Counterspeech offers a way to tackle harmful content online without restricting freedom of expression. This work explores counterspeech generation using small language models (SLMs) as lightweight and cost-effective alternatives to large language models. We evaluate SLMs ranging from 100 million to 3 billion parameters using simple prompting strategies as well as fine-tuning, combining automatic and robust human evaluations. Our findings demonstrate that small language models can generate relevant, coherent, and high-quality counterspeech, suggesting their potential suitability for efficient and responsible deployments.