Hey folks, has anyone else noticed Poe bot responses getting cut off mid-sentence lately? I was running my usual Looker dashboard analysis through Claude-3.5-Sonnet yesterday, and the response just stopped at a completely unnatural point, like it hit a hidden character limit. It wasn't a network issue—the message was marked as complete.
I use Poe's API for some lightweight data pipeline summaries, and this is causing real headaches. The truncation isn't consistent; sometimes it happens after a few paragraphs, other times after a longer stretch. It feels like a silent, unannounced change.
A few things I've checked:
* It's happening across different bots (Claude, GPT-4) for me.
* The cutoff point isn't at a round number of words or characters (as far as I can tell).
* No warning or "continue" button appears.
If they've tweaked the underlying models or imposed new output limits, it'd be nice to know! It breaks workflows that rely on complete, coherent answers. For cost optimization, incomplete responses mean wasted queries.
Anyone have similar experiences or found a workaround? Could it be specific to certain subscription tiers?
Data doesn't lie, but dashboards sometimes do.
Yeah, I've seen this too with the API. It's not just a UI thing. For me, the cutoffs often happen right after a JSON block or a table in the response, which makes me think it's a token counting bug, not a hard character limit. Super annoying when you're expecting a full summary.
Could be a silent change in how they're handling the context window between the user prompt and the bot's output. Makes cost tracking a nightmare if you're paying per query and have to re-run.
—b
That's exactly what I've been seeing with the GPT-4 bot for my infrastructure summaries. The cutoff feels totally arbitrary.
What's especially frustrating is that it happens more often when I'm asking for a detailed cost breakdown. The bot will list a few AWS services, give partial numbers, and then just stop. It's making the output useless for my weekly reports, and I'm having to double my API calls to piece together the full picture.
Have you checked if it's related to your prompt structure? I found that when I split my original mega-prompt into two separate, sequential API calls, the truncation happens less frequently. It's a hack, not a fix.
terraform and chill
Of course you're seeing more cutoffs with detailed cost breakdowns. You're hitting the exact issue that makes these API calls so expensive for no real benefit. The model is trying to output your laundry list of EC2 instances, RDS costs, and S3 storage numbers, and it's hitting a token limit it can't gracefully handle. Splitting the prompt is just admitting you're asking for more output than the system can reliably return in one go.
I'd argue you shouldn't be using a general-purpose LLM for detailed, structured cost reporting in the first place. You're paying for it to generate pseudo-tabular data that then gets truncated. You'd get more complete, accurate, and cheaper data by querying the Cost Explorer API directly and using a simple script to format it. Using GPT-4 for this is like using a Formula 1 car to haul gravel.
The real problem is treating the symptom. Your "hack" of doubling your API calls is literally doubling your cost for the same information. That's the opposite of optimization. If you're committed to using the bot, you need to force it to output in a more token-efficient way - ask for the top three cost drivers first, then ask for the next three in a follow-up. But honestly, you're using the wrong tool.
pay for what you use, not what you reserve
What you're describing matches what I see when API-level token limits are enforced silently, usually on the server side. The lack of a "continue" or truncation warning is a dead giveaway this is a backend constraint, not a model one.
Since you're using the Poe API, check your response headers for any content-length or x-token-count values. I've had cases where the truncation coincides with hitting a specific token boundary that's lower than the advertised model limit, likely due to overhead from their own system prompt or chat formatting. It's inconsistent because your output token count varies.
For your data pipeline summaries, you might need to implement client-side response validation and automatic continuation requests. It's a pain, but until they document the limit, you're stuck with it. Have you tried explicitly setting a max_tokens parameter below, say, 2000, to see if the truncation becomes predictable?
Show me the benchmarks.