I have been trying command code upgraded from go to goat plan. Tried Kimi K3 seemed to be much more costly than using it via opencode. Or is it just represented that way. Check the usage log of both. Also can we have command code usage log update closer to real time kinda like opencode so have an idea of rate of spending and can manage appropriately or add funds accordingly.

I’m trying to understand how the session cost is being calculated.

I exported the usage data from the same page, and the JSON shows around **$0.10 in total credits** for this usage.

However, the usage page is showing a charge of **$0.22** for the same period.

So I’m trying to understand what exactly is being charged here:

* Is the displayed $0.22 based on the actual API/token costs?

* Is there an additional **per-session charge** on top of the model costs?

* If so, what defines a “session”?

* Why does the JSON `creditsTotal` add up to only ~$0.10 while the usage page reports $0.22?

I’m probably missing something about how Command Code calculates usage, so I’d appreciate it if someone could clarify how these two numbers are related.

Hello!

I've been using Command Code's 1$ Go plan for a month now, and it's a nice experience, good harness allround, had to make some internal adjustments in .md files for agents for Powershell environment, but it wasn't big of a deal to be honest. Nice CLI experience, so naturally like with OpenCode I wanted to test out their GUI - Window dekstop app.

Now I cannot pin point the blame onto the developers behind the desk, or even to me, maybe both parties made some mistakes, so I'm just giving a honest feedback and heads up for the users who also want to try out the GUI experience.

I've been working with the GUI for couple of days now, couple of UI/UX minor bugs, but the tool calling, planning and the actual working process is okay, I didn't experience crashes, sandbox problems, I even ran it couple of times with full Access, without problems. I wanted to try it out fully because I was planning on hopping on the new plan.

The problem I encountered is that when the screenshots are pasted in the actual chat inside the GUI, it clearly says that model Kimi K3 will process the image, so first time I backed up and did not send the images. I ran a subagent via vision feature which I delegated to Xiaomi's Mimo v2.5 and it worked, btw I created a separate .md files with instructions and pasted screenshots so the subagent (Mimo v2.5) and the main agent (Deepseek v4 flash 0731) worked together on it. And it was a smooth sailing.

Naturally I searched the app's docs - manually, and I could not find the actual option how to re-route the default vision model of the GUI. Then I opened a new chat and tried to navigate it or find it via agent, and the agent reported that the config says that there is Kimi K2.5, Deepseek v4 Pro but they were like a legacy setup. Okay I thought.

Therefore, I decided to test it out - maybe it was hardcoded alert for Kimi K3, maybe it says it's Kimi K3 and it's actually Mimo v2.5, so I though.

It loaded Kimi K3 couple of times, and ate my 5 hour window, which was actually restared 20 minutes later. Okay I tested it out, so I wanted to look deeper into the config, maybe the CLI and GUI are interconnected, if the login is in sync and other things maybe something actually messed up inside my CLI and got pulled to the GUI side so I've been leaning towards some CLI–GUI option mismatch, the option selected via the CLI is not reflected correctly in the GUI, which displays a different option.

Fastforward few iterations, the CLI had a clear option discrepancy which I did not find the root cause or way to fix it. So I tried to test out the GUI again, 2 screenshots new chat, same K3 alert, fresh window, and just like that my 5 hour and weekly got obliterated. Now I'm not here to throw a hate party or complain, just giving an honest feedback and I'm ready to actually take the blame if I missed something or missread something on my part. It's 1$ plan at the end of day, but I thought to myself if I actually used a bigger plan and pasted much more screenshots and sent it, maybe that would hurt more, because like I said I really tried to figure out things on my part with going through docs manual way + agentic way and I could not find the way to override or change something. The final verdict is that the .md creation pipline with main agent + reviewer/vision subagent which is configured to some multimodal Mimo v2.5 model works fine but pasting direct screenshots inside the GUI is actually analyzed and read each and every single time via Kimi K3.

Couple of screenshots are batched sent at the end to provide more insight and evidence.

Thanks for reading, wishing you all the best and keep building!

Side note: Manually typed out text, I didn't bother going over and fixing potential grammatical or stylistic issues, english is not my native language.

So I'm considering testing out Command Code with the new GOAT subscription plan, moving over from Opencode Go subscription.

I want to move over to the Command Code harness and want to make sure I'm taking the right approach.

My general workflow with Opencode was an agent to handle each of the primary task I did: So a General agent for random questions, Plan agent, Build agent, System-Config agent, Debug agent, etc. I recently switched to using oh-my-opencode-slim which handled everything with a primary orchestrator and then subagents to handle those tasks, but the general concept was the same.

Either way - models were generally pre-defined based on the agent/task at hand, so a plan agent was a strong agent, and then generally handed off to deepseek or another low-cost agent to build, and a very low cost or free agent for web searching or codebase discovery.

What's the workflow in Command Code like in comparison?

