Beast Models

Beast Nano is the fastest model in the Beast lineup: a streamlined multi-agent reasoning system tuned for cost-sensitive, high-volume workloads. It runs a lighter deliberation profile at inference time, preserving the core quality advantage of multi-agent reasoning while reducing latency relative to Beast Mini and Beast Max.

Because Beast Nano still deliberates before answering, it is not a real-time model in the absolute sense. Deliberation inherently takes longer than a single forward pass through a conventional LLM. If sub-second response time is a hard requirement, Beast models are not the right fit.

Beast Nano supports multimodal inputs (text and images) with unified text-based output and a 254K-token context window. The reasoning_effort parameter lets you trade depth for speed within the multi-agent pipeline. Ideal for everyday agentic workflows, batch jobs, classification, routing, and any task where you want disciplined reasoning at the lowest cost in the Beast lineup.

Beast Max is BEAST’s flagship model: a multi-agent reasoning system built for depth, accuracy, and complex problem solving. Multiple internal agents deliberate at inference time, converging on a high-quality response before delivery.

Beast Max supports multimodal inputs (text, images, and PDF documents) with unified text-based output and a 892K-token context window for sustained multi-turn reasoning. The reasoning_effort parameter controls the depth of multi-agent deliberation, from rapid convergence to full inference-time depth.

Because Beast Max deliberates before responding, it trades latency for dramatically reduced hallucinations and measurably higher output quality. Ideal for research, complex coding, and agentic workflows where accuracy matters more than speed.