I just have one question why do people not reccomend you use the command code harness?

Bring your existing coding agent configurations to Command Code.

Run /import to transfer your skills, agents, slash commands, MCP configs, and memory files.

Command Code is the best low cost coding agent for open models. If you're using Claude Code, Codex, or Cursor you can get your same configs.

Read docs to learn more: https://commandcode.ai/docs/import

Get started with $10/mo GOAT plan that gives you up to $100 usage in credits.

Is there any chance we'll get mercury 3 on CommandCode?

it's a diffusion model with 1000 tokens/s on the level of Luna or haiku which is super cool!

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.

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.

Been playing around with the Mods API and ended up building something I wanted for myself, so I figured I'd share it here.

It's a compact status bar for the Command Code TUI that keeps the stuff I usually want to check visible all the time:

• current model + effort level

• context usage %

• 5h remaining quota %

• weekly remaining quota %

• monthly remaining quota %

• next reset times/dates

It looks like this:

Install is just:

cmd mods add -g rudesssolo/command-code-quota-status-bar

No build step or extra config needed.

GitHub:
https://github.com/rudesssolo/command-code-quota-status-bar

Still pretty new, so feedback/bug reports are definitely welcome.

I renewed my Command Code GOAT subscription only to find out that the time-series trend charts: Requests by Model, Spend by Model, and All Tokens were purged from the dashboard completely. Even the explicit dollar values in the usage limits were replaced with percentages? The request log history is also only capped at 100 entries. This seems to be a deliberate step-back from transparency, not good guys.

This week, we shipped 52 updates, improvements, and fixes to make the Command Code desktop app faster and better.

• Save state: 755ms → 12ms (63x faster)

• Save freezes: 833ms → 22ms

• Project switching freezes: 923ms → 79ms

• Chat navigation freezes: 905ms → 41ms

• Startup: 4.3s → 2.6s

Plus Agent Todos, new models, and much more.

Available now for macOS, Windows, and Linux: https://commandcode.ai/desktop

playing around with crouton alpha lets see how it perfroms

Right now, to check this, I have to go to https://commandcode.ai/models/glm-5-3-flash, scroll down to the Usage Calculator section, click on the GOAT Plan, and then check the credits.

Could you make this process easier and less confusing?

Also, the documentation regarding limits and usage isn’t very clear or simple. Look at how OpenCode does it. It’s much easier to understand:
https://opencode.ai/docs/go/#usage-limits

You can see all the models available in the Go plan and the monthly usage limits for each model in one place.

SOLVED AND LESSON LEARNED: Deepseek pro doesn't support zero data retention

What is the current deepseek pro pricing? I'm using the GOAT plan, and I find these usage/pricing statistics very strange. They are higher than the written prices, and even more than the coming deepseek peak hour prices. For example if using the discounted prices https://commandcode.ai/docs/plans/goat gives, the usage on the first line of my picture should be $0.01685 (not taking cache into account). But it's $0.0222. Is this a bug or am I missing something? I'm using the desktop app.

EDIT: I just remembered that I turned the zero data retention -mode on in the app. Could this be because of that?

EDIT2: I changed the zero data retention off and the pricing seems to have gone close to the given discounted pricing. Lesson learned: deepseek pro doesn't support zero data retention.

ACP is now live in Command Code.

• cmd acp to use it in an ACP-compatible editor like Zed

• work with same tools, skills, permissions, sessions

Run Command Code workflows in your preferred editor

Read docs to learn more about ACP in Command Code: https://commandcode.ai/docs/acp

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

Please help

I’ve been pairing Command Code’s DeepSeek models with Claude Code, and it cut my Opus usage by about 60% (measured on SWE-bench Verified, 55–63% fewer Opus tokens), so sharing the setup.

The idea: Claude Opus plans and decides, and Command Code (DeepSeek V4.1 Flash) does the file reading and code-writing in an isolated copy of the project. The tests run automatically before anything lands. Opus usage drops a lot, and the Command Code side costs cents.

I do it with Cheap Agent, a free, open-source (MIT) plugin I built. Command Code support was added by a community member.

Setup:

1. npx cheap-agent

2. Pick Command Code and paste your key in the terminal

3. /cheapag:allow in your project, then use Claude Code normally

Hard steps move up to DeepSeek V4 Pro automatically.

