This really captures something I've been experiencing with Gemini lately. The models are genuinely capable when they work properly, but there's this persistent truncation issue that makes them unreliable in practice.

I've been running into it consistently, responses that just stop mid-sentence, not because of token limits or content filters, but what appears to be a bug in how the model signals completion. It's been documented on their GitHub and dev forums for months as a P2 issue.

The frustrating part is that when you compare a complete Gemini response to Claude or GPT-4, the quality is often quite good. But reliability matters more than peak performance. I'd rather work with a model that consistently delivers complete (if slightly less brilliant) responses than one that gives me half-thoughts I have to constantly prompt to continue.

It's a shame because Google clearly has the underlying tech. But until they fix these basic conversation flow issues, Gemini will keep feeling broken compared to the competition, regardless of how it performs on benchmarks.

https://github.com/googleapis/js-genai/issues/707

https://discuss.ai.google.dev/t/gemini-2-5-pro-incomplete-re...

Another issue: Gemini can’t do tool calling and (forced) json output at the same time

If you want to use application/json as the specified output in the request, you can’t use tools

So if you need both, you either hope it gives you correct json when using tools (which many times it doesn’t). Or you have to do two requests, one for the tool calling, another for formatting

At least, even if annoying, this issue is pretty straightforward to get around

Unfortunately Gemini isn't the only culprit here. I've had major problems with ChatGPT reliability myself.

> I've been running into it consistently, responses that just stop mid-sentence

I’ve seen that behavior when LLMs of any make or model aren’t given enough time or allowed enough tokens.

Small things like this or the fact that AI studio still has issues with simple scrolling confuse me. How does such a brilliant tool still lack such basic things?

chatgpt also has lots of reliability issues

The latest comment on that issue is someone saying there's a fix available for you to try.

I wonder if this is because a memory cap was reached at that output token. Perhaps they route conversations to different hardware depending on how long they expect it to be.

Yes agree, it was totally broken when I tested the API two months ago. Lots of failed to connect and very slow response time. Hoping the update fixes these issues.

That used to happen a lot in ChatGPT too.

FWIW, I think GLM-4.5 or Kimi K2 0905 fit the bill pretty well in terms of complete and consistent.

(Disclosure: I'm the founder of Synthetic.new, a company that runs open-source LLMs for monthly subscriptions.)