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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsReflection AI announced Beam on October 5, 2026, as its first open-weight model for coding, reasoning, and agentic workloads. The sparse mixture-of-experts model has 501 billion total parameters and 23 billion active parameters. At announcement, final evaluations were still underway: Reflection offered a waitlist and said it planned to publish weights and developer materials later in October, under an Apache 2.0 license. Those were plans, not confirmation that the files had been released.
What is Reflection AI’s Beam model?
Beam is a large language model built using a sparse mixture-of-experts (MoE) architecture. Reflection says it has 501 billion total parameters, of which 23 billion are active for a given input. The company describes it as an efficiency-focused model for coding, reasoning, and agentic tasks—workflows in which a model can use tools or take steps toward completing a task.
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Reflection calls Beam “open-weight,” a term that refers to releasing model weights for others to run or adapt. The company’s announcement said it planned an Apache 2.0 weights release, along with a technical report, model card, and developer tools for running, evaluating, and fine-tuning the model. The announcement does not establish that training data, every training component, or the full development process will be open. Check the actual release artifacts and license text before relying on the announced terms.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Reflection’s stated approach to “open intelligence” includes model weights, published research, and open-source software, but that is the company’s framing of its own work. Reflection AI’s about page also describes a broader focus on enterprise, government, and sovereign AI deployments; it does not confirm Beam is already available through those channels.
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- Dual-Brain Hybrid Power: Combines the Qualcomm Dragonwing QRB2210 MPU (Quad-core Arm Cortex-A53 @ 2.0 GHz CPU, Adreno GPU, AI acceleration) and the real-time, low-power STM32U585 MCU for advanced applications like object recognition, voice commands, and motion detection.
- AI & Linux Capabilities: Unlocks AI-powered vision and sound solutions; runs Linux Debian OS for coding in Python and supports the Arduino ecosystem with libraries and Sketches; quick start with Arduino App Lab.
- Advanced Features: Equipped with 4 GB LPDDR4 RAM, 32 GB eMMC built-in storage, ideal for single-board computer (SBC) mode, running multiple simultaneous high-level processes, more complex AI or ML models, extensive logs. Dual-band Wi-Fi 5 (2.4/5 GHz), Bluetooth 5.1, and high-speed headers for vision, audio, and display peripherals.
- Seamless Expansion & Connectivity: Features the classic UNO form factor for shields compatibility, an 8x13 LED matrix, and a Qwiic connector for easy expansion with Modulino nodes; power and connect via the USB-C connector.
- Intended Use & Development: The perfect platform for prototyping robotics or IoT projects, empowering innovators with a unified development experience to mix Arduino Sketches, Python scripts, and containerized AI models in a single interface.
When will Beam be released?
On October 5, 2026, Reflection said Beam was undergoing final red-teaming and evaluations, offered a waitlist for early access, and targeted a release later in October. It said the release would include weights, documentation, and developer artifacts. TechCrunch and The Information also covered the announcement and its then-current status. A target date is not evidence of delivery, so availability should be checked against Reflection’s current release materials.
Reflection said it planned distribution partnerships and integration with open-source libraries and harnesses. TechCrunch reported that hyperscalers and neoclouds were among the intended distribution routes. The announcement did not name a confirmed Beam hosting provider, and it gave no end-user hardware requirements. The 10.5K NVIDIA GB300 GPUs Reflection says it used for training are not a deployment recommendation.
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- Dual-Brain Hybrid Power: Combines the Qualcomm Dragonwing QRB2210 MPU (Quad-core Arm Cortex-A53 @ 2.0 GHz CPU, Adreno GPU, AI acceleration) and the real-time, low-power STM32U585 MCU for advanced applications like object recognition, voice commands, and motion detection.
- AI & Linux Capabilities: Unlocks AI-powered vision and sound solutions; runs Linux Debian OS for coding in Python and supports the Arduino ecosystem with libraries and Sketches; quick start with Arduino App Lab.
