SwiReasoning claims fewer tokens and better answers — but why so obscure?
SwiReasoning is a technique that lets an AI model switch between 'explicit reasoning' (thinking out loud in text) and '' (thinking internally without writing it out) depending on the situation. Doing this can sharply cut the number of tokens (units of text the model processes) needed to solve a problem, making responses feel faster overall.
One person tested it on the model and found the answers were more accurate and solved problems much faster. The raw tokens-per-second speed was actually a bit slower, but because far fewer tokens were needed in total, the overall experience felt quicker.
The technique itself is about nine months old, with a (sdc17/SwiReasoning) and a llama.cpp already available. Despite this, it hasn't caught on widely, which prompted the question of what the catch might be.
Key points
- SwiReasoning switches between explicit and during inference
- Reported accuracy and perceived speed gains when applied to
- Tokens-per-second speed is slightly lower, but total tokens needed drop significantly
- The technique is about 9 months old, with GitHub and llama.cpp s available
- It remains unclear why the method hasn't become more widely adopted