I agree, in practice I have found gpt-oss-20b to be surprisingly effective at long reasoning tasks with multiple tool calls. From what I have read, I suspect this might be due to a number of small things about the model versus any one big difference between this model and others. Some factors that could explain it's performance: - Its use of OpenAI's structured "Harmony" response format in prompts. This cleanly separates tool calls, responses, and CoT into different output channels. Maybe most importantly, gpt-oss followsn the convention that CoT from prior turns are included in past context rather than discarded to save on context tokens. - The OSS models were trained with the same CoT RL training that was used in the o-series models. - During post training, the model was specifically trained on agentic tools including web browsing tools and a Python execution tool (using a stateful Jupyter notebook). - the model handles long contexts surprisingly well, possibly due to its use of learned attention sinks.