GLM-5 Error 1302 Explained: Why High Concurrency Happens and How to Fix It

If you’ve recently tried to use the new GLM-5 model for coding and run into Error 1302: High concurrency usage, you know how frustrating it is to have your workflow stall mid-task. This error typically appears when you try to run multiple tasks at once, like having a coding agent write code while another researches documentation. The good news is that this is usually a resource management issue rather than a problem with your code, and it can be fixed with a few practical adjustments.

Quick Answers: Why Is This Happening?

• Too many simultaneous tasks: The GLM Coding Plan, especially Lite and Pro tiers, often limits you to 1 active request at a time.
• Hidden background processes: Tools like OpenCode or Cline may run background agents that quietly use up your single concurrency slot.
• Hardware bottlenecks: The massive size of GLM-5 (744B parameters) has placed heavy demand on servers, leading to stricter limits.
• Different from message limits: You can still have plenty of prompts left in your quota and get blocked if two requests are sent at the same moment.

Why Does Error 1302 Happen?

Concurrency is not the same as your prompt quota. Your subscription might allow 400 messages every five hours, but concurrency controls how many of those messages can happen at the same time.

Because GLM-5 is extremely large and runs on specialized hardware that is in high demand, the platform enforces a strict one-at-a-time rule for most users. If your coding tool sends a second request before the first one finishes, even by a fraction of a second, the server rejects it with Error 1302.

This often happens with modern AI coding tools that run background checks, auto-completions, or file indexing while you work.

Fixes You Can Try

1. Configure Your Coding Tool (Easiest Fix)

Most AI coding assistants allow you to control how many requests they send. Reducing this to match your account limits is usually the most effective solution.

For OpenCode Users:

• Locate your configuration file, usually ~/.local/share/opencode/auth.json or your project config.
• Find the provider section for zai-coding-plan.
• Add or update the setting: “providerConcurrency”: 1.
• Disable the oh-my-opencode plugin if installed, as it frequently polls the API in the background.

For Cline / Roo Code Users:

• Disable Parallel Research in the settings.
• If possible, explicitly set your custom endpoint model ID to glm-5 or glm-4.7.
• Make sure you are not running multiple instances of the tool at the same time, such as in different VS Code windows.

2. Use Model Tiering (Best for Efficiency)

You do not need the full GLM-5 model for every task. Using lighter models for simple work reduces processing time and lowers the chance of traffic bottlenecks.

• Complex logic and reasoning: Use GLM-5.
• Simple tasks like unit tests or documentation: Switch to glm-4.7-flash.
• Why it works: Flash models respond faster, clearing requests quickly so your next task can proceed without delay.

In tools like Claude Code, you can configure this in your settings.json file:

• Set ANTHROPIC_DEFAULT_OPUS_MODEL to glm-5.
• Set ANTHROPIC_DEFAULT_HAIKU_MODEL to glm-4.5-air.

3. Implement a Rate Limit (For Developers)

If you are building your own Python script or bot, you must ensure your application does not send parallel requests. A semaphore can help control concurrency.

Python Example:

import asyncio

concurrency_gate = asyncio.Semaphore(1)

async def safe_call():
    async with concurrency_gate:
        await client.chat.completions.create(...)

This guarantees that only one request runs at a time.

When to Switch Strategy

Use the Pay-As-You-Go API

If you are on a deadline and repeatedly hit Error 1302, consider temporarily switching to the standard API.

• Add a small balance to your Z.ai dashboard.
• Change your tool’s base_url to https://api.z.ai/api/paas/v4/chat/completions.
• This costs extra per request but typically has more flexible concurrency limits.

Host It Yourself (Advanced)

If you have access to enterprise-level GPU hardware, you can host GLM-5 locally using vLLM.

• This removes cloud rate limits.
• Your data stays private.
• It requires roughly 800GB or more of VRAM for practical performance, making it suitable only for high-end compute environments.

What Not to Do

• Do not keep retrying instantly: Repeated retries can trigger Error 1305 and place your account into a temporary cooldown period.
• Do not open multiple sessions: Running the tool in multiple tabs or windows counts as concurrent usage.
• Do not ignore plugins: Background plugins are often the hidden source of concurrency issues.

Common Error Codes Explained

• 1302: Too many concurrent tasks. Fix by waiting for the current request to finish.
• 1305: Rate limit triggered. Pause briefly before sending another request.
• 1310: Weekly or monthly quota reached. Wait for the reset date.
• 1113: Account balance issue. Check your payment method.

Conclusion

Error 1302 is frustrating but common with powerful large-scale models like GLM-5. The solution usually involves forcing your tools to operate sequentially instead of in parallel.

Start by setting your tool’s concurrency to 1, checking for background plugins that might be consuming your request slot, and using lighter Flash models for simpler tasks. If you truly need parallel execution, switching to the pay-per-use API for that session is often the fastest workaround.