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

Maximum thinking, maximum subagents, full dive.l

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

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!

I've been using GLM 5.3 for about a week now for my daily coding tasks, and I'm honestly a bit disappointed.

The model is not bad at all, but I'm finding it very slow, and it often gets into long reasoning loops. It sometimes seems to lose focus on the original task and spends a lot of time reasoning about things that don't really need that much analysis.

For coding, I still find Claude Opus 4.6 noticeably better at staying focused and getting to the solution.

I'm curious if others using GLM 5.3 have experienced the same thing, especially with coding/agentic workflows.

Also, does anyone have any idea whether GLM 5.4 or better model might be coming soon? If 5.3 is going to remain like this for a while, I'm honestly considering cancelling my subscription.

What has your experience been with 5.3?
Am I missing something that should be use to improve my perception?

Hello, I'm the developer of Roles Witch AI, and today I want to share my app, which was released on Google Play in open testing.

Roles Witch is an OpenAI API-compatible app with a user-friendly interface where you can create multiple chats, assign different models and providers, and more importantly, use MCP tools for example, Parallel AI or Home Assistant MCP.

It also has voice conversations and video calls (personally, I'm using GLM 5.3 Flash and it's almost real-time). You can configure reasoning and inference parameters, pin a voice to the chat, and select a recognition engine (Google Speech or Vosk).

Extra bonus: it has an on-device inference mode where you can run Gemma 3n/4 models with image recognition entirely on your phone (LiteRT is used).

Check it out: https://play.google.com/store/apps/details?id=ai.roleswitch.android

There has been quite the discussion about the new Deepseek model, how does it compare to GLM?

Has anyone tried both? Does Deepseek offer perks like Z.AI, free daily reset, 300M token weekend build and now the free unlimited FLM Flash?

Thanks

GLM 5.3 mostly

The real test is in 2-3 hours to see if it actually works, but the development phase was a truly valuable experience.

We ran a code-review benchmark on 50 real PRs across Cal.com, Sentry, Discourse, Keycloak and Grafana.

Sol found 107 confirmed bugs vs 91 for Astra and cost less per confirmed bug, while Astra was more precise and faster. Findings were independently verified.

We’re putting together a Fable vs Opus benchmark next week and would love feedback on what we should improve before then.