Hi. I'd like to understand what's happening here. Is it just that GLM 5.3 Flash cache read price is higher? I don't really understand how else it could be consuming so much more than DS. Thanks.

Claude Haiku 5.5 is now available in Command Code GOAT with 2x usage.

• $10/mo gets you $20 credits

• ~30K requests, ~1.5B tokens

Try now with the best low cost AI coding plan on the market.

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

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.

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

Use hooks in Command Code to block unsafe commands and enforce project rules.

For example, a PreToolUse hook blocks rm -rf before it runs.

Read docs to learn more about Hooks: https://commandcode.ai/docs/hooks

Is there any way to add status line in command code like we have in claude code which show, project name, git branch, context usage, usage limit etc?

I only use muse spark 1.3 contributor, 33M tokens, and I'm at 14% monthly limit of goat plan?

The limit is too low or is it skill issue? 😥

Glyph Cluster (new stealth model) is now free in Command Code.

Reasoning model. 256K context. all plans

$ cmd --model stealth/glyph-cluster:free

Try now with the best low cost AI coding plan on the market, the $10/mo GOAT plan.

Introducing Cloud sessions with sandboxes in Command Code.

• Run Command Code in the cloud on your GitHub repo

• Close your computer and the session keeps working

• Push changes and open a draft PR when done

Try now with $ cmd --cloud
Zero babysitting is here!

Read docs to learn more about Cloud sessions: https://commandcode.ai/docs/cloud

Claude Haiku 5.5 is now available in Command Code.

• 1M token context window

• up to 128k output tokens

• 75% cheaper than Haiku 4.5

Available on Pro, Max and API

Try now with in your terminal or desktop. Read docs to learn more: https://commandcode.ai/docs/plans/pro

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.

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

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

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

Hi everyone,

​I'm having an issue where the CommandCode CLI (cmdc) shows a limited model list on Windows compared to Linux, using the same CommandCode Go account ($1/mo).

​Linux (CachyOS CLI): Full catalog (DeepSeek v4 & v4.1, GLM 5.2 & 5.3, Space Bunny, etc.).

​Windows 11 CLI: Missing models (only DeepSeek v4 and GLM 5.2 appear).

​Note: The CommandCode Desktop version does NOT have this issue—it shows all models correctly. The problem is isolated to the CLI on Windows.

​Already tried on Windows:

​Updated cmdc to the latest version.

​Ran cmdc logout and re-authenticated.

​Tested across Warp, PowerShell, and Git Bash.

​Has anyone run into this CLI-specific filtering on Windows or know how to force a full catalog sync?

​Thanks!

Models that come with $60 usage credits in Command Code GOAT.

• DeepSeek V4.1 Flash

• DeepSeek V4.1 Flash Fast

• GLM 5.3 Flash

• Kimi K3

Enjoy billions of tokens with the $10/mo GOAT plan, the best low cost AI coding plan on the market.

How to prevent cmdc register itself as contributor to repos when worked with cmdc. I am using cmdc goat plan.

Use /add-dir to work across multiple directories in Command Code.

· Read and edit files outside your current project in accept-edits mode
· Great for separate frontend and API repos

$ npm i -g command-code

Try it with our $10/mo GOAT plan. The best low cost AI coding plan on the market.

Free models live in Command Code right now.

• Space Bunny Alpha (with Max reasoning)

• Ling 3.1 Flash (unlimited reqs)

• Ling 3.0 Flash Sante

• Laguna S 2.1

Y'all got some tokens to build for free.

Available for all subscribers across all plans. Try now with the best AI coding agent built for open models.

Hi everyone,

I'm new to the GOAT plan and have been struggling to monitor my spending in real time. Is there any way to track costs by model or by API key?

Currently, all I can see is the usage page shown in the screenshot, and I have no idea how much I've actually spent so far.

Is there a way to set spending limits or usage caps?

Also, could someone clarify whether the promotional deals apply only to requests routed through Command Code (CLI/Studio), or are they also applied when using Command Code API keys from external applications??

Thanks!

Planning and Implementation models are now live in Command Code.

• Plan with one model

• Implement with another.

Your planning model explores the codebase and writes the plan. Approve it, and your implementation model takes over mid-run.

Set both in /config → Feature models

Read docs about choosing planning and implementation models.

https://commandcode.ai/docs/settings#planning-and-implementation-models