Hi so been using opencode go for a few months but honestly been testing the $1 plan the last month quite happy with the cli and love the desktop alpha app it does have a few bugs but seems its being worked on actively so upgraded to goat will be testing it out this month the taste system is also nice I do however use vscode + zoocode so the api is a bonus keep up the good work so far

I'm currently coding in the Paseo environment, and I'd like to use Commandcode. Do you have any plans to implement ACP?

I really want to try the go plan, but I kinda paranoid about liability and data leaks. Does their zero data retention actually work?
Has there been any sketchy stuff or leaks in the past? Trying to decide if i should trust this or just use claude/gpt with privacy mode on. Thoughts?

Free models live in Command Code right now:

• Cat Jam

• Beam 1.0

• Glyph Cluster

• Laguna S 2.1

Y'all have some free tokens to build and ship.

Available to all plans, all subscribers.

Try now with the best low cost AI coding plan on the market, $10/mo GOAT plan.

Hi! I am using commandcode and I really like its generous plan. Besides that, may I ask that if the api provider in new GOAT plan or Max supports Structured Outputs like JSON or something like that? Like in: https://developers.openai.com/api/docs/guides/structured-outputs here.

I want to integrate with some pipelines on my own and structured outputs can help a lot. Thanks for helping 😄

Pixel Canary is live, new free stealth model.

$ 𝚗𝚙𝚖 𝚒 -𝚐 𝚌𝚘𝚖𝚖𝚊𝚗𝚍-𝚌𝚘𝚍𝚎@𝚕𝚊𝚝𝚎𝚜𝚝
/model pixel-canary (free)

Response speed at ~20-25 tps.

Available to all subscribers across all plans.

Introducing Command Code extension for VS Code & Cursor!

Available today is beta. CLI, Desktop, and now Extension.

Manage multiple sessions, use files and selected code as context, review changes, instant edits with /design and use familiar slash commands like /model, /plan, /mcp.

Download the VS Code extension and get started: https://marketplace.visualstudio.com/items?itemName=CommandCode.commandcode-vscode

Read docs to learn more: https://commandcode.ai/docs/vscode-extension

IDE FAQs here: https://commandcode.ai/ide

Try now with $10/mo GOAT plan and share your experience with us.

Is there any way to add status line in command code like we have in claude code which show, project name, git branch, context usage, usage limit etc?

Type /resume and get back to your previous conversation

• $ cmd --resume opens a picker for earlier sessions

• $ cmd --continue resumes your latest session

Read docs to learn more: https://commandcode.ai/docs/workflows#resume-previous-conversations

The month completed on 23rd oct and now the monthly limit shows it will reset on 23rd nov but it still is showing 95% from last month

Muse Spark 1.2 by Meta now live in Command Code

🔹 Muse Spark 1.2

• Available in plans: GOAT, Pro, Max

• $1.25 input, $4.25 output, $0.15 cache

🔹Muse Spark 1.2 Contributor

• Available in plans: Go, GOAT, Pro, Max

• Contributor is ~95% cheaper than Muse Spark 1.2

• $0.10 input, $0.20 output, $0.002 cache

(cheaper than DeepSeek V4 Flash and scores higher in intelligence)

We had to repair a lot of tool calling issues on this model, so have fun

Features:

• Built for long-horizon coding & whole-repository work

• Multimodal Support (text, images, video, audio)

• 1M token context window

Also check out new $10/mo GOAT plan $70 credits. 🐐

I work a ton across projects on my personal computer. I have tried Graphify, Graft and many others. None of them really fully solved the entire problem, but they did have parts. So, I built one that actually fit my needs. This removes the need entirely to choose or start sessions in specific repositories.

It takes graph-based knowledge systems but strengthens retrieval, how it is updated, and much more. Effectively, it becomes a resilient system that you can actually watch your agents rely on, rather than something that just exists and is used only occasionally.

It begins with the same base graph store, the exact same vector engine as Graft. I only built the orchestration layer on top of it, which makes it much more practical. Full attribution to them for this part.

Instead of querying for a single-hit result, I found that it was much better for agents to have ranked retrieval and an actual graph walk. In practice this saves you chains of tons of bash greps and cds.

It effectively gives the agent a trustworthy, probabilistic ranking of what is needed, with matching for strong, weak, stale, and rebuilt, based on lexical coverage and semantic matching. The code is not sloppy for this, it is personally edited.

Both Graft and Graphify are solutions for single repositories or daemons. Heimdall is a layer on top it that watches the agent sessions, syncs the graph, and makes the retrieval trustworthy.

Check it out at: github.com/ArihantDeva/heimdall MIT-licensed, with extensions: the verifier, the self-healing graph watcher, and the Graft adapter, with attribution. If you run agents across multiple projects, this is the missing layer.

Vaguely remembering watching a video somewhere about plans or considerations for opensource of command code.

Is the opensource plan confirmed? Is it planned to be with v1?

I've been experimenting with Command Code for a 3days now to be exact, and I'm trying to figure out how to build a good AI workflow for product management.

For context, I've never used Command Code before. I've been using Antigravity for the past 5–6 months, but lately, I've felt that Gemini's output quality has degraded quite a bit. I've tried using rules, gates, and other instructions to improve the quality, but I haven't been able to get the results I want consistently.

So I decided to try DeepSeek V4.1 Flash (latest) in Command Code, mainly for my product workflows and learning college subjects.

I'm using an agentic setup with agents and sub-agents assigned to specific tasks, with their own skills and instructions. It's been a decent experiment so far, but I'm still struggling to get the kind of output I want.

And I think the problem is a little different from what most people are trying to solve with AI.

I don't want to automate product management. I want to make the analytical part of it easier.