- Advanced Features: Equipped with 2 GB LPDDR4 RAM, 16 GB eMMC built-in storage, ideal to develop in PC-connected mode, running the OS, Python scripts, and basic network services (SSH) without a demanding GUI or heavy multitasking; great for lightweight AI and memory-optimized TinyML applications, needing local storage for basic OS and core libraries. Dual-band Wi-Fi 5 (2.4/5 GHz), Bluetooth 5.1, and high-speed headers for vision, audio, and display peripherals.
- Seamless Expansion & Connectivity: Features the classic UNO form factor for shields compatibility, an 8x13 LED matrix, and a Qwiic connector for easy expansion with Modulino nodes; power and connect via the USB-C connector.
- Intended Use & Development: The perfect platform for prototyping robotics or IoT projects, empowering innovators with a unified development experience to mix Arduino Sketches, Python scripts, and containerized AI models in a single interface.
What training details has Reflection disclosed?
Reflection says it pretrained Beam on 23.8 trillion tokens from curated web and licensed datasets. It describes a data pipeline focused on source code, technical explanations, mathematics, and scientific knowledge, using quality classifiers and fine-grained quality tiers to select material for agentic coding.
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The company also says its pipeline removed about 95% of raw Internet tokens through parsing, deduplication, and curation. It claims conventional techniques would have missed roughly 1.8 trillion high-quality tokens it retained, including 87% of its curated web-code tokens. These are Reflection’s descriptions of its pipeline, not independently audited measurements.
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- Single core ARM Cortex-A7 32-bit core, integrated with NEON and FPU
- Built in Micro's self-developed 4th generation NPU, with high computational accuracy and support for mixed quantization of int4, int8, and int16. Among them, int8 has a computing power of 0.5 TOPS and int4 has a computing power of up to 1.0 TOPS
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- It has powerful encoding performance, supports intelligent encoding, adapts to save bit rates according to the scene, and saves more than 50% of the bit rate compared to conventional CBR mode, making the captured images high-definition, smaller in size, and doubling the storage space
- The design with built-in RISC-V MCU supports low-power fast startup, 250ms fast capture, and simultaneous loading of AI model library, enabling facial recognition to be completed within 1 second
For reinforcement learning, Reflection reports more than 100 million rollouts using 10.5K NVIDIA GB300 GPUs over four weeks. These figures, like the training-data figures, come from the company’s October 2026 announcement and have not been independently audited in the sources reviewed.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What do the Beam benchmark results show?
Reflection’s announcement table reports a score of 44.4 on DeepSWE v1.1 and 77.2 on SWE Bench Pro v2-Hard for Beam. Those are results on two named agentic coding and terminal benchmarks; they do not establish a universal ranking across coding or AI models. The table includes comparison models and uses “NR” for scores that have not been reported. Reflection’s figures are vendor-presented evaluations, not independent validation.
Rank #4
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Reflection characterizes Beam as competitive with larger open models such as GLM 5.2 and as approaching Qwen 3.8-Max on coding and agentic tasks. It says Kimi K3 remains ahead on raw capability and presents inference efficiency as Beam’s advantage. The Information separately reported Reflection’s claim that Beam outperformed Inkling and Nemotron 3 Ultra on certain coding and reasoning tests while lagging leading Chinese models. These comparisons are claims about particular evaluations, not broad, independently established rankings.
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What is established—and what remains unknown?
- Established at announcement: Reflection introduced Beam on October 5, 2026, as its first open-weight model, with stated dimensions of 501 billion total parameters and 23 billion active parameters.
- Announced, not yet confirmed as delivered: an Apache 2.0 weights release, technical report, model card, running and evaluation tools, fine-tuning support, and later-October availability.
- Not specified in the announcement: end-user hardware requirements and a confirmed hosting provider.
- Company-reported rather than independently audited: training scale, data-curation figures, and benchmark results.
Sources: Reflection AI’s Beam announcement (October 5, 2026); TechCrunch’s launch coverage (October 5, 2026); The Information’s announcement coverage (October 5, 2026); and Reflection AI’s about page.
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