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Subquadratic launches AI architecture with 12-million-token context window, outperforming GPT-5.5 on retrieval benchmarks

By

Frederic Lardinois

23d ago· 8 min readenNews

Summary

Subquadratic has launched a new AI architecture featuring a 12-million-token context window, shattering the current million-token standard set by frontier models like GPT-5.5 and Claude Opus 4.7. While most models struggle to effectively utilize their advertised context windows (with GPT-5.5 scoring only 74.0% on the MRCR v2 retrieval benchmark), Subquadratic's architecture outperforms GPT-5.5 on retrieval benchmarks. The article explains that the million-token limit in current models stems from the computational constraints of attention mechanisms in transformer-based architectures, which Subquadratic's approach aims to overcome.

Key quotes

· 3 pulled
Every frontier model in 2026 advertises a context window of at least a million tokens, but almost none of them are actually great at making use of all of that information.
On MRCR v2, the multi-reference retrieval benchmark labs report, the best model is GPT-5.5, which scores 74.0%. Others like Claude Opus 4.7 at 32.2% are far behind.
One major reason for the million-token max is the same one that has shaped every transformer-based model since 2017: Attention co
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Subquadratic has launched a new AI architecture featuring a 12-million-token context window that outperforms GPT-5.5 on retrieval benchmarks.

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