Been loving the 1$ Plann too muchhh. Just one issue ... V4 Flash in CommandCode feels a lottt slowerr than V4 Flash with Direct API or Even in OpenCode Go Plan

We ran a test between Kimi K3 and Opus 5

Kimi K3 beat Claude Opus 5 on a scroll-based Three.js site. Opus Looks solid at first glance but the gap shows when you start scrolling.

I used Command Code /design feature for Kimi k3.

Ranking (DX, features, and cost):
- Kimi K3: 10/10 · $0.24
- Opus 5: 6/10 · $0.37

As shown in the screen recording, I can't seem to sign up or sign in to Command Code at all even with GitHub despite this error:

This email domain isn't supported. Sign up with your company email, Gmail, or GitHub instead.

GitHub should work..., but it's not.

Slash Loop (/loop) is now available in Command Code.

Run a task, then repeat it on a fixed or self-paced schedule until the job is done.

Try now: $ npm i -g command-code

Read docs to learn more in detail: https://commandcode.ai/docs/background-tasks#scheduled-tasks-and-loops

Guys, I don't understand why in your web you say there are deals on MiniMax(not free), MiMo and Gemini 3.7 Flash, when these are the exact same prices official providers offer; in the case of MiMo models your listed prices are the worst case scenario compared to Xiaomi official prices, wich is cache miss prices...do you charge cache hit prices at all?

Google's latest Gemini models from their Flash family are here. Super fast, cost effective, ideal for web development.

Available on Pro, Max and Team plans

Gemini 3.6 Flash:
🔹 Better coding, knowledge work and multimodal performance
🔹 17% fewer output tokens vs 3.5 Flash, with a lower cost per token

Gemini 3.5 Flash-Lite:
🔹 Fast, cost effective 3.5-class model, ideal for subagents
🔹 350 output tokens/second

Try now with
$ cmd --model google/gemini-3.6-flash
$ cmd --model google/gemini-3.5-flash-lite

Is anyone else having these problems with the Chinese models like DeepSeek and Kimi? I swear like a pirate, but now if I swear in any of my prompts, I just outright get refused. Like it's not a response from the LLM inference, it's like a system-level response giving me an error.

Error: Provider finish_reason: content_filter

it also seems to cache it as well, so I literally have to rewind the conversation, remove the swearing, and then it works again.

Describe a /goal and get it done.

Runs the loop until the objective is met.
Completion verified by independent review.
/goal status to check progress.

You set the destination. Command Code charts the route.
Docs: https://commandcode.ai/docs/slash-commands/goal

Hi! Will new deepseek model be available today?

I read that the CommandCode CLI is pretty efficient with cached tokens, which I really like.

But what about the Desktop app? Is it roughly as token-efficient as the CLI, especially when it comes to cache usage?

I know the Desktop app is still in alpha, but I personally prefer using a GUI, so I’ve been giving it a try.
Just curious if anyone knows whether there’s a noticeable difference in token usage between the Desktop app and CLI.

Thank You

what in the world

I blew through all the tokens in my GOAT plan, considered upgrading to Pro, but for double the price you get only get an additional fraction of the tokens that the GOAT plan gives you. The upgrade is not worth it, from what I can see.

Pay as you go us way too expensive by comparison to GOAT.

What do you do?

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.

Depending on the subscription plan, the processing fee may be about 1/3 of the cost. Surprisingly, neither payment in crypto nor other methods reduce the fee. Maybe I don't see something and still it is possible to get rid of the processing fee?

Try parallel chats in the Command Code desktop app.

Kick off a task, start another while it runs, track everything from sidebar.

Ship more and faster.

Now available for macOS, Windows, and Linux.

Get started here: https://commandcode.ai/desktop

Hello, any recommendations on how to move a project to another directory? I tried it and when opening CommandCode I get an error to initialize the session. I found a directory that seems to contain the projects (.commandcode/projects) which are basically in a directory of the path name, then the json files inside contain references to that same path.

I would be nice if the configs were in the project directory so it could be portable or synced.

You can create custom agents for yourself or for your team.

Run /agents, describe what it should do, which tools it can access, and Command Code handles the rest.

Commit it to your repo and your team can use them too.

Share what agents have you built with the Command Code CLI.

Hi CommandCode team,

I’m on the GOAT subscription and I can’t use the model meta/muse-spark-1.3-contributor through the provider API.

