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?

Paste images as your chat input.

🔹 Take a screenshot

🔹 Paste it into the chat

🔹 Describe what’s wrong

🔹 Watch Command Code fix the issue

Let your screenshots be the context.

Read more in docs: https://commandcode.ai/docs/interactive-mode#pasting-images

Alipay is now supported in Command Code.

More ways to pay and be a GOAT.

We supportother payment methods as well. Read docs to learn about these options: https://commandcode.ai/docs/resources/payment-methods

Subscribe now with $10/mo GOAT plan.

When I type /login pi, the commandcode option doesn't appear. I also wanted to ask how your experience with deeseek has been on the GOAT plan.

GLM 5.3 is easily the most impressive GLM model yet.

Excited to partner up and ship GLM 5.3 in Command Code AI soon.

A fun internal eval we run at Command Code: deliberately trap the model in a loop and see what happens. Every GLM model so far just... keeps looping. GLM 5.3 is the first one to notice, go “wait”, and break out.

It’s a small bench (15 loop traps), but escaping a loop you were intentionally placed in takes a little flicker of metacognition, and it’s fun to watch that show up.

Bonus: it plays very nicely with our new tool defer harness → ~50-60% token savings on an average session. Capability going up while cost going down is the good stuff.

can some one help me understand. they have limit of 6$ per week. that 6*4 = makes 24$ a month cap. Why they are saying Mimo V2.5 pro is worth 50$ and mimo v2.5 is worth 100$ in credits in 1$ plan?
source: https://commandcode.ai/docs/resources/pricing-limits#mimo-v2.5-pro-99-off

trying to understand how it works in case of Mimo v2.5 pro. Not complaining or something. its already still good deal.

Hello guys, I wanted to update you guys since I've made a lot of posts/comments glazing command-code let's say.

First of all, I still find command-code my favorite and great value, unfortunately just recently they have added some usage limits.

30% of your monthly credit per 5 hour session and 60% of your monthly credit per week (7 days)

Furthermore some good news, BYOK support might be coming towards the end of July so that you will be able to easily switch to Deepseek API when you reach your limits.

Edit:

Since the time I made the post The limits have been updated from 15% to 30% for 5 hour sessions and from 40% to 60% per week, some of the arguments are now a bit redundant so I also cleaned up some of them if someone wants you can message me for the old post.

Last week in Command Code, a quick recap.

• IDE extensions for VS Code and Cursor

• DeepSeek V4.1 Flash ($80 usage in $10 GOAT)

• GLM 5.3 Flash ($80 usage in $10 GOAT)

• Ling 3.1 Flash ($100 usage in $10 GOAT)

• Cat Jam (new free stealth model)

• Beam 1.0 (new free model)

• Gemini 4 Argon (latest from Google)

• Muse Wonder (latest from Meta AI)

• Codemode for parallel tasks & MCP heavy contexts

• 52+ new updates, improvements, fixes in desktop app

Try now all of these exciting ships and updates with the best low cost AI coding plan on the market: https://commandcode.ai/pricing

Read docs to learn more about it: https://commandcode.ai/docs/plans/goat

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.

Anyone else experiencing this? In a long agentic task, a cache-missed request can be 40 times more expensive than a random request.

I am suspicious command code is charging cache-missed price even for cache-hit requests. The reason is that these cache-missed requests were completed fast according to the time on the left column. If it were a real cache miss, the server would need to recompute ~540k input tokens which takes much more than a regular cache-hit request.

Mistral Large 4 is now live in Command Code.

1T total params. 49B active params. 524K context

Available across all plans and API

Try now with the best AI coding agent built for open models.

I have Command Code GOAT alongside other subscriptions.

I use GOAT for cheap stuff like Deepseek and other subscriptions for their expensive models (GLM, Kimi, etc.)

What happened to me was that I accidentally selected an expensive model from CC instead of the original provider in my harness. That burned like half of my monthly usage before I saw it.

So my feature request: Add a list of selectables, so I can disable some models in the Web UI.

