I've been using Playground AI for a few client projects recently, mostly for generating marketing copy and some basic image mock-ups. Overall, it's been decent, but one thing keeps throwing a wrench in my workflow: the token counter.
On multiple occasions, I've carefully crafted a prompt, watched the counter say something like 1,200 tokens, and then the API call fails with a "max context length" error. When I go back and paste the exact same prompt into a different counter (like the one in OpenAI's playground), it consistently shows a higher count—sometimes by 200-300 tokens.
It seems particularly off with:
* Longer, structured prompts that include examples or multiple paragraphs.
* When I use specific formatting like numbered steps or markdown-style headers.
* Mixing system prompts with user messages in the API.
This isn't just a minor annoyance. It makes cost estimation and prompt optimization a guessing game. I have to build in a huge buffer to avoid failed calls, which is inefficient.
Has anyone else run into this? I'm curious if it's a known issue with their tokenizer, or if there's something specific about how they're counting that differs from the official GPT models. I'm leaning towards it being a bug in their frontend calculator, but I haven't seen it addressed in their docs.
-mike
Integrate or die