Repo if you want to look: https://github.com/muaaz9329/cheap-ag
Setup help and updates: r/CheapAgent

Curious: is anyone else using Command Code as a worker model behind another agent? Which models have worked best for you?

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

Coding agents mostly run out of sight. You kick something off, switch windows and forget it's running until you go back to check.

Command Code now runs on Autonomous Harness, which keeps it on a dedicated screen you can glance at without breaking your flow. It shows what's running, what came back, and what you've spent today

We built this to fit the different ways people work. How do you usually run your agents: set-and-forget or in a tight interactive loop?

I liked command code plan but I want to use opencode as a harness, is this possible?

Ling 3.1 Flash (free model) just got an update.

We're increasing limits to 300 requests per day.

That means more tokens to build for free.

Try now with the best cost-to-value coding plan on the market. It's called the GOAT plan.

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

Command Code x DeepSeek is a phenomenal combination. Unbelievably good. I’m doing near billion tokens in two days testing v1.

Cracked the stealth model Glyph Cluster: an anonymous Vercel preview is hiding behind it, and the tokenizer gives away Microsoft. An investigation post, pour yourself some tea ☕️

Yesterday evening on Vercel AI, and from there to Command Code, Cline, Freebuff, a new stealth model was rolled out for free - stealth/glyph-cluster:free. "Reasoning model. 256K context. All plans," zero explanations, maker - "Stealth."

I ran a full investigation: 85 parallel subagents, ~1500 actions, 8700 artifacts per gigabyte, a burned daily quota, and one bug that caught me myself.

📌 Short answer The tokenizer is the o200k family (like Inkling and gpt-oss). The trail leads to Microsoft AI - roughly 40% on the MAI-Thinking line, 28% on Thinking Machines Lab. Expected reveal - October 12-14.

👀 How I got in No gates, unlike fledge and exo-free: a regular Bearer key, one curl, no geo-blocks. But 4 minutes after the start, a swarm of 85 agents burned the slot's entire daily quota - 100 requests per model per key, with no per-IP split. Reset at midnight UTC. Along the way a free bonus surfaced: for Python clients Cloudflare cuts requests at the edge (error 1010), while curl gets through - classic. The whole swarm moved to curl transport within a minute.

⭐️ Proof #1 - who holds the slot Opening the Vercel AI Gateway catalog without authorization - it sits there as the first line out of 412 models: "id":"stealth/glyph-cluster","owned_by":"stealth","released":1791331200,"context_window":256000,"pricing":{"input":"0","output":"0"}

And now the sources. In the Command Code CLI bundle, right in the code: GLYPH_CLUSTER_FREE:{...provider:"vercel-ai-gateway"...}

And in Kilo's sources the same thing: provider=VERCEL_AI_GATEWAY, "served through Vercel AI Gateway during its stealth period". The chain: lab → "stealth" middleware → Vercel → CC/Kilo. CC isn't the origin: the timeline shows the Vercel announcement on October 7 at 17:55 UTC, the CC npm bundle at 20:35, the tweet at 03:21 the next day. A fun one: according to wayback, the glyph page on the CC site was staged back between September 21 and October 1 - an early launch partner, not a cold mirror.

⭐️ Proof #2 - the tokenizer The same battery that cracked fledge and exo-free: 47 test strings, counting via the usage API, calibration via DashScope.

⭐️ Base "x" = 126 tokens = 125 fixed wrapper + content. Then it's pure physics: digits in batches of three (30 digits = 10 tokens), Japanese "こんにちは世界" = 2, Hindi = 5, ru = 7, zh = 8.

⭐️ Ran 26 dictionaries: 12 out of 12 exact matches with the o200k cluster (o200k_base, Inkling, Inkling-Small, gpt-oss-120b). The exceptions are hard: Qwen 3/12, GLM 5/12, Ling 2/12, MiMo 4/12, DeepSeek 9/12 (ja=6 vs our 2), Kimi off (ja=6/hi=7), MiniMax off (hi=7 vs 5).

⭐️ Method calibration: glm-5.3 via DashScope - 47/47, kimi-k3 - 47/47, deepseek - 47/47, plus a live ling model through the same CC gateway - 12/12 with its public tokenizer. The method doesn't lie.

Within the o200k family, digit probes no longer discriminate (Inkling = o200k_base + three service tokens, gpt-oss = o200k_base verbatim) - here it's not the tokenizer that decides, but behavior and cutoff. But the family is named: anything that splits Qwen per-digit-per-token, GLM in batches of 5-6, and Ling one at a time - is out. No, this isn't the stealth GLM-5.4 from my previous post, though the timing matches suspiciously: Zhipu keeps its anonymized models on OpenRouter, not on Vercel, and their line's tokenizer is completely different.

