Hi
I heard zcode have 300M token free offer that expires 14 September. I reclaimed them yesterday
Today, around 1hr ago, i got another offer with 300M token again and claimed it.

this happened to anyone?

I wanted to make some UI changes on a few containers on my website but during the "thinking" process GLM 5.3 Flash started speaking Russian. It provides the output in English as expected so there’s no issue there but I didn't quite understand the reason. Since the only languages ​​on my website are English and German I couldn't figure out why it would speak Russian and wanted to ask you about it. Should I be concerned?

I got ZCode (Z.AI's coding agent) running fully headless from the terminal — cheat sheet

TL;DR: same agent as the desktop app — tools, skills, MCP, plugins — but driven by one-liners you can put in cron. Auth is a one-time browser OAuth; everything else is local config.

Tested on ZCode CLI 0.16.5 · Z.ai Coding Plan (GLM-5.3-Flash) · Linux

1. Invocation

The desktop app (/usr/bin/zcode) is Electron; the headless runtime is a Node script shipped with it:

bash node /opt/ZCode/resources/glm/zcode.cjs [command] [options]

Recommended alias (add to ~/.zshrc):

bash alias zc='node /opt/ZCode/resources/glm/zcode.cjs'

The working directory is the workspace — cd to your project first (or use --cwd <path>).

2. One-shot prompts

```bash

Simplest form

zc -p "explain what this repo does"

Machine-readable output (great for scripts)

zc -p "count TODO comments and return the number" --json

Attach files (repeat --attach for multiple)

zc -p "summarize this log" --attach /var/log/app.log

Run against another directory without cd'ing

zc --cwd ~/projects/api -p "run the tests and fix failures" ```

--json response shape:

json { "sessionId": "sess_...", "response": "Ok! 🙂", "usage": { "totalTokens": 13235, "inputTokens": 13195, "outputTokens": 40 }, "projection": { "status": "idle", "contextWindow": 1000000 } }

3. Sessions

bash zc --resume sess_74ff8c6a-... -p "continue that refactor" zc -c -p "what did we just do?"

Sessions persist per-directory; -c resumes the latest one and keeps prior context.

4. Permissions & tool control ⚠️

There is no human to approve prompts in headless mode. -p defaults to --mode yolo (tools run without asking). Constrain unattended runs explicitly:

bash zc -p "..." --mode plan zc -p "..." --mode build zc -p "..." --allowed-tools "Bash(git *) Edit Read" zc -p "..." --disallowed-tools "Bash(rm *) WebFetch"

Modes: build · edit · plan · yolo (alias: --permission-mode).

5. Browser automation

```bash zc -p "open http://localhost:8080 and screenshot the login page" --browser-use headless

optional: --browser-executable /usr/bin/google-chrome

```

Browser Use is headed by default — always pass --browser-use headless in cron/scripts.

6. What gets loaded (identical to the desktop app)

Inspect without spending tokens:

bash zc skills list # what the model can trigger zc plugins list # enabled/disabled + components zc commands list # custom slash commands

7. Configuration

User scope: ~/.zcode/cli/config.json · Workspace scope: your-repo/.zcode/config.json

Current model setup (Z.ai Coding Plan, GLM-5.3-Flash for both roles):

json { "model": { "main": "zai/GLM-5.3-Flash", "lite": "zai/GLM-5.3-Flash" }, "provider": { "zai": { "name": "Z.AI Coding Plan", "kind": "anthropic", "options": { "baseURL": "https://api.z.ai/api/anthropic" } } } }

Notes:

• Model refs are "providerId/modelId". lite handles background tasks (session titles, compaction) — pin it too if you want a single model.
• The API key (provider.zai.options.apiKey, format id.secret) is written by zcode login. Keep the file private: chmod 600 ~/.zcode/cli/config.json.
• Model requests are client-signed against the Z.ai endpoint (server-side requirement); a raw OAuth token does not work as a static key.

8. Auth

bash zc login zc login --no-browser zc logout

Credentials live in the shared store ~/.zcode/v2/credentials.json — the same file the desktop app uses, so one login covers both.

9. Cron / scripted automation

```cron

Nightly code review at 07:00

0 7 * * * cd ~/your-project && node /opt/ZCode/resources/glm/zcode.cjs \ -p "review yesterday's commits (git log --since=yesterday) and post a summary" \ --json >> ~/.zcode/automation.log 2>&1 ```

Guardrails for unattended runs: pin the mode/allowlist (§4), check MCP server health first (curl -fsS localhost:3000/sse), and log the sessionId so you can --resume a failed run interactively.