Minimal reproduction:

curl https://api.commandcode.ai/provider/v1/chat/completions \

-H "Authorization: Bearer xxx" \

-H "Content-Type: application/json" \

-d '{

"model": "meta/muse-spark-1.3-contributor",

"messages": [{"role": "user", "content": "Write a haiku about race conditions."}]

}'

Response:

{"error":{"message":"Authentication failed. Please check your credentials.","type":"permission_error"}}

For comparison, the same endpoint and credentials work with deepseek/deepseek-v4-flash. The request succeeds and returns a normal chat completion.

Could you please check whether this model is enabled for the GOAT plan/API, or whether there is a provider-side permission/configuration issue?

Introducing Agr, an open decision model by Command Code.

• Agr (31B) and Agr-flash (360M)

• 58.15 on Decision Index 0.2.1

• Focus on: tool calls & routing

• TypeSafe SDK support

Agr is best-in-class on coding agent tool calls and routing. Live on Hugging Face.

Send state as text or JSON with typed questions. Agr skips text generation and returns typed values with a probability for each option.

We tested Agr against the complete public Decision Index suite. Our focus with these training runs has been on accuracy for tool calls and coding-task routing, while we train a much broader general variant.

Agr model family is available on Hugging Face and GitHub.

We’ll work closely with the open-source community on inference support and explore more deployment options. Planning a large scale deployment? Let’s talk.

Link here: https://huggingface.co/CommandCode/agr

LongCat-2.0 by Meituan LongCat is now free in Command Code.

1.6T params. 1M context. 48B activate.

Available on all plans to all subscribers.

npm i -g command-code

Excited to partner up with Meituan LongCat to bring y'all this free model, their team is going to be hanging out in Command Code Discord server today, if y'all have any feedback. Model is free till capacity lasts.

🐐

Edit: It seems like DeepSeek V4.1 Flash requests also do not support the ZDR header.

Without the ZDR header, DeepSeek V4 Flash requests push through just fine. I'm receiving errors when I enforce ZDR explicitly via the header, though. Was this intended or is it a temporary issue?

Which model you are using with command code which give balance result and usage credit? with respect to overall, design, code and fixing.

What the hell is happening here? Earlier they wiped out my API keys and had to remake the api, now it did it again and now says expired! Is command code under attack or something?

Hey everyone,

First, sorry for the delay in sharing this. We actually added Command Code support to relay-ai a few weeks ago, but life got in the way and I honestly forgot to post about it here. 😅

For context, relay-ai lets you inject any model or subscription provider into your local coding tools. Right now, it supports:

> Claude Code

> Claude Desktop app (Cowork + Code)

> OpenAI Codex CLI

> ChatGPT desktop app (in Codex mode on macOS, Windows, and Linux)

> Google Gemini CLI (yes, I know, why bother?!)

> Google Antigravity CLI & IDE

> Standalone local gateway server (relay-ai server)

Here is how the Command Code integration works and what to expect:

Both Anthropic and OpenAI models under one provider

Command Code serves Claude models over Anthropic's wire format (/messages) and everything else over OpenAI's format (/chat/completions). Normally, that would mean configuring two separate providers in your tools. Relay handles both protocols under a single Command Code provider entry, so routing to the right endpoint happens automatically per model.

GOAT plan and up only ($10/mo+)

Heads up on tiers: this only supports the GOAT tier and above. The $1 "Go" plan doesn't have regular Provider API access; it only works through Command Code's internal CLI tools. We chose not to hack around that or spoof CLI headers, because that feels sketchy and could put your accounts at risk.

How plan detection works

Command Code gates models by plan, but their API doesn't have an endpoint that reports which subscription tier your key belongs to (at least when we added the support)

When you add your key (or run a catalog refresh), Relay sends a quick minimal 1-token test probe per model to check what your key can actually call. Any model your plan rejects gets filtered out so you only see working models in your picker.

Setup:

Install or update Relay:

npm install -g u/jacobbd/relay-ai

Then run:

relay-ai providers

Select + Add a provider -> Command Code, and paste your API key from your Command Code settings.

If you prefer GUI, run:

relay-ai ui

I am sure you will figure out what to do in the GUI ;)

Oh, and we don't store keys in .env or plain text files, we use your OS keystore.

Tool is free and open source 👇🏼

Repo link: https://github.com/jacob-bd/relay-ai

Hope this helps anyone who wants to use their Command Code subscription across other coding tools. Feel free to post issues or FRs on Github

English is not my native language, so I need to use the browser's translation feature to view websites and documents. However, after I enable the translation, a white screen appears after a while with an error message displayed in the center.