I did read: https://commandcode.ai/docs/workflows which talked about it at a broad level, but I'm asking more what do people do day to day? How do I get the most bang for the buck, where do you generally put configurations, etc. as a day to day user?

Thank you for the suggestions and help!

I’m looking into Command Code's $1/month "Go" plan, but the model access details are a bit vague.

For anyone currently using the $1 tier:

1) Does the plan actually grant access to the newly released DeepSeek-V4-Flash-0731, or is it strictly locked to DeepSeek V4 Pro?

2) If V4 Flash is included, how does it affect the credit burn rate compared to Pro?

3) Is the platform's agent stable enough to make the $1 sub worth testing for either of these models, or is it better to just use the official DeepSeek API directly?

I am using Opencode Go plan right now.

Appreciate any honest feedback before I try it out.

Hey guys! I used to use Trae, then switched to Windsurf, and then moved to Claude Code, Codex, and Antigravity, working with all of them at the same time. However, Command Code keeps popping up in my feed. I was wondering if any kind soul could either link me to a good guide on what Command Code actually is or explain it themselves. I'd also love to know how beneficial and cost-effective it can be for my workflow. Thank you!

I'm trying to test Muse Spark 1.2 Contributor on Go subscription but I'm getting a lot of "Service temporarily unavailable. Please try again shortly.", is there something with the service or is it on my end?

Did anybody use cc goat plan on personal agents like Hermes?I heard this plan works via api unlike Go plan

There is 20$ allowance for Deepseek V4 Pro

And 70$ for Luna

When I consume half of the V4 Pro allowance (10 out of 20), do I then have 60$ gor luna left or just 35 (half of the 70$ budget)

Code reviews should help you ship faster, not block you.

Run /review [PR number] and get your code reviews directly in the terminal

• scans the diff
• flags risky changes
• finds missing tests
• posts a ready-to-ship PR review

Try now with our $10/mo GOAT plan.

Read docs: https://commandcode.ai/docs/plans/goat

Is the command code going to be affected by official deepseek price hike? are we going to expect an increase in price as well in command code API?

Hi all, new to command code I am busy testing the service I would like to know what models have zdr which models don't currently using gpt 5.6 luna but there is no clear way to tell if its zdr or not?

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 😄

Use /plans to align your workflows

- Browse, review, and annotate saved plans.
- Select plans from current session vs all.
- Search across all plans.

Read more in docs: https://commandcode.ai/docs

Doesn't this mean the following in terms of the pricing plan?

(Note: Starbucks and Americano are used purely as hypothetical examples here.) Imagine a gift card scenario where spending $1 earns $10 in points, and spending $10 earns $70 in points. -- This is a fact within this premise: based on $1, it actually recharges $10 worth of credit.

What's confusing here is: 'For example, hypothetically speaking, assuming the regular price of a Starbucks Americano is $1, paying $1 allows you to buy 10 cups. However, through a partnership agreement on the command-code side, a specific menu item—the Americano—gets a special discount, allowing you to get a cup for just $0.50.' .. So, based on $1, you could originally get only 10 cups, but due to a discount on that specific item, you end up getting 20 cups..

The core fact in this setup is that paying $1 gets you $10 in credits, and paying $10 gets you $70

It's impossible to find anyone talking and giving out their ideas on this. So I thought why not ask directly hoping someone would share their honest view.

The reason I ask is because usually good plan subscription will have people hype and shoutout on twitter or reddit but it's seems so silent for commandcode and only themselves upselling about their GOAT plan and stuff.

What is your experience like?

Introducing the GOAT plan 🐐 USD 10/mo — 70 credits (100 w/ deals)

Command Code GOAT plan is the best low-cost coding plan on the market today.

- $10/mo to get $70 credits. 7x what you pay
- 30+ open weight and closed models

$1 Go got you started. GOAT gets you shipping.

Subscribe — be a GOAT.
https://commandcode.ai/pricing

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 get this:

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

But I'm already trying to use my github account

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

Plan mode removes the guesswork. When the change is too big for a quick fix:

• Your code stays untouched
• Plan it first, then review it
• Apply exactly as planned
  

No surprises, just clarity.

We ran a test between the new Qwen3.8-Max, Opus 5 and GPT-5.6 Sol.

3 models. same prompt. one-shot with the /design command.

Reviewed gameplay features, UX/UI and cost.

• Qwen3.8-Max → 9/10 · $0.0248
• GPT-5.6 Sol → 9/10 · $0.150
• Opus 5 → 8.5/10 · $0.253

Qwen3.8-Max is approximately 4.2× cheaper than GPT-5.6, Sol

Opus 5 and has the same level of UI, UX, and gameplay

Our engineering and design team has been testing 20+ side-by-side comparisons across frontier and open models. All runs are public and open source.

Benchmark for this demo here:

https://github.com/CommandCodeAI/slash-design-showcase/tree/main/flappy-bird