Benchmarks
Data-driven performance comparisons for small language models fine-tuned with distil labs.
What Small Language Model Is Best for Fine-Tuning
We benchmarked 15 small language models across 9 tasks to find the best base model for fine-tuning. Qwen3-8B ranks #1 overall. Liquid AI's LFM2 family is the most tunable. Fine-tuned Qwen3-4B matches a 120B+ teacher on 8 of 9 benchmarks.
The 10x Inference Tax You Don't Have to Pay
Benchmarking fine-tuned small language models (0.6B-8B) against 10 frontier LLMs across 8 datasets shows that task-specific SLMs match or beat frontier models at 10-100x lower inference cost.
When Does Reinforcement Learning Help Small Language Models?
A controlled experiment across 12 datasets reveals that adding RLVR after SFT consistently improves text generation tasks (+2.0pp) but provides no reliable benefit for structured tasks like classification and function calling.
We Benchmarked 12 Small Language Models Across 8 Tasks to Find the Best Base Model for Fine-Tuning
A systematic benchmark of 12 small language models across 8 tasks reveals Qwen3-4B as the best for fine-tuning, with fine-tuned models matching or exceeding a 120B+ teacher. Smaller models like Llama-3.2-1B show the highest tunability.
distil labs: Benchmarking the Platform
Benchmarking distil labs' distillation pipeline across classification, information extraction, QA, and tool-calling tasks, showing that compact SLMs consistently match or exceed teacher LLMs.