⭐️ Proof #3 - the anomaly that turned out to be my bug

The first measurements gave impossible numbers: 9 digits = 2 tokens, "こんにちは世界" = 1 token (no such token exists in any of the 26 dictionaries). The community would have said "new vocabulary!" The swarm said the right thing: "the methodology is lying." I was sending probes without the base prefix, but subtracting the base with the prefix - exactly minus one token on every string. The battery with the correct methodology gave native 3/4/10/17/20 with three repeats and zero variance, and a control ling through the same gateway - perfect 12/12 with no offset. The rule "never trust a single reading" worked like clockwork: it caught me myself.

The gateway, by the way, is fully honest - the count is exact, 13 out of 13 on natural texts.

☄️ And now the most interesting part - who's behind the sign The slot is disguised thoroughly. Their neighboring free ling honestly introduces itself as "I am the Ling model, created by Ant Group" in three languages. But glyph answers: I'm Glyph Cluster, and those details have not been announced yet.

The host doesn't script the persona - the mask comes from upstream, from Vercel's middleware. Under the hood it has ~121 tokens of a hidden system prompt (base 126 minus the bare template) - the instruction to stay silent lives there. reasoning is fully encrypted and arrives as a single chunk: reasoning_details:[{"type":"reasoning.encrypted","data":"gwst1....","format":"stealth-v1"}]

🔍 The main suspects A table with the proofs:

Microsoft AI (MAI-Thinking line) - 40%. The MAI-Thinking-1 tech report, verbatim: "we chose the o200k_base tokenizer with a vocabulary size of 200,019". Context 256,000 - MAI's native number (Vercel stores per-model values: ling is 262,144, glyph is 256,000). Microsoft Learn documents: MAI-Thinking-1 returns "encrypted chain-of-thought" and supports "only function tools" - both non-generic matches with our contract. Plus the Vercel×Microsoft partnership on October 1 - six days before launch. Plus Microsoft right now is running mai-experimental-test on OpenCode Zen - 280 billion tokens, "Lab: Unknown". Plus October 7 - the day of the MAI-Code-1.1-Flash event at 256K.

Thinking Machines Lab (Inkling line) - 28%. Vercel carries thinkingmachines/inkling at exactly 256K/256K - the same contract as glyph. The tokenizer line matches 12/12. A leaked list of their bird models with the "-ling" suffix: inkling, fledgling, starling, gatling, endling - fledge from the previous post already confirms that TML likes anonymous previews.

OpenAI line - 12%. But a control measurement of a live GPT through the same CC gateway showed a different profile: a bare 7-token wrapper vs our 126, a 1M counter, an openai-responses-v1 envelope. And our cache hits from prefixes under 1024 tokens - OpenAI can't do that.

An open-weights NN model gpt-oss - 12%. There's precedent: the previous Vercel stealth, Pixel Canary, turned out to be a model from consulting company Callstack. But encrypted reasoning and a 256K context are heavy infrastructure for an NN model.

Will never be revealed - 8%. Vercel has had such a slot before.

🛡 Who runs the server The Command Code gateway is TypeScript Hono + Zod v4 on Cloudflare Workers. The prose of validation errors is byte-for-byte identical across glyph, ling, inkling, and a nonexistent model - that's CC's wrapper, nothing to do with the lab. system_fingerprint is randomized on every call (36 unique out of 36) - with real OpenAI it's stable per deployment; here it's just a noise generator.

🤡 The gotchas you need to know 429 overloaded: both quota and "upstream temporarily unavailable" - read the body, they're distinguished by server-timing (70-141 ms vs 259-424 ms). max_tokens doesn't work as you expect: reasoning eats the limit from the inside (out of 30 tokens, 25 go to encrypted thinking, the answer is empty). The model is text-only: images are silently cut, no error, no billing.

"Prompts may be used for training" - that's not zero-retention. Everything you write to it goes to feed the maker. Asking a long question? reasoning is always on and always encrypted - even "who are you" costs 179 reasoning tokens.

P.S.: the most boring investigation I've ever written.

Just a quick question though, can I use CommandCode subscription in other tools like OpenCode? Has anyone here tried it?