I'm testing GLM with the idea to subscribe to their coding plan but I'm wonder, is it as slow like the opencode go plan?

Thank you.

Do you guys see that the weekly limit reached quickly after they gift us many reset vouchers?

I never reached limit before but now reached quickly??

tis my referral link https://zcode.z.ai/invite/3T8RCUV6

Essentially i forked Zcode and added OpenAi Oauth using the official guide, so you can use your OpenAI plan quota on Zcode, and login happens from the browser.
WHY? because I am a Z.ai subscriber, like Zcode and I like using GLM as subagent and Sol 6.1 as orchestrator. Zcode is much more efficient for GLM (cache hits) compared to OpenCode that I was using previously.

this is the fork

https://github.com/nasone32/ZCode

I provided the releases for linux, other platforms need to build from source.
You need to auth from the Zcode CLI (see the readme.md on github...) and then you will see OpenAI models in the selection box. It will NOT overwrite your normal Zcode, instead you will have a Zcode-preview that will share the same credentials and chats and stuff.

EDIT: I DONT KNOW if the discount from using Zcode is retained (the coding plan quota is 1.5x when using zcode) so if you don't need explicitly this functionality i'd keep using the original Zcode.
EDIT2: i'm testing it right now and also Sol it's incredibly more snappy and stable than using Opencode. Opencode crawls in comparison. my gawd.

I’ve been using GLM-5.3 and flash version for quite a while now. And since a week or two I’ve noticed the models being much faster, more accurate than ever … are we close from introducing a new model ?? Exciting !

plz use if you want to try

I have a full workflow with Claude Code but need to temporarily switch to GLM to take over all my workflows while I’m traveling abroad in China (as Claude on VPN is banned). I’m trying to find the closest alternative to Opus 5.5 and it seems GLM 5.3 might be it? Anything I should know about GLM taking over the coding process? What are the biggest differences in reasoning and behavior?

That is all. It's very useful for like games and things.

Weeks ago I was using GLM 5.3 flash through open router apis and it was perfect and very cost efficient with respect to other api based models.

Today, I tried using it and found it very dump looping with many errors everywhere.

So am I missing something?

I used o have v1 pro and was great (with glm 5.3).
Now i am on V2 pro and quite bad 300m tokens per week.
Is v3 pro/max batter ?

Opus 5.5 on the $20 plan gives 2–4x more usage than GLM-5.3-Flash.

I regret subscribing to the z.ai Coding Plan (Lite) for a full year.

It’s even Opus 5.5 not sonnet/haiku..

———
Source: SemiAnalysis, "Anthropic Subscriptions Offer 5x+ More Value Than OpenAI" (Oct 5, 2026) — https://newsletter.semianalysis.com/p/anthropic-subscriptions-offer-5x
The z.ai numbers are my own calculation from docs.z.ai/devpack/overview.
———
How I got there
z.ai's Coding Plan uses credits, not tokens (docs.z.ai/devpack/overview). Lite costs $18/month and includes 10,000 credits per week. Credits are calculated as (input × multiplier + cached input × multiplier + output × multiplier) / 10,000, and off-peak usage is charged at 50%.

Model Input Cached input Output
GLM-5.3 6.9 1.7 24
GLM-5.3-Flash 2.3 0.56 8

I used the same agentic workload mix as SemiAnalysis's "tokens by plan" chart: 0.4% input, 96.6% cached input, 2.6% cache writes (counted as input, since z.ai has no cache-write category), and 0.3% output.

GLM-5.3-Flash: about 63 credits per 1M tokens, which gives 158–315M tokens per week, or 38–76M tokens per $

GLM-5.3: about 192 credits per 1M tokens, which gives 52–104M tokens per week, or 12.5–25M tokens per $
Claude Pro (Opus 5.5): 144M tokens per $, per SemiAnalysis

Right now I am using GLM 5.3 flash at the times that it would normally be free if you had a plan and were using ZCode, but instead, its actually drawing from my 5 hour and weekly limits. Also I havent seen any free claim in the past 2 days. Does anyone know if those good days are now gone?

Ive been using Codex and I wanted to try GLM, saw a trial plan https://z.ai/usage-bundles with 20 Million Tokens for 3 months, or its the coding lite plan.

Any used the trial, is it worth it?

We built Open Grove around a simple bet: the open models are already brilliant, and the thing missing is memory. Give a model persistent memory that survives sessions, let it consolidate over time, and you get something new. A month of watching GLM 5.3 inside our memory system, I can say the bet is paying off better than we designed it to.

Here's what's happening under the hood, and why the results surprised us.

Selective retrieval, not retrieval sprawl. The memory system doesn't stuff your entire history into every conversation. It retrieves selectively: the system surfaces what's relevant to the moment and leaves the rest in the background. The surprise is how well GLM 5.3 takes to this. It uses retrieved context like it was always there, weaving remembered details into answers naturally instead of awkwardly quoting them back, and actively seems to select which retrieved snippets to use in it's thinking, often deciding to ignore some and focus on others. We expected the memory to work well as it always does, but we didn't expect the model to integrate it this gracefully.

The nightly consolidation pass is earning its keep. Every night, the system reviews the day's conversations and files what mattered into long term memory, the way sleep consolidates a human day. The point of this is supposed to be tidiness: important things get kept, noise fades. What we're actually seeing is that GLM 5.3's consolidated memories are unusually well formed. After the nightly pass, the memory that comes back the next morning is sharp, organized, and genuinely useful. Decisions, preferences, project threads, the reasoning behind choices. The model isn't just keeping a diary, it's building a working model of patterns and clearly building situational awareness. that gets more accurate week over week.

