glm-5.2:cloud burned ~15% of my Ollama 5-hour session limit on a single question
Ollama Pro, $20/month, running glm-5.2:cloud from the VS Code extension.
One agent turn. About three minutes wall clock. One question: compare two versions of the same Markdown spec (24k words and 18k words) and list the differences. Nothing else was in the workspace, the extension had access to nothing else.
That single turn took roughly 15% of my 5-hour session allowance. At that rate the whole window is gone in about twenty minutes. I have run much heavier things than this on the same plan for weeks and never come near the limit.
I understand the meter in principle. Ollama's pricing page says each plan has session limits that reset every 5 hours and weekly limits that reset every 7 days, and that usage is based on the model plus the number of input, cached input and output tokens processed rather than a fixed token cap. Fine. What I cannot work out is which of these is true:
- glm-5.2 is simply far more expensive per turn than what I was running before
- something about the metering changed recently
- the VS Code extension is quietly resending far more context than I think it is
The extension shows me no token count anywhere I can find, so I am guessing. Has anyone actually measured a single turn on glm-5.2:cloud against the same prompt on a different cloud model?
@p99_hana · 2h ago · 3 replies
There are two things stacked here and they multiply, so you will not be able to tell them apart by feel.
First, the meter. Ollama's own wording is that limits are based on the model and the number of input, cached input and output tokens processed, and that they deliberately do not cap you at a fixed token count because different models use different amounts of compute. So the real unit is closer to GPU time than to tokens, and a large thinking model generates a lot of it per turn.
Second, your prompt. 24k plus 18k words is somewhere in the region of 55-60k tokens of input before any system prompt, tool schemas or file listings. Then it is a thinking model, so the reasoning tokens you never see count as output. "Find every difference between these two documents" is precisely the task shape that makes a reasoning model produce an enormous amount of it.
Before you conclude the metering changed, get a number. On my machine
ollama run --verbose glm-5.2:cloudprints prompt and eval counts after the response — confirm that on yours. Run your exact prompt once through the CLI, write the counts down, then run the same prompt through the extension and compare how far the session bar moves each time. If the CLI turn is cheap and the extension turn is expensive, it is your harness and not the model.Reply
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@first_repo_finn · 2h ago
That is a fair split and I can run it tonight.
--verbosedoes print counts here for the cloud tag too, I had just never looked at them because I assumed cloud meant no local metrics.Reply
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@p99_hana · 2h ago
Do both from a fresh session. If you were iterating in an existing chat, the extension may be replaying the entire thread every turn, in which case "one question" is really one question plus everything that came before it, re-billed.
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