SubQ

Founded by Alexander Whedon

First fully sub-quadratic LLM. 12M token context, 92% accuracy, 50× cheaper than leading frontier models.

Cost

Demo

Rating

★ People love it

Time to value

Quick Setup (< 1 hour)

You can use SubQ to process extremely long contexts up to 12 million tokens in a single prompt. It uses sparse attention architecture that reduces compute by 1000x compared to traditional models. You can analyze entire codebases, months of pull requests, or long conversation histories without quality loss. The model provides 150 tokens per second processing speed at one-fifth the cost of other leading models. It excels at software engineering tasks and long-context retrieval with high accuracy.

What SubQ does

Analyze complete codebases in single promptsRetrieve information from 12 million token contextsProcess long conversation histories without truncationExecute software engineering benchmarksGenerate responses from massive document collectionsHandle multi-needle context resolution tasksStream responses while processing large inputsIntegrate with existing coding workflowsProcess 12 million tokens in one promptSparse attention reduces compute by 1000x150 tokens per second processing speedOne-fifth the cost of leading modelsOpenAI-compatible API endpointsReal-time streaming with tool useThird-party validated benchmarksLinear cost scaling for large contexts