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SubQ is presented as the first model built on a fully sub-quadratic…
Brief
SubQ is introduced as a breakthrough LLM built on a fully sub-quadratic sparse-attention architecture (SSA) that supports a 12 million-token context window. Alexander Whedon claims it achieves 52× speed vs. FlashAttention at 1M tokens, costs under 5% of Opus (caption also said 10× cheaper than Opus 4.7), and requires nearly 1,000× less compute.
Why it matters
SubQ is presented as the first model built on a fully sub-quadratic sparse-attention architecture (SSA) and the first frontier model with a 12,000,000-token context window (announcement by Alexander Whedon, posted 2026-05-05).
Key details
- SubQ is claimed to be 52× faster than FlashAttention at 1,000,000 tokens and to cost less than 5% of Opus (the post’s caption also stated it was '10x cheaper than Opus 4.7').
- The post asserts SSA 'finds and focuses only' on important token relationships, yielding 'nearly 1,000× less compute' and enabling a new scaling approach for transformer-based LLMs.