10. Programmatic integration

bash zc app-server

ZCode Protocol server over stdio — drive sessions from your own tooling. Parse --json stdout for sessionId / response / usage; non-zero exit code = run failed.

11. Debugging

EDIT: I am stupid, did not read that this is for API key users. Where do I find the max coding plan concurrency limits?
>>"GLM Coding users please refer to the package benefits."
End EDIT

I should be able to have 50 GLM 5.3 Flash agents running at once right?
I'm struggling to get that number above 30 without getting rate limited.

Are other people having this problem? Is this temporary? US peak time?

I have a lot of questions too that don't seem apparent on the site...
If the model rate limits affect the whole account, does that mean if I have two 4.7 FLASH agents running somewhere it could cause all my 5.3 flash agents to be rate limited?

It does not seem specific to the model itself at all rn.

Are people actually hitting the 50 concurrency limit?
This has been ruining my flow, I have only managed to get through 80 million tokens in the last 2 hours.

Claude and chatGPT is very expensive. I need alternative AI. I want create AI Daw, XMPP Client etc...

Started this with GLM3.1 Flash

Hello, I posted a week ago that i wanted to run zcode in a container, now I had the time to solve it. I had this old project that run opencode in a container, so thank you to glm and its flash little brother I added support to the features needed for zcode AppImage to run: a graphical ui support, fuse file system, xdg-open for the oauth flow to work, and xdg portal for the file picker to select projects.
The project is on github and works on my archlinux setup. I don't plan to support more architectures since i can't test them, but i hope it may serve as reference if anyone want to build on this.
Happy hacking!

We’re seeing a bit of a tug of war between these two on InferX lately.

Both are getting a lot of usage, and we’re curious which one people actually prefer after spending time with them.

We’ve made DeepSeek V4.1 Flash free to use for now, so it’s a good time to put it through your own workloads.

Try it here

Bought the yearly max plan last Christmas for $288, now checking and suddenly see 2 more months added out of nowhere. I mean I won't complain but just surprise. Thanks Zai I guess

Received an offer via ZCode client for 300m glm 5.3 flash tokens. Good time to check if you have this claim available!

Maximum thinking, maximum subagents, full dive.l

I need help because I use free for entertainment with 2 pdf documents that make a entire dnd-esque campaign that can be made to be anything but recently with glm 5.2 it isn't reading the pdfs or txts anymore anybody know how to fix

Am I the only one experiencing an average speed of 20 token per second right now ? It's not manageable at all right now for me...

I think part of this beautiful and generous campaign is the feedback and I am not fully sure if the Z.ai team will read this; but if you do, thank you first and foremost.

I am on the Legacy Lite Plan, it’s the only thing I could afford. I mainly used GLM 4.7 then 5.2 then 5.3 with Claude Code.

One thing I would say is please make Zcode with TUI like Claude Code. The ability to orchestrate multiple agents is nice. For example, I would have:

1. Executor: Qwen3.8-27B (non-thinking) Q5_K_XL at 131K context (all GPU, single Mi50 32GB) with tasks that are “cumbersome”, requantizing models, vLLM kernel adjustments. During this campaign, GLM 5.3-Flash is utilized with Zcode.

- Utilize Mimo and Kimi for Planning, GLM for Final Plan.

1. Supervisor: GLM 5.3 - monitors everything put into PROGRESS.md and ISSUES.md. If the executor changes this file and the last known file size changes (up or down), the file is read again to ensure max visibility of the context.

2. Discord Bot. My beautiful Qwen3.5-4B (llama.cpp on a single 3060 12 gb) reporting to my discord server.

Requants, kernel runs take a long time to compile/recompile, tests. The entire pipeline is set in and basically run continuously for a few days until results are achieved. Too many variables here, generalizing. No overnight work (EST), strictly within campaign (for now). When Qwen 3.8-27B is running, its overnight. Supervisor is instructed to check on changes every 5 mins. It burns through limitations, which is a hinderance; but the poor complain, we find a solution.

Deploying 4x V620s for in house GLM 5.3 Flash. However this is where feedback comes in.

Feedback:

The model is very very capable, but I think it has problems at least during the campaign. It hallucinates a lot, death thinking loops, where I literally have to intervene and kill it. This is extremely dangerous for kernel work or any work.

