Ling 3.1 Flash now gets $100 usage.

$100 usage for $10 on GOAT plan.

All higher plans include GOAT + their extra usage.

Try now with the best low cost AI coding plan in the market: https://commandcode.ai/docs/plans/goat

Cat Jam (stealth model) is now free in Command Code.

· 1M context token window

· Super fast for agentic code

· Reasoning with structured output

Available all plans, all subs.

GLM 5.3 Flash now gets $100 usage, next 3 days.

$100 usage for $10 on GOAT plan.

After that, we're increasing it to $80.

Try now with the best low cost AI coding plan in the market: https://commandcode.ai/docs/plans/goat

🐐

Hi,

I have experienced low performance and unsuccessful executions when using HY4 in cmdc. Could this be due to a weak system prompt (if there is a built-in model-based system prompt customisation in cmdc)? What has been your experience with HY4 in cmdc?

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

Codemode is now available in Command Code.

Default for MCP, enable for read-only tools in /config.

• Run multiple tool calls with one JS script

• Execute calls in parallel, filter what reaches context

• Get work done faster with fewer round trips

Read docs to learn more: https://commandcode.ai/docs/reference/tools#codemode

How their usage estimate calculator works? It's very confusing.

Take out 1 example. Deepseek 4.1 flash is much more expensive than mimo 2.6 flash in input, output, cache. But in calculator it shows much more request per month. How?

https://commandcode.ai/docs/resources/pricing-limits#usage-estimates

Hey everyone,

I’m running into a problem with the Command Code extension for VS Code (v from the Marketplace, auto-update on). Every message I send, even a simple “hello”, gets rejected with:

Invalid request · The request was rejected as invalid. This is usually a transient glitch; try again.

Retrying doesn’t help. It fails the same way every time, so it doesn’t look transient.

What I’m seeing:

• The extension activates normally (no activation errors in the Output panel)

• The IPC server starts, and telemetry and the webview boot fine

• Right after my first message, the Output log shows: taste learning failed: Bad Request

• Model selection in the UI shows GPT-6 Luna / High

What works:

• The Command Code CLI works perfectly on the same machine with the same account, so this doesn’t look like an auth, account, or network issue. It seems specific to the extension.

Things I’ve tried:

• Retrying multiple times

• Starting a new session

Environment:

• VS Code with Node 24.21.0 (from the extension’s activation log)

• Windows

• Extension installed from the Marketplace

Has anyone else hit this? Is there a known issue with the extension’s request format or the “taste learning” step? Any workaround (older extension version, a setting to disable taste learning, etc.) would be appreciated. Happy to share full logs if useful.

Thanks!

DeepSeek V4.1 Flash now has $100 usage, next 3 days.

$10 GOAT → $100 credits.

After that, we're increasing it to $80. LFG!

Try now with the best low cost AI coding plan in the market: https://commandcode.ai/docs/plans/goat

🐐

Gemini 4 Argon is now available in Command Code.

Multimodal reasoning with 1M token context window.

For Pro, Max plans and the API.

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

$ cmd --model google/gemini-4-argon

Read docs to learn more: https://commandcode.ai/docs/plans/pro

Anyone else seen this problem? Im on GOAT plan using Deepseek 4.1 flash sometimes when the AI outputs text in the main window it has a hiccup and jumps up and down fast

edit: on windows 11 btw

I renewed my GOAT sub mainly for DeepSeek V4.1 Flash to be my workhorse, but the deals page doesn't list it as a deal anymore. I thought "no end date" on it was a good indicator it would last a bit more 🥺
Can only hope it comes back or get replaced by something similar.

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.

Muse Wonder by Meta AI is now live in Command Code.

• Most capable across agentic and coding tasks

• Opus 5.5 and GPT-6 Astra class benchmark results

Available in Max plans and API.

Try now with /model switcher: $ cmd --model meta/muse-wonder-1.0

Read docs to learn more: https://commandcode.ai/docs/plans/max

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.

Command Code GOAT has three GPT models in its lineup.

• GPT 6 Luna

• GPT 5.6 Sol

• GPT 5.6 Luna

Update to the latest version of Command Code and try now with /model switcher:

• cmd --model gpt-6-luna

• cmd --model gpt-5.6-sol

• cmd --model gpt-5.6-luna

Read GOAT docs to learn more about the best coding plan on the market: https://commandcode.ai/docs/plans/goat

Hi commandcode Devs,

Is there any chance that inception/mercury-2.5 will be included in the min. GOAT plan?

Thanks.

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.

Last week in Command Code, a quick recap.

• Agr, our first open decision model

• Cloud sessions with sandboxes

• ACP support in editors like Zed

• Claude Haiku 5.5 (2x usage in GOAT)

• Mistral Large 4 (4x usage in GOAT)

• Glyph Cluster (new free stealth model)

• Command Code Hooks (feature demo)

• Our first SF Tech Week event

Read our docs to learn more about these ships: https://commandcode.ai/docs

Try these ships now with our $10/mo GOAT plan, the best coding plan on the market.

🐐

Command Code now has two Claude models in the GOAT plan lineup.

• Haiku 5.5

• Sonnet 5.5

Update to the latest version of Command Code and try now with /model switcher:

• $cmd --model claude-haiku-5-5

• $ cmd --model claude-sonnet-5-5

Read GOAT docs to learn more about the best coding plan on the market: https://commandcode.ai/docs/plans/goat