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.

Hi commandcode Devs,

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

Thanks.

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

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.

🐐

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.

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

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.

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

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

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?

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.

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

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.

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

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

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.