SubQ 1.1 Small

Subquadratic released a model today, SubQ 1.1 Small, claiming near-frontier performance with a context window of a staggering 12M tokens. The API and plans are still in closed early-access, so don’t get your hopes up for personal long context tasks. SubQ 1.1 Small appears around the Sonnet 4.6 and GPT-5.4-mini level of performance but is exceedingly good at needle in the haystack problems.

I understand that Subquadratic can’t release the math behind their Subquadratic Sparse Attention (SSA), but I would love to see the math behind this model. If open source models like DeepSeek or Qwen got their hands on this kind of technology, we could see a revolution in local LLMs, running ultra-long context LLMs for personal use.

In the end, I would like to see how it fares against RAG, RLMs, and other long-context solutions for both needle in the haystack problems and reasoning over twelve million tokens worth of context.