We Benchmarked 27 Open-Source LLMs — Then We Had to Fix Our Own Benchmark
One of the most useful things an LLM benchmark can reveal is that your benchmark is wrong.
We recently tested 27 chat-capable open-source models through DigitalOcean's OpenAI-compatible Serverless Inference API.
The initial goal was simple:
Find models that offer a good balance of quality, latency, and reliability for real-time AI workloads.
We expected to discover which models were fast.
Which ones produced better answers.
Which ones failed under load.
We found all of that.
But we also found something more useful.
Some of the results that initially looked like model or infrastructure failures were actually caused by our own benchmark configuration.
That changed the way we interpreted the entire test.
What We Tested
Every model received the same six Turkish customer-support-style prompts:
- greeting / simple conversation
- how-to request
- short or ambiguous question
- customer complaint
- detailed comparison request
- off-topic question
The models were called through:
https://inference.do-ai.run/v1/chat/completions
using the OpenAI SDK...
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