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AIMoonshot AI has released Kimi-Linear-Base and Kimi-Linear-Instruct, both 48B total and 3B activated parameters with a 1M context length, on Hugging Face. The models use Kimi Delta Attention in a 3:1 hybrid ratio with global MLA, cutting KV cache by up to 75% and boosting decoding throughput by up to 6x at 1M tokens. The KDA kernel is open-sourced in FLA, and the checkpoints were trained on 5.7T tokens.

Why it matters: The model card gives concrete throughput and KV cache figures for a hybrid attention design, which helps readers weigh its long-context tradeoffs against full attention.

AICognition has released SWE-1.5, a model optimized for software engineering that it says reaches near-frontier coding performance while running at up to 950 tok/s with Cerebras inference. The company reports it is 6x faster than Haiku 4.5 and 13x faster than Sonnet 4.5, and it is available now in Windsurf. The post's SWE-Bench Pro chart places SWE-1.5 at 40.08%, behind Sonnet 4.5 at 43.60%, and it notes that the model was trained with reinforcement learning on the Cascade agent harness.

Why it matters: The post pairs a benchmark chart with a 950 tok/s speed claim and describes how harness, RL environments, and inference were co-designed, useful context for judging the speed-versus-quality tradeoff.