Open Weight

DeepSeek releases open-weight V4.1-Flash-1017, with search APIs at $0.75 per 1,000 calls: Deepseek released V4.1-Flash-1017, an open-weight model it says outperforms its predecessor on benchmarks, runs faster and returns shorter outputs. It also opened its search engine via Search and Fetch APIs at $0.75 per 1,000 calls; users in Claude Code and Codex get free access for now.

AI Models • The Decoder

Reflection AI releases Apache 2.0-licensed weights for Beam, a 501B-parameter model: Reflection AI released Apache 2.0-licensed weights for Beam, a 501B-parameter model. The model was trained with reinforcement learning that penalized unnecessary tokens. The company is pitching Beam against Chinese open models used by many U.S. companies.

Open Source AI • Techmeme

Black Forest Labs releases FLUX 3 Image Klein, a 5.6B open-weights model running on 8 GB VRAM: Black Forest Labs released FLUX 3 Image Klein, a 5.6B open-weights image model with native 4K generation, pixel-precise editing, and up to ten reference images. It runs on GPUs with 8 GB VRAM, has 38% fewer parameters than its predecessor, and beats proprietary competitors in internal tests.

AI Models • The Decoder

Ant Group open-sources Ling-3.1-flash, a 560B-parameter AI model: Ant Group released the weights of Ling-3.1-flash, allowing developers to deploy the AI model on their own infrastructure. The model has about 560 billion total parameters, activates about 25 billion per token, and supports a context window of up to one million tokens.

AI Models • TechNode

Meta's Muse Spark Open is an 816B-parameter open-weight multimodal model: Meta’s Muse Spark Open is a new open-weight model with 816 billion parameters that processes text, images, video, and audio with up to one million tokens of context. It performs roughly on par with the most capable open-weight Chinese AI models on benchmarks and is optimized to run locally on Apple's M5 Ultra.

Meta • The Decoder

StepFun releases open weights for Step 5 Preview, a 600B model with 27B active parameters: StepFun released open weights for its Step 5 Preview model, with 600B total and 27B active parameters and a 1M context window. It published a technical blog describing the model's 92-layer design, sparse grouped-query attention, and long-horizon agent training.

StepFun • Pandaily

Study finds LLM reasoning uses 3.7× more inference energy on average: A study measured inference energy for 86 open-weight LLM checkpoints across seven benchmarks and six hardware platforms. Most configurations used a median 2.6–4.8× more energy than another configuration scoring at least as well, with gaps up to 27×; reasoning raised energy about 3.7× on average.

AI Research • arXiv