Why Is GLM Long Context Performance Degrading? The Truth Behind Slow AI Responses

Mar 24, 2026

Problem

When I use GLM’s long context feature (100-200k tokens), I notice it has become significantly slower recently. The responses take much longer, and sometimes the service feels unreliable.

This is frustrating because long context was one of GLM’s key selling points. I paid for a subscription expecting this feature to work well, but now it’s degraded.

Environment

GLM model: GLM-4 and GLM-5
Context window: 100k-200k tokens
Subscription: Annual plan

What happened?

I use GLM primarily for tasks that require large context windows—analyzing long documents, code reviews, and multi-turn conversations with extensive history.

The pattern I noticed:

Week 1-2:  Long context works normally, fast responses
Week 3-4:  Long context gets noticeably slower
Week 5-6:  Random outages, sometimes get "server busy" errors
Week 7+:   Service degraded, long context barely usable

Other users report similar experiences:

“I paid for something and I’m not getting it”
“I’m mad I paid yearly for a tool that I can’t use for unknown periods of times randomly”
“They gave us quantized model for unknown reasons”

But here’s the interesting part: short context requests still work fine. Only long context is affected.

The reason

I think the key reason is GPU resource diversion for model training.

Here’s why:

Memory scales exponentially with context length

Context       Memory Required    Cost
─────────────────────────────────────
4K tokens     ~8GB VRAM          Baseline
32K tokens    ~64GB VRAM         8x
128K tokens   ~256GB VRAM        32x
200K tokens   ~400GB VRAM        50x

Long context is the most GPU-intensive feature to serve.

Training cycles compete for resources

Total GPU Resources
├── Inference (Serving Users)
│   ├── Short context (cheap)
│   └── Long context (expensive) ← CUT FIRST
├── Training (New Models) ← PRIORITY
│   └── GLM 5.1 development
└── Fine-tuning & Experiments

When a company is training a new model, they divert GPUs from inference. Long context gets cut first because it’s the most expensive.

Historical pattern suggests GLM 5.1 is coming

Before GLM 5 was released, users reported similar issues with their “lite” subscriptions. About 60 days later, GLM 5 launched. The same pattern is happening now.

Minimax (a competitor) just released m2.7, which suggests the industry is in an active training cycle.

Workarounds

If you need reliable GLM access, consider these alternatives:

1. OpenRouter - Third-party provider with GLM models
   - Pay per token
   - Works even when Zhipu's service is degraded

2. Alibaba Coding Plan - Includes GLM access
   - Optimized for coding tasks
   - Subscription-based

3. Self-host the open weights
   - Full control over performance
   - Requires significant GPU hardware

A Reddit user confirmed: “GLM 5 through other providers seems to work fine… Even openrouter I find pretty good.”

Summary

In this post, I explained why GLM’s long context performance has degraded. The key point is that GPU resources are being diverted for model training (likely GLM 5.1), and long context is the most expensive feature to serve, so it gets cut first.

This is a predictable cycle in AI providers: training begins → service degrades → new model releases → service improves → cycle repeats.

If you need reliable service, use alternative providers like OpenRouter instead of relying solely on Zhipu’s direct service.

Final Words + More Resources

My intention with this article was to help others share my knowledge and experience. If you want to contact me, you can contact by email: Email me

Here are also the most important links from this article along with some further resources that will help you in this scope:

👨‍💻 Reddit Discussion: GLM Long Context Issues
👨‍💻 Zhipu AI Official

Oh, and if you found these resources useful, don’t forget to support me by starring the repo on GitHub!