The Efficiency Turn: Why This Week's Most Interesting AI Models Were About Speed, Not Size
Diffusion text models, sparse 500-billion-parameter open weights and a 4GB on-device agent all point the same way: AI's next frontier is how much intelligence you get per token, per second and per gigabyte. TL;DR Inception's Mercury 3 (19 October) is a diffusion language model that claims more than 1,000 tokens per second and quality on par with or better than GPT-6 Luna and Claude Haiku 5.5, at launch pricing of $0.10/$0.35 per million input/output tokens [1][2]. The benchmarks are self-reported; no independent tests had been published at the time of writing [2]. Reflection's Beam 1.0 (16 October) is an Apache 2.0 open-weight sparse mixture-of-experts (MoE) model: 501B total parameters, about 23B active per token, 1M-token context [4][5][6]. NVIDIA's Nemotron 3.5 Bolt E4B (23 October) packs a multi-turn conversational agent into roughly 4GB, aimed at RTX PCs, Jetson boards and robots [7]. Cordoval's view: these releases come from very different labs, bu...