Gemini 4 Argon sets a new record on WANDR. It is now available in Perplexity for Pro and Max subscribers. https://t.co/n1TyI9RhBX
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Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active. - Frontier reasoning efficiency - Advances the Western open frontier on coding & agentic tasks - Trained end-to-end from scratch Full weights release this month. Learn more about Beam: https://t.co/c3Qx2cpM8G
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On October 16, Reflection released its first open-weight model Beam on Hugging Face under the Apache 2.0 license, and Baseten provided day-0 support the same day through Model APIs and Dedicated Inference. Beam is a 501B total-parameter MoE autoregressive Transformer with 23B activated per token, a context window of up to 1 million tokens, pretraining on 23.8 trillion tokens, and weights released in BF16, FP8, and NVFP4 precisions. The evaluation results given by Reflection are GPQA Diamond 90.5%, SWE-bench Verified 80.9%, AIME 2026 97.8%, Terminal-Bench 2.1 80.1%, and MCP Atlas 78.7%, and it also open-sourced the terminal agent tool Mirror CLI.
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Meta has released the open-weight model Muse Spark Open, which uses a MoE architecture with 256 experts, activates about 42B parameters per token, has a context window of 1 million tokens, and supports text, image, video, and audio input but outputs text only. Meta has partnered with Apple to run the approximately 530 GByte four-bit quantized version on two interconnected Mac Studio M5 Ultra machines, distributing computation via the MLX framework and Thunderbolt 5, achieving about 25 output tokens/second at an 8192-token context. Artificial Analysis tests show that it scores 39 on the Intelligence Index, making it the strongest American open-weight model currently available, but it trails MiMo-V2.6-Pro's 46 points and GLM-5.3's 45 points, and ranks last in four Agent tests. The model weights are available on Hugging Face in two versions: 16-bit at approximately 1.6 TByte and four-bit at approximately 530 GByte, with API pricing at $1.25 per million input tokens and $4.25 per million output tokens.
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NVIDIA has added three models to the Nemotron open-source model family, covering enterprise workflows, local AI agents, and conversational applications across data centers, desktops, and edge devices. These include the 120-billion-parameter Nemotron 3.5 Super VL, which NVIDIA claims delivers up to 5x the throughput of the next-best comparable model, with accuracy approaching that of its largest reasoning model at roughly one-fifth the parameter count; there are also two small models in the Nemotron 3.5 Bolt series: 74B-A9B 24GB for everyday agent tasks on RTX and DGX Spark, and E4B 4GB for conversational and physical AI agents. Super VL and Bolt 74B-A9B are already available, and Bolt E4B is scheduled for release on October 23.
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Meta released the open-weight model Muse Spark Open on Thursday, saying its capabilities can match and in some scenarios surpass comparable products from Chinese vendors. Meta AI head Alexandr Wang called it the most capable U.S. open-weight model to date; the model targets coding and agentic tasks, excels at long-horizon tasks, complex software engineering and multi-step reasoning, and has built-in safety safeguards. The model is optimized for the Apple M5 Ultra chip, and users can run it on the desktop with two Mac Studios equipped with that chip.
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Hugging Face has released Beam, a 501B-parameter sparse MoE open source model aimed at long-text reasoning and agent tasks with 1M context, activating only about 23B parameters per token. The model uses 52 layers of hybrid attention, with 43 layers using a 512-token sliding window and 9 layers using global attention; the KV cache grows by 36 KiB per token, about one-sixth that of a full-attention approach. The official release provides three checkpoints—BF16, FP8, and NVFP4; NVFP4 can compress the weights from about 1 TB to about 280 GB, and day-0 support is already available in transformers, vLLM, and SGLang.
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Reflection's first open-weight model, Beam, is officially released, offered free for a limited time with Ollama as a day-zero partner and with zero data retention (ZDR). Beam is an MoE model with approximately 501 billion total parameters, about 23 billion activated per token, and a 1 million token context window. It targets software engineering, reasoning, and long-horizon agentic tasks, and is released under the Apache 2.0 license. Users can run beam:cloud via Ollama Cloud and connect it to tools such as Claude Code, Codex, and OpenCode.
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Reflection's first open-weight model, Beam, is now available on the Baseten platform, with day-0 support through Baseten Model APIs and Dedicated Inference. Beam uses a MoE architecture with 501B total parameters and 23B activated per token, a context window of up to 1 million tokens, and is aimed at coding and agent workflows. During training, Reflection used longer RL rollouts and added a length penalty to encourage the model to complete tasks with fewer tokens; users can also adjust reasoning effort via a parameter to control how much reasoning the model invests.
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Reflection has released its first model, Beam, an open-weight model with 501B total parameters and 23B activated parameters. It scores 30 on the Artificial Analysis Intelligence Index, ranking second among US open-weight models, behind only Meta's Muse Spark Open (39) and ahead of NVIDIA's Nemotron 3.5 Super VL (26). Beam consumes about 26k output tokens per Intelligence Index task, of which about 19k are used for reasoning and 7k for answering; it scores 48.5% on AutomationBench-AA, but only 3.0% on Terminal-Bench 4.0. On AA-Omniscience, it answers 33% of questions correctly, with a hallucination rate of 47% and a net index score of 1.5.
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what's your early feedback on Gemini 4 Argon? where should we improve? we take your input seriously.
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