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New models and updates: flagship releases, open weights, performance changes, and pricing changes.
347 top picks all-time · 169 in the past 30 days · chosen from 1,689 items collected all-time
Top picks 341–347 of 347
Quoc Le@quocleixXAI score62AIQuoc Le, who posts for Google and Gemini, says Gemini 3 Deep Think is now powered by an even stronger version of the engine behind Google's gold medal-level results at IMO and ICPC. He calls it state of the art above state of the art and says more will follow soon.
Why it matters: The post links Deep Think to earlier IMO and ICPC gold-level results and shows benchmark charts, helping readers compare it with Gemini 3 Pro and rival models.
AIMoonshot AI released Kimi K2 Thinking, an open-source thinking model that interleaves step-by-step reasoning with tool calls across 200 to 300 sequential invocations.
Why it matters: The model card gives benchmark tables, quantization details, and deployment settings, letting readers compare Kimi K2 Thinking against GPT-5 and other models on specific tasks.
AIRunway announced Gen-4.5, a video generation model that it says holds the top position on the Artificial Analysis Text-to-Video benchmark with 1,247 Elo points.
Why it matters: The post separates Runway's own ranking claim from the listed limitations, such as causal reasoning and object permanence errors, which helps judge where the model is reliable.
AIMoonshot AI released Kimi Linear, a hybrid linear attention architecture with 48B total and 3B activated parameters and a 1M-token context length, on Hugging Face.
Why it matters: The model card gives concrete long-context speed and memory figures for a hybrid attention design, useful for judging whether linear attention can replace full attention in practice.
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.
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.
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.
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.
