I'm curious if you could comment on why your DeepSWE performance is below Haiku. This is not a dig - getting 46% vs. Haiku's 52% is still amazing. I'm just wondering if perhaps your post-training was less aggressively RL'ed on coding tasks than perhaps Haiku was. Having a cheap 1,000 tok/s option for coding work is a dream because - if it's reliable enough - you could blast through work such as high-level security reviews or finicky and repetitive refactorings that don't necessarily require SOTA performance.

We might not be able to precisely explain the 6% difference on that specific benchmark, but overall we have found the model to be Haiku- or Luna-level in our own daily usage. That means that it won't yet replace Opus or Fable as the primary driver of a harness, but we have used it successfully for subagents, including implementing kernel optimization ideas produced by a larger model (and those improvements have gone into production -- a form of RSI).

In general, the model continues to improve when given additional high quality data like a normal LLM, which is what makes us very bullish on the scaling of dLLMs.