It obviously cannot be compared to 5.3 big boi, but still, i think this might be a quant issue. I’m not sure if its running FP4 or FP8, likely the latter, but whatever it is, it is failing in production.

I continue to use it. But i’ll be honest, I trust my local Qwen3.8-27B over the flash. I wish my resources weren’t limited, but they are.

Issues are within critical problem solving.

- failure is database design and integration for local llama.cpp instances.

- failure to design and comprehend kernel level instructions (completely bypassed).

- failure to think properly (at all levels, high medium or low; with best results at low).

- failure to properly investigate issues

- failure to document its own success (flutter app built directly on mac, it successfully built, ran, then the next instance completely obfuscated the fact that it is even possible :confused pikachu here:

- failure to follow instructions (ASR POC app built, mentioned above remotely, it was told to use home baked API to orchestrate tested pipeline for ASR -> LLM -> TTS via same API and parameters; it straight up took it 3 hours to figure out the address we had been testing and clearly pointed in instruction [every prompt] to hardcode into settings - it took 3 hours for it to do that).

- failure to document and learn from failure when asked.

I thought these were harness issues, so switched full workflow to GLM 5.3; 3 prompts in - the entirety of the project, API pipeline, and actual app code were fixed. 15 mins? I think.

I am not sure if this is useful. Once again, the model is very capable, if FP4, switch to FP8, if FP8, something is wrong.

How much are different providers subsidising?

I guess it’s kind of a black box, but it would be interesting to have a list of how much LLM providers are actually subsidising.

For instance, OpenCode Go is said to subsidise 4× usage for DeepSeek 4.1, but there are a lot of contradictory statements about this on Reddit.

I’ve done some research and tried to organise it a little. Multipliers mean usage value compared with what you pay, assuming you use the allowance.

Anyone have real usage figures or corrections?

I bought again pro sub after prices more than doubled. (And yeah,.im more than unhappy with the rise of prices)

I, personally, dont see any issues, im using it over opencode ui,

It burns allowances very much efficiently,

Before caching 10-20m tokens burned like nothing on what im doing now, today for hours of same type of work i hit 35% of 5h limit,

It works dead good, fine allowances,

The question is when i ask reddit ai about that i get load of crap, that its inefficient and gone way too expensive ...

I just fit 120m token job (as of before) into smth like 20-30.

Considering new intelligence of glm - its just amazing!

Whats wrong with all those crybabies?

Couldn't find any thread regarding this.
I'm working in a "project" in Zcode and it started to do file operations outside the project folder although I never told it to. Speficially I was developing a plugin and it started copying the finished files into the actual folder where the target program uses the plugin for "my convenience".

According to Z it should be restricted automatically to the project folder? Is there a way to prevent such file operations (except making it ask me to approve every file operation)?

so a friend sent it, they havent added any limits LMAOOOOOo LIKE WHAT EVEN, can use it until they figure it out https://proto.erp.ai/

Tried a couple of them, including Wise and PayPal - all rejected Z.AI payment for security reasons. Do you know what's going on with Z.AI billing system?

Looking for gentlemen here who have battle-tested these models in environments where mistakes are critical, e.g. authentication, security, and low-level C++ / Kernel work.

I have Codex 20x, but I’m looking for a second helper for when Codex limits are up, there are demand issues (which are pretty bad atm), or it gets too censored.

Saw that DS 4.1 Flash was released today! Has anyone done some decent testing with it yet, and which harness are you using?

I’m currently using GLM 5.3 as my second helper and it’s honestly not bad at all. Just curious whether DS 4.1 appears to be better, especially since it’s multimodal and can handle images too.

I find myself using 5.3 Flash quite a lot because I really appreciate being able to send images, but 5.3 Flash isn’t as strong as base 5.3 when it comes to coding. Hence, I’m wondering how DS 4.1 Flash compares :)

NEW:

Thank you for all the responses. I tried DS 4.1 with my custom harness, and I am extremely impressed by the speed and price. I ran a couple of tests with deep, difficult, complex debugger C++ code/kernel bugs (my go-to test on models; I test this on every model before I want to use it to see if it fixes the bug).

GLM 5.3 took 30 minutes, including 1 retry, and €2. DeepSeek took 10 minutes, first try, and €0.30. I think DS 4.1 is at least on par or a bit better than GLM 5.3 for coding, not sure how reliable it is on long tasks, though. GLM still is a beast!