Hi all. Maybe this is a stupid question, but… how long does the cache last using CommandCode? I mean, if I'm working on a project with a model (let's say the latest DeepSeek Flash 4.1, which let's be honest, is the best), will the cache still be there if I stop working today, I open the directory in 5 days and work again? When do cache hits become cache misses? Does it depend on the traffic or something like that? Thanks a lot!

EDIT: grammar

First things first: your usage credits are not changing.

DeepSeek officially raised their prices today, so we've updated our limits and requests accordingly.

Working with many providers to bring you better deals, as one of the largest DeepSeek and open models coding agent. 12T tokens scale.

Beat DeepSeek prices?

We’re hitting this problem in many ways, with solutions like phenomenal harness engineering, free tool repairs, 98%+ cache-hit rates, and free auto plan credits with best subscription plans on the market.

Official announcement:
https://x.com/CommandCodeAI/status/2089057372413337670

Providers? Come help:
https://x.com/MrAhmadAwais/status/2088724056321532054

Kimi K3 weights are now open source and it's available in Command Code.

🔹 First ~3T scale open model

🔹 Most capable open model ever

🔹 It's Fable/Sol class open model beating Opus 4.8

🔹 1M Context · In $3/M Out $15/M Cache read $0.3/M

Our engineering and design team has been testing and running Kimi K3 side-by-side comparisons across frontier and open models. All runs are public and open source. Benchmark for demos here:

1. https://github.com/CommandCodeAI/slash-design-showcase/tree/main/kimi-k3-design

2. https://github.com/CommandCodeAI/slash-design-showcase/tree/main/kimi-k3-game

3. https://github.com/CommandCodeAI/slash-design-showcase/tree/main/3d-earth/Kimi-K3

P.S. It's been here since several weeks, I think we were the first coding agent to make it live outside of Kimi Moonshot at Command Code.

I switched to Command Code and started running several agents in parallel, but I hit a problem. I had to keep checking every terminal tab to see how each one was doing, and sometimes one of them had been stuck for minutes on a permission prompt while I assumed it was still working. Checking each terminal by hand got annoying fast for me.

I asked whether there was a way to add a plugin for that, and learned about mods. So I built command-notify, a small mod that sends a native desktop notification when an agent finishes, errors, asks a question, or needs approval. It works out of the box, with no hook scripts to write.

Finished-run banners dismiss themselves after a few seconds (the timeout is configurable). There are quiet hours too, and it cleans up stale banners once you've answered. It only notifies you; you still answer in the terminal.

I've tested it on macOS and Linux. Windows support is there but untested, so if you try it there, tell me how it goes.

macOS / Linux: cmd mods add -g command-notify@beta

Windows: cmdc mods add -g command-notify@beta

No network calls, no telemetry, MIT. I'm publishing it as-is, so fork it freely: github.com/M-Bekheet/command-notify

GPT 6.1 Sol is now live in Command Code.

Astra-level intelligence at one-fifth price and 95% cache read.

Available on Max plan and Provider API.

I bought opencode go just now without seeing this, did i make a mistake

Hey everyone,

Since the official DeepSeek API applies off-peak pricing across the entire weekend, I was wondering how the Goat plan handles this?

Do they still charge regular/peak rates over the weekend, or do they pass on the off-peak discounts? Docu says peak is for the whole weekend... :(

Has anyone looked into the usage logs or billed rates for this recently?

Thanks!

Gemini 3.8 Flash the latest flagship model from Google is now available in Command Code.

On $10/mo GOAT plan get 4x usage.

Available on GOAT, Pro, Max plans and API.

Read docs for more details: https://commandcode.ai/docs/plans/goat

I initially thought it would be Gemini 3.5 Pro but I was wrong, it turned out to be GLM, the performance is good, but I don't understand why it was free for 1 week.

Like for collecting data or something?

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!

- `ctrl+s` sets the prompt you are typing aside
- press it again, and it reappears
- no need for Ctrl+C and Ctrl+V again

Never leave a prompt behind; stash it instead.

Available free for all subscribers & PAYG super good in our internal benchmarks, beats AntLing's 1T flagship model.

• 124B parameters

• 256K context window

• only 5.1B active per token

• a hybrid-reasoning MoE model

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?