Tag archive
RAG is 1,250x cheaper per query – but Long Context delivers better results for global document understanding. The 2026 data shows: hybrid architectures with intelligent routing are replacing the ideol
Through one aggregator, nine tiered models billed up to 6.7x past lines their pages never mention, and one endpoint billed 1.25x its listing. How to stay under.
MiMo API: Xiaomi's Long-Context AI Models Xiaomi's current long-context model is...
A framework that chunks long documents, compresses each chunk into memory blocks and gates which blocks the model reads extrapolated from 7,000 tokens of training context to 1.75 million at inference, with half the peak GPU memory of a leading baseli
A team spanning several Chinese universities released the first prototype of what it calls a memory foundation model - a backbone carrying a memory state that updates on every interaction through a plain forward pass, with no gradients and no externa
A 2026 decision framework for choosing RAG vs long context vs hybrid: real 1M-token pricing math, recall trade-offs, and a when-to-use-which table.
The cheapest chatbot API for long-context support chat is the one that meets a measured...
A new systems paper called LongStraw shows reinforcement-learning post-training can execute on prompts beyond 2 million tokens on a fixed 8-GPU budget by scoring the shared prompt once without gradients and backpropagating only through the short gene
2M-token context vs RAG in 2026: cost, latency and when each actually wins Summary. Google...
Tencent's Hunyuan team introduced HiLS, a sparse-attention method that learns end-to-end which parts of a long document to focus on, matching full attention while handling context 64 times longer than it was trained on.
Two new techniques treat a language model's long-context memory like an operating system's memory hierarchy - keeping coarse summaries on the GPU and paging compressed detail out to the CPU - with one, SeKV, cutting GPU memory use by 53% at 128,000 t
MiniMax M3 needs 32GB+ at Q4 + massive KV cache for 1M context. RTX 5090 32GB is the consumer floor. 5 GPUs ranked for 1M-context RAG in 2026.