Topic
Subquadratic
3 episodes
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“Take work away from frontier LLMs”: Subquadratic’s decision model claims 12x Jev’s context window
Subquadratic launches SubQ Snap, a decision model with 384K-token context window — 12x Jev's 32K limit — claiming it can process 100K tokens and return a decision in ~1 second. Built on Subquadratic Sparse Attention (SSA) for linear attention scaling, Snap targets enterprise agent workflows: file selection, tool routing, test verification. Benchmarks show Snap beating Jev on 13 of 16 enterprise tests (Decision LCR 74 vs 48, Nimble 82.9% vs 76%), though Jev wins on When2Call. All results are self-reported; independent verification pending. Pricing at $0.05/M input tokens, free outputs. The real question: does a 384K window actually eliminate retrieval/chunking fragility for coding agents, or just push the bottleneck elsewhere?
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A Startup Claims It Broke Through a Bottleneck That's Holding Back LLMs
Subquadratic claims its SubQ model breaks the dense attention bottleneck in LLMs, offering 12x context, near-SOTA performance, and radical efficiency. Third-party Appen benchmarks validate speed/cost claims, but full reproducibility and open access remain pending. Cody questions the 'transformer-replacement' hype; Justy sees a niche for high-throughput document and code analysis.
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The context window has been shattered: Subquadratic debuts a 12 Million Token window
Cody is skeptical that a 12-million-token context window is broadly useful today, while Justy pushes the angle that it solves a very real pain point for teams with giant codebases, logs, and long-running workflows. They land on it as a real technical milestone with a narrow early market, plus a lot of unanswered questions about cost, latency, and whether most users need this kind of scale.