AWS Launches Reflection Beam: Open-Weight AI Model for Coding

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TL;DR: AWS announced deployment support for Reflection Beam, an open-weight sparse mixture-of-experts model with 501 billion total parameters designed for agentic coding and long-horizon tasks. The model can be deployed on AWS through Amazon SageMaker JumpStart real-time endpoints or Amazon SageMaker HyperPod Inference for Kubernetes-native control over distributed GPU infrastructure.

What's New

AWS has announced support for deploying Reflection Beam, an open-weight AI model specialized for agentic coding tasks. Developed by Reflection AI, Beam is designed for maintaining code, using tools, interpreting results, and completing multi-step workflows.

Beam is a sparse mixture-of-experts model with 501 billion total parameters and 23 billion active parameters. Reflection AI reports an effective context length of 1 million tokens. The model is released under the Apache 2.0 license through Hugging Face and is available in three checkpoint variants: BF16, FP8, and NVFP4, each offering different memory footprints and hardware compatibility.

According to AWS, developers can choose between two primary deployment paths. The Amazon SageMaker JumpStart real-time endpoint provides a fully managed solution with an AWS managed model package, endpoint, and invocation API without requiring Kubernetes operations. For teams needing more control, Amazon SageMaker HyperPod Inference offers Kubernetes-native control over distributed GPU infrastructure with options to use JumpStart, SGLang, or vLLM.

Reflection AI's stated mission is to make intelligence open and accessible through open model weights, published research, and open source software. The company's full AI stack spans models, core software, production infrastructure, and applications, with Beam bringing this approach to software engineering and agentic systems.

Pricing and Availability

At launch, Beam is available in Amazon SageMaker JumpStart in US East (N. Virginia) and US East (Ohio) regions.

Pricing details were not provided in the announcement. Users will need GPU capacity and quota for supported Beam configurations. The deployment requires multi-GPU setups due to the model's 501 billion total parameters. For FP8, AWS recommends ml.p5.48xlarge with NVIDIA H100 GPUs or ml.p5e.48xlarge and ml.p5en.48xlarge with H200 GPUs. For NVFP4, ml.p6-b200.48xlarge with NVIDIA B200 GPUs is recommended.

Users can utilize on-demand capacity when available, or use a SageMaker training plan as a short-term reservation for scheduled work.

How to Try It

To get started with Beam via SageMaker JumpStart real-time endpoint, AWS provides a sample repository. Users need an AWS account with appropriate permissions for SageMaker deployment, access to Beam through SageMaker JumpStart or Hugging Face checkpoint repositories, acceptance of model terms, the AWS Command Line Interface, and Python 3.11 or later.

The quickest deployment path involves cloning the sample repository from GitHub (sagemaker-genai-hosting-examples), creating a Python virtual environment, and running the deployment script. AWS provides a bounded deployment command that deploys the endpoint, sends a test request, prints the response, and cleans up the endpoint.

For the HyperPod Inference path, users need kubectl access to an EKS-orchestrated HyperPod cluster with the HyperPod Inference Operator and required add-ons configured.

Who It's For

Reflection Beam is designed for developers and researchers building software engineering and agentic systems. The open-weight release gives users control over the runtime, deployment environment, and customization process.

The model is particularly suited for teams working on code maintenance, tool integration, multi-step workflows, and long-horizon tasks that require sustained reasoning and action. Organizations needing to resolve software issues, modify code, call tools, and act on intermediate results may find Beam valuable.

Teams requiring Kubernetes-native control over distributed GPU infrastructure will benefit from the HyperPod Inference deployment option, while those preferring a managed solution can use the JumpStart real-time endpoint path.

Alternatives

While AWS did