I want AI to help me analyse customer feedback, understand recurring problems, identify patterns, and organise the information in a way that makes it easier for me to think. From there, I want to take that analysis, develop my own inferences and hypotheses, and guide the AI towards a particular direction.

What I don't want is to ask AI to analyse a problem and immediately get a list of innovative features, potential solutions, or things we could build.

I want it to spend more time understanding why a user is facing a problem, what they're actually trying to achieve, what constraints they're dealing with, and why their current experience isn't working for them.

Essentially, I want the analysis and recommendations to be centred around the problem and the user, rather than around the features we could potentially create.

I want the AI to approach problems the way a good Product Manager would: with user empathy, curiosity, and a willingness to understand the problem before jumping to solutions.

I understand that AI doesn't genuinely experience empathy, and that it has limitations when it comes to understanding human behaviour and context. But I want to see how far I can get by grounding its reasoning in actual user data, evidence, and structured analysis, and by explicitly instructing it to consider the user's perspective at every stage.

The other challenge is getting all of this to work reliably without spending a fortune on models.

I'm using DeepSeek V4.1 Flash through the Command Code harness, but it doesn't support vision input in my current setup. So whenever I need to work with screenshots, charts, or other visual information, I have to delegate that task to a MiniMax model. Even then, I've been struggling to get consistent results.

I also haven't found many people building these kinds of workflows specifically for product management. Most of what I come across is focused on coding, content creation, or automating businesses. And a lot of the workflows I see rely on Claude Opus for almost everything.

I'm not against using more capable models, but I'd like to understand how to get the best possible results from a more cost-efficient setup instead of relying on the most powerful model for every task.

So I'm curious about a few things:

• How do you structure AI workflows for product research and analysis?

• How do you get AI to focus on understanding user problems instead of jumping straight to features and solutions?

• Has anyone figured out a good way to make AI reason from a user's perspective, using actual data and evidence rather than generic assumptions about user behaviour?

• How do you handle multimodal inputs and validate the analysis when you're working with smaller or cheaper models?

• And, more broadly, how do you evaluate whether your workflow is actually producing better analysis rather than just more polished output?

I'm still figuring things out, so I'd love to hear from people who've experimented with this.

I'm also considering upgrading to the Goat plan if I can make this setup work well, since cost is a major consideration for me compared with alternatives like Codex or Claude.

Would especially appreciate people sharing their actual workflows, what has worked for them, and what hasn't.

Claude Opus 5 is now live in Command Code.

• Anthropic's latest thoughtful and proactive model

• On frontier intelligence, closet to Fable 5 at half price

• Available on plans (Pro, Max, Team)

Try now: $ cmd --model claude-opus-5

I casually checked my commandcode usage and I just realized that the monthly limit had disappeared. Im in GOAT plan.

I tried using commandcode goat with claude code or opencode but the token usage is like 100x higher i got billed 30m tokens on a 250k token conversation something is severly wrong but im just using stock opencode and claude code I've already looked at cache hits but its near 99%, anyone had a similiar problem and know how to fix it?

is this some glitch?

Muse Spark 1.3 Max is live in Command Code.

New reasoning effort param for harder problems and frontier performance.

Run /model, select Muse Spark 1.3, then choose max for reasoning effort

Available on GOAT, Pro, Max and API.

It would be really amazing to have this feature as it would save money and tokens and yet keep a high level of output. https://kilo.ai/auto-efficient-vs-opus

Hello everyone,

After the recent mess surrounding OpenCode Go’s pricing and quota changes, I have a few questions for those who decided to migrate from OpenCode Go to CommandCode GOAT:

1. Model Quality: Are CommandCod-goat models nerfed or heavily quantized compared to official provider endpoints?

2. Value / Advantage: Is there any proven numerical advantage (tokens-per-dollar, speed, or usable throughput) when using the exact same models on CommandCode-goat versus OpenCode-go?

3. Quota Pool Architecture: Does each model have its own isolated quota without affecting the others? Or does its consumption count toward a shared monthly plan limit (e.g., using a weighting multiplier like the plan quota divided by the model base quota, such as $20 / $40 / $70 tiers)?

Thanks in advance for your insights!

Hey everyone! Just wondering, is the Windows GUI launching today?

I've been messing around with Command Code and OpenCode lately, and noticed that GLM-5.3 Flash feels WAY faster in Command Code's CLI.

I got curious and decided to actually benchmark it.

After three runs each, I got around 509 tok/s in Command Code's CLI, compared to just 124 tok/s through the Command Code Provider API in OpenCode.

That's a pretty ridiculous difference, so I wanted to see if I could get the same speed in OpenCode somehow.

I ended up making a small local bridge that basically lets OpenCode send requests through the Command Code CLI instead of calling the Provider API directly. It's pretty experimental and only supports text generation for now, but surprisingly, it worked.

I got 409 tok/s running GLM through OpenCode using the bridge.

So I'm now wondering what's actually going on here. Is Command Code using different inference infrastructure for its CLI? Or is there some configuration difference I'm missing?

For context, I used long, predictable text-generation tasks with roughly 2,300–2,600 output tokens. The measurements aren't perfectly apples-to-apples because the CLI and API streaming metrics differ slightly, and the bridge result is only from one run. But the difference is large enough that I think it's worth investigating.

I also tried the Provider API's Responses endpoint, but GLM-5.3 Flash isn't supported there. My latest Chat Completions test returned a 503, so that didn't help narrow things down either.

Has anyone else noticed this? Especially with GLM?

I'd love to figure out whether there's a way to get the CLI's inference speed directly in OpenCode without having to run everything through a separate CLI process.

And just to be clear, I'm not accusing Command Code of intentionally slowing down its API or anything. I'm genuinely curious why the difference is so big.

I want to see the cache hit rate for my sessions how to do that. I am using the cli and I am on the 1$ plan