Model releases

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.

AICognition introduces SWE-grep and SWE-grep-mini, fast agentic models trained with reinforcement learning for multi-turn context retrieval in coding tasks. The company says they match frontier coding models at retrieval while taking an order of magnitude less time, and they power the Fast Context subagent in Windsurf. The models issue up to 8 parallel tool calls per turn within 4 turns, and Cerebras serves SWE-grep-mini at over 2,800 tokens per second and SWE-grep at over 650 tokens per second.

Why it matters: The post explains the speed-intelligence tradeoff in agentic code search, showing how parallel tool calls and RL training change the cost of retrieving context for coding agents.