Subquadratic releases SubQ Snap 1.1 decision model with 384K context

Subquadratic presents SubQ Snap 1.1, a multimodal decision model with a 384K-token context window aimed at fast, long-context reasoning and agentic decisions in enterprise workflows. The page claims it is tied for first on the Artificial Analysis Decision Index 1.1, and it is priced at $0.04 per 1M tokens. The company attributes efficiency to a proprietary Subquadratic Sparse Attention (SSA) method that reduces compute relative to dense attention. Benchmark tables are shown alongside a comparison against Jev 1.14, with mixed results.

Key takeaways

• SubQ Snap 1.1 is described as a multimodal decision model with a 384K-token context window.
• The page reports a tie for #1 on the Artificial Analysis Decision Index 1.1 with a score of 65.
• Pricing is listed at $0.04 per 1M tokens, with 100M free tokens for new users.
• The page's own comparison shows Snap 1.1 trailing Jev 1.14 on PhishNChips (45.5 vs 67.5) and API-Bank.

Why it matters

Long-context decision models targeting cheap, fast routing and classification steps in agent pipelines could change how teams split work between small and frontier models. Claims of efficiency gains from sparse attention are relevant to cost-sensitive deployments.

Read with care

Source claims not independently checked; benchmark results and efficiency claims come from the vendor's own page.