Fireworks Targets Inference Bloat With Lean Ember-1 Reasoning Model
Fireworks Research released Ember-1 on September 24, 2026, directly tackling the token bloat weighing down modern AI reasoning. Built on Kimi K3, the specialized architecture cuts reasoning trace lengths by roughly 40% to slash unnecessary compute overhead. Despite the sleeker output traces, it supports a massive context length of 1,048,576 tokens for heavy-duty enterprise workloads. With pricing set at $3.00 per 1M input tokens and $15.00 per 1M output tokens, this setup could dramatically undercut bloated frontier alternatives. Can concise thinking finally dethrone brute-force verbosity in the race for reliable reasoning?
Perceptron Mk1.5 Pushes Multimodal AI Directly Into Physical Space
On September 25, 2026, Perceptron launched Perceptron Mk1.5, an embodied reasoning model built to operate in the physical world. Designed for robotic agents, the architecture ingests text, image, video, and audio across a 36,864-token context length. Rather than just generating text, it outputs structured spatial annotations—including points, boxes, polygons, and tracks—at a rate of $0.15 per 1M input tokens and $1.50 per 1M output tokens. This low-cost spatial intelligence could rapidly accelerate how autonomous machines map, interpret, and navigate dynamic environments. Will giving robots native spatial tracking finally unlock the next leap in physical automation?