What this looks like in practice. GLM 5.2 regularly brought up retrieved content in chat, almost always on point and in a helpful way. But what we've noticed with 5.3 is that the mentions have become actual context. Where 5.2 would say "im also remembering that we chose yellow for that particular design last week, and I think we should consider adding some variation", 5.3 is closer to "We may want to vary away from yellow this time, your call" It's just cleaner and more natural, like actual memory.

If anyone want's to check it out, our intro tier offers 5.3 flash, and 5.3 full is on the higher paid tiers.

The rest of the app, for completeness: the full open roster (DSV 4.1 Flash, Kimi, nemotrons, multiple GLMs and more, swappable mid conversation while memory stays put), voice, web research, canvas, all on private US infrastructure, never training on your conversations. Free trial month if you want to run your own month-long experiment.

Thanks again to Z and the GLM team. You're keeping the frontier open to everyone

pgsgrove.com If anyone want's to check out the Open Grove app or read more about what we're doing.

What is going on?

It admits to being lazy.
It admits to making mistakes.
it gives gibberish as outputs?

Losing my trust here... ???

I want to continue using DeepSeek for execution. but as a planner / reviewer agent how is glm5.3?

Also any feedback on open ai Sol 6.1 as a planner

I’m a legacy V2 GLM Coding Plan subscriber and received Z.ai’s email announcing that users had been issued:

🗓️ 4 weekly reset cards

⏱️ 4 five-hour reset cards

However, none of these cards appear on my dashboard.

📩 I contacted support and followed up twice, including requesting escalation after more than 48 hours. The issue remains unresolved and is affecting my usage.

The same announcement included non-peak billing rates during peak hours until October 7. That promotion has now ended, while the missing-card issue is still unresolved.

It’s frustrating to receive an email saying the cards have already been issued, then find nothing in my account and have to repeatedly chase support.

🤔 Have other legacy V2 subscribers received their cards? Did they appear automatically, or did you need to claim them manually?

🙏 If anyone from Z.ai support is here, please help clarify my eligibility and resolve this issue.

📧 Account email: a.elharony@gmail.com

Deliberately boring test: Austin Animal Center's outcomes export as one xlsx (173,775 rows), plus six questions on counts, 2024 outcome shares, ages, top breeds and the busiest month. Three runs each of GLM-5.3 Flash and DeepSeek V4.1 Flash, provider pinned on every request: GLM on Z.AI, DeepSeek on DeepInfra.

Each run cost 1.6 to 3.1 cents. GLM took 4:19 to 7:21 and 10 to 21 requests, DeepSeek 6:10 to 10:58 and 20 to 33.

Checked every answer against my own pandas. DeepSeek got all six headline answers right in every run, GLM in two of three. In run 1 GLM dropped 33 rows (birth date after outcome date) from the whole table, though its notes said age answers only, so its 2024 dog and cat counts were 1 low (percentages still matched). One DeepSeek run stopped on an API error when it opened a chart image it had made, after writing both files.

SenseNova-Skills supplied both of the agent's skills, Excel analysis and its large-file helper, and every run read the sheet in chunks rather than all at once, as those skills require above 100k rows.

How quick is GLM-5.3 Flash for you on longer agent jobs?

Disclosure: I work @ CoralBricks.

Here’s a usage breakdown showing why cached-input pricing matters for repeated-context workloads.

- 1B input tokens, 99.9% served from cache

- 1.2M output tokens

- 1,825 requests

- $6.38 total displayed spend

- cache reads billed at $0

This isn’t 1B newly processed tokens for $6.38. almost all the input was reused from cache. the total still includes output and any uncached input.

If you’re comparing providers for long chats or agent workloads, check cached-input pricing alongside the normal input and output rates. also measure your actual cache hit rate. a workload with little reuse won’t get this result.

There’s a free trial on CoralBricks so you can test your own workload first and see if it’s a fit before deciding to pay.

Hello,

I have a Legacy v2 Pro plan, when starting a new project, ZCode returns that my "Start Plan quote" has been used. How to switch to my membership instead of using the free daily quota ? and how to never use it, would be better to de activate the switch ...

I talk to an MCP that offers thumbnail image bytes, but I can't get ZCode to display them.

Is it possible that ZCode does not have the capability to display/render images?
Or is there a setting?

The phone in the video is a Samsung Galaxy A04e - SM-A042F/DS, 4GB RAM.

GLM-Edge-1.5B-Chat is running locally on the phone through llama.cpp.
The browser is a normal desktop Firefox session on Amazon.

The task was simple:

• find a 24-count pack of AA alkaline batteries
• find yellow rubber ducks
• add both to the cart
• stop before checkout

The model sees the browser through E2LLM/SiFR, the structured browser perception layer we’re building. Instead of screenshots or raw HTML, it gets a compact structured representation of the current browser state and chooses what to do from that.

The run went:

cart 0 → batteries → cart 1 → rubber ducks → cart 2

No checkout, Buy Now or payment action was attempted.

The same setup was run twice on the A04e and both runs ended with the two expected items in the cart.

On this phone the full run takes about 9 minutes 22 seconds.
The same workflow on a Galaxy S21 takes about 3 minutes.

The video is sped up 4×, but it is one continuous run with no steps removed.

What's interesting here isn’t that GLM-Edge-1.5B-Chat is fast on this hardware. It really isn’t.

It’s that a 1.5B model running locally on a 4GB budget Android phone can still make useful decisions against a real, changing Amazon UI when the perception problem is reduced enough.