GLM 5 Error 1302 Fix for High Concurrency
GLM 5 error 1302 usually means the provider thinks you are sending too many overlapping requests at the same time, not that your account is broken. Z.AI’s official error codes define 1302 as high concurrency usage, while 1303 is about high request frequency, so the fix starts with reducing parallelism before you start blaming billing, prompts, or model quality.
This trips people up because GLM 5 is built for agentic coding and long running tool workflows, which makes it easy for editors, wrappers, and coding agents to fan out several requests at once behind the scenes. That is especially common in tools that stream, retry, call tools, inspect diffs, and open multiple background tasks from one user action. Z.AI’s own GLM 5 overview positions the model for agent engineering, and recent community reports from OpenCode users show the exact 1302 pattern appearing during normal coding sessions rather than only during obvious stress testing.
What error 1302 actually means
Error 1302 is a concurrency limit signal. In plain English, the platform is telling you that too many requests are in flight at the same moment for your current route, plan, or provider allocation. That is different from a pure requests-per-minute limit, which is closer to “frequency,” and different again from quota exhaustion, which is what later codes like 1308, 1309, and 1310 describe.
If you see a 429 wrapped around code 1302, treat it as a throttling event, not as proof that your API key is invalid. The wording in the official docs is blunt: reduce concurrency or contact support to increase limits. Community issue threads line up with that interpretation, with users reporting they still had plan headroom but were blocked by overlapping request behavior.
Why GLM 5 hits high concurrency so easily
The short version is that modern coding agents do not behave like one simple chat box. One prompt can spawn a chain of calls: planning, tool selection, tool output summarization, file reading, follow-up completion, and automatic retries. When the client does that in parallel instead of serially, your “one task” can turn into several in-flight API requests. That is why reading about parallel agent worktrees is useful here: the same pattern that speeds up coding also multiplies concurrency pressure if you do not control it.
There is another layer to this. Z.AI documents a dedicated coding endpoint for the GLM Coding Plan, separate from the general API endpoint. If you point a coding workflow at the wrong route, or mix general API assumptions with coding-plan behavior, you can end up debugging the wrong problem entirely. Start by confirming you are using the correct endpoint for your workflow before changing client logic.
The most common real world causes
1. Your editor or agent is sending parallel calls
This is the biggest one. IDE extensions, OpenCode style agents, and automation wrappers often queue multiple requests at once. Even when the UI looks idle, background tasks may still be streaming, retrying, or validating outputs. If you have multiple tabs, multiple repos, or multiple agents open, the overlap gets worse. Recent OpenCode issue reports describe repeated 1302 responses that temporarily recover after retry, then fail again once the next pass begins.
2. You enabled retries without controlling concurrency
Retries are supposed to make clients safer, but they can make overload worse if every failed request instantly retries while other requests are still running. OpenAI’s retry guidance and Microsoft’s throttling guidance both recommend exponential backoff, random jitter, and honoring retry delays instead of hammering the endpoint again right away.
3. Streaming requests stay open longer
Streaming is useful, but it also means connections stay active for longer. Z.AI documents streaming on its chat completions API, which means a request may remain in flight while your client is already trying to start the next one. That is a classic way to trigger high concurrency even when your visible prompt rate looks low.
4. Your wrapper treats tools as “free” side work
Tool calls, repo reads, and helper actions are not free from a concurrency standpoint. They may all end up as separate model requests or related network operations. That is one reason articles about secure local inference keep stressing observability: if you cannot see queue depth, active requests, and retries, you will misdiagnose throttling as randomness.
5. You are mixing plan assumptions with API assumptions
Z.AI’s docs separate coding-plan usage from general API billing and also note plan-specific usage windows and model consumption behavior. If you assume “I still have quota left, so 1302 must be a bug,” you can miss the point. Quota remaining does not mean your current concurrency allowance is wide open.
How to fix GLM 5 error 1302 step by step
Set concurrency to 1 first
If you need a fast diagnostic test, force your client to send only one request at a time. No parallel tool calls. No second worker. No background summarizer. No prefetch. If 1302 mostly disappears, you have confirmed the root cause in minutes. This sounds basic, but it is the clearest way to separate concurrency pressure from quota, auth, or prompt issues. The p limit library is a simple example of how to cap active promise-based work in JavaScript clients.
