
Ant Group's Ling-3.1-flash Squeezes a 560B Model Into Four GPUs
Ant Group's InclusionAI opens weights for a 560B hybrid-linear MoE model with 25B active parameters, 1M context, and MIT licensing.
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Ant Group's InclusionAI opens weights for a 560B hybrid-linear MoE model with 25B active parameters, 1M context, and MIT licensing.

Reflection AI opens the weights of Beam, a 501B Mixture-of-Experts model, alongside a 40+ partner ecosystem and a full agent toolchain.

Tencent Hunyuan open-sources a 30B memory writer model, a dual-system memory SDK, and MemHub to unify memory across AI tools.

A compiler converts if-else and loops into frozen matrix weights, producing exact, differentiable neural nets that run JavaScript programs in a browser.

Meta's new 816B parameter mixture-of-experts model targets long-horizon coding and agentic work, with open weights that fit on two Mac Studios.

Liquid AI's new multimodal decision model tops the Artificial Analysis Decision Index, returns calibrated probabilities with zero generated tokens, and costs $0.042 per million input tokens.

QuantTrio shipped a 4-bit AWQ quantization of Qwen3.6-35B-A3B that compresses the MoE down to roughly 24GiB for vLLM deployment.

NVIDIA's new open 120B mixture-of-experts model fuses text, image, document, and video reasoning into one agent-ready backbone with 12B active parameters.

OpenBMB's MiniCPM-V 4.7 packs 35B parameters into a mixture-of-experts vision model that runs on an iPhone at 13+ tokens per second.

A 315M-parameter Japanese embedding model built on ModernBERT-Ja tops JMTEB benchmarks while handling 8K context, outperforming OpenAI and billion-parameter rivals.

IFM's new Arabic-first model family spans 0.9B to 375B parameters, with a flagship MoE that matches Claude Sonnet 5 on Arabic benchmarks and acts as an agent.

A training-free sparse attention method uses a handful of representative attention heads as proxies, delivering 10.3x attention speedup on long contexts.

TypeSafe AI ships Jev 1.14 with image inputs, a bounding box primitive, and 4x longer context, all behind opt-in danger zone headers.

Google DeepMind's new frontier model ships with a 1M token output ceiling, agentic coding wins, and verified lab discoveries in fusion, quantum, and biology.

Google released quantization-aware trained Q4_0 GGUF checkpoints of Gemma 4 E4B, delivering near-bfloat16 quality in a 5.15 GB on-device package.

Japan's National Institute of Informatics releases an 8B dense and 32B MoE model trained from scratch on 12T tokens, with the full pipeline open to inspection.

Google's frontier Gemini 4 Argon model lands in Cursor at $2/$10 per million tokens, matching Claude Opus 5.5 on CursorBench coding evaluations.

Google's newest frontier model lands in Copilot with gains across every coding benchmark and especially strong results in the terminal agent.

Liquid AI's new 24B MoE activates just 2B parameters per token, hits 184 tok/s on a MacBook, and targets fully local agents.

A new leaderboard pits purpose-built decision models against LLMs on classification, tool calling, knowledge, and reasoning, with cost and latency side by side.

Cognition added Google's new frontier model to Devin Desktop and CLI, where it tops Gemini scores on FrontierCode at 60.5% with a 75% launch discount.

Google's new frontier model tops the Vals Index at 68.9%, ships with a 1M token window, and launches at $2/$10 per million tokens.

AutoTrust AI's open-weights JEV-35B turns a Qwen3.5 MoE into a System 1 decision model that picks among 256 options in 204 ms.

Ai2's TutorMoments benchmark replays real math sessions to test whether LLMs scaffold learning or spoil it by over-helping students.
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