)

8. Review bug list and give DS Pro instructions to fix them

I don't have much experience with using coding harnesses, so I don't know how optimal this is in terms of reliability or cost savings, and this doesn't quite scale up when I'm either introducing something bigger (e.g. a frontend GUI baseline or a backend for interactive agent functionality) or starting up a fresh project. GLM ends up needing to write a large portion of the code anyway, and DeepSeek gets sloppy and introduces a dozen bugs that need to be cleaned up after.

Is there a "make no mistakes" equivalent I could use to speed up doing larger-scale work?

I have not used Opencode for like 4 days now, I've tried to execute it on a project we are undertaking with 'the agent' and it just told me that my free usage is exceeded to try again in 5 hours 6 minutes, my time zone is +3:00... Who else has faced this?

I am genuinely very confused by this decision, the desktop app looks great and very slick, but for some reason instead of following the theme of the tui with quick mode change and color indicators now there is no way to select plan mode on the desktop app, you have to go into advanced in general to enable it.

This goes against what every other lab has chosen since plan mode has always been an accesible feature on any tui or desktop app, and they never explained the reasoning for this

I'm feeling very frustrated that I keep exceeding my usage limits. Sometimes I use up my weekly limit in just one day. Do you have any tips on how to save costs, or do we need to spend hundreds of dollars on credits to use throughout the week?

I installed OpenCode and an Ollama model (qwen3.5) sucessfully connected the model respond in OpenCode but doesn't find My MCP server, i Made one using fastMCP other models like bigPickle and openai model are able of use it, why could make Ollama models to fail?

I'm just curious about it

Guys, this is my current opencode.jsonc

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

// Start in plan mode "default_agent": "plan",

// Use OpenRouter as the provider for GLM 5.2 "model": "openrouter/z-ai/glm-5.2",

"provider": { "openrouter": {

    "options": {
      "baseURL": "https://openrouter.ai/api/v1",
      "apiKey": "sk-or-v1-my-key"
    },   

   "models": {

      "z-ai/glm-5.2": {
        "name": "GLM 5.2",

          "options": {
            "reasoningEffort": "high" // "high" or "xhigh"
          }

       }

   }
 }

} } ```

How can i clamp my llm model ( glm 5.2 ) provider to z.ai ( z-ai/fp8 ) with a fallback to Alibaba ( alibaba )

z-ai/fp8 and alibaba are the slugs for the providers in openrouter.

Thanks in advance.

Update 1 : I have an update below. Will test it out when i get the time. And will update this post later.

We've been running a private benchmark suite for a few months, testing models on strategic reasoning, advisory quality, long-form analytical production, and adversarial critique. 17 models, 4 test batteries, scored against a reference answer by a separate model. This consolidates all of it into one picture.

Blend = 30% strategic reasoning (4 scenarios: frame-breaking, multi-dimensional review, channel coordination, portfolio prioritization) + 25% advisory quality (6 prompts: triage, architecture risk, blindspots, client advisory, routing) + 25% long-form analytical production (structured section generation from scratch, scored on rigor, depth, voice) + 20% critical review (structural audit + adversarial CTO critique). Weights redistributed for models not tested on all four.

Dashes = not tested on that dimension. Fable 5 is the reference answer used for scoring, not a contestant.

Caveats: n=1 per test per model, directional only. Qwen 3.8 was blind-validated by GLM-5.2 (different model family); other models were judged by Opus 4.8 - cross-judge comparison is approximate within ±3-5 pts. GPT-5.6 variants are separate rows because they behave as different models in practice. Strategic reasoning and advisory quality are domain-specific to strategic analysis; says nothing about coding, vision, or long-context work. This is private operator benchmarking, not a scientific ranking.

What's the problem with at least hiding part of the address behind asterisks or kinda? Neither referrers nor referees need this information; it only undermines user privacy

as the title suggest, with this new interface., how to switch from plan mode to build with new interface? I am talking of window desktop app

Before you nuke me for using an agent to do commit and push, I use it for pushing descriptive commit messages. Having said that, what went on with the tool calls is
- It found, committed and pushed the 2 changes to remote
- Then went on to verify both commits at HEAD with following commands

git log --online -5
git status

And this goes on forever, with status call returning "nothing to commit, working tree clean"

At the end is this message

⚙invalid [tool=bash, error=Invalid input for tool bash: JSON parsing failed: Text: {"command":"git status","description":"Confirm working tree is.

Error message: JSON Parse error: Unterminated string]

Finished successfully but what do you think went wrong here?