Add a real queue instead of firing requests directly
Do not let every button click, file scan, and tool callback hit the API immediately. Put requests through a queue with a concurrency cap. Start at one active request, then test two only if the workflow stays stable. A queue fixes the hidden overlap that causes most 1302 loops. It also makes your logs readable because you can finally see what was waiting, what was running, and what retried.
Use exponential backoff with jitter
When 1302 or 429 happens, retrying instantly is the wrong move. Use exponential backoff with a little randomness so a burst of failed requests does not all come back at the same moment. That is standard guidance for rate-limited APIs, and it matters even more when your client has multiple workers. Also remember one subtle point from OpenAI’s rate-limit guidance: failed requests can still count against your limit, so aggressive blind retries can dig the hole deeper.
Respect Retry After when it exists
If the response includes a retry delay, obey it. Microsoft’s throttling guidance is clear on this: the fastest recovery path is usually to wait the server’s requested time rather than guessing your own short delay. Even if your GLM client library hides the raw header, it is worth checking logs or middleware so you can surface and honor that value.
Reduce hidden parallelism in your coding tool
Turn off aggressive background features for testing. That includes automatic retries, parallel file analysis, simultaneous agent branches, and speculative follow-up requests. If you are using a multi-agent workflow, scale it back first, then reintroduce one feature at a time. ToolSwift’s piece on parallel agent safety is useful here because it shows how quickly parallel work expands once you split tasks across branches and helpers.
Confirm the endpoint is correct
If you are using the GLM Coding Plan, confirm you are pointing coding workflows at the dedicated coding endpoint rather than the general endpoint. Z.AI documents them separately and explicitly says the coding endpoint is for coding scenarios. Using the correct route will not magically remove concurrency limits, but it prevents a lot of avoidable misconfiguration. You can pair that check with ToolSwift’s broader notes on OpenAI compatible endpoints and observability if you are proxying traffic through your own stack.
Watch active requests and queue depth
If your client can show “running,” “queued,” and “retried” counts, turn that on. This is one of those problems that becomes obvious the moment you graph it. What feels like random throttling is often a neat staircase of overlapping requests. Cloudflare’s rate limiting basics explain the core idea well: rate limiting is about controlling how often actions happen in a time window, and concurrency is the same pressure viewed through the lens of active overlap.
A practical retry pattern that usually works
A safe pattern looks like this:
- Cap concurrency at 1.
- If a request gets 1302 or 429, wait before retrying.
- Use exponential backoff with jitter.
- Honor Retry-After when present.
- Cap the maximum retry count.
- Do not start new optional background requests while one is already retrying.
That sounds almost boring, but boring is what you want here. Rate-limit handling should be predictable. Anything that “tries a bunch of smart things in parallel” is usually what caused the problem in the first place.
Example pseudocode
queue_concurrency = 1
max_retries = 5
for task in tasks:
enqueue(task)
worker(task):
attempt = 0
while attempt <= max_retries:
response = call_glm(task)
if response.success:
return response
if response.code in [1302, 429]:
delay = retry_after_if_present(response) or random_exponential_backoff(attempt)
sleep(delay)
attempt += 1
continue
raise response.error
raise "request failed after retries"What not to do
- Do not assume remaining quota means concurrency cannot be the issue.
- Do not launch several agents at once and then add retries on top.
- Do not retry every failure instantly.
- Do not keep streaming connections open while spawning new optional requests.
- Do not debug this blind. Add logs for start time, end time, retry count, and active requests.
When contacting support actually makes sense
Contact support after you have done the basic engineering cleanup: correct endpoint, capped concurrency, controlled retries, and reduced background overlap. Z.AI’s own error text says to contact customer service if you need higher limits, which implies there are cases where the limit itself is the bottleneck. But support is most useful when you can show clean evidence such as “single worker is stable, two workers always fail,” not just “the model feels flaky.”
Quick answer for most users
If you want the fastest working fix, do this: set your GLM client to one active request at a time, turn off aggressive background retries, add exponential backoff with jitter, and make sure coding workflows use the proper Z.AI coding endpoint. In most setups, that removes the constant 1302 loop without changing your prompts at all.
After that, raise concurrency carefully instead of all at once. One extra worker may be fine. Four might be enough to trigger the wall again. The right mindset is not “what is the maximum I can send,” but “what is the highest stable level that keeps the workflow responsive.” That is how you stop GLM 5 error 1302 from turning every coding session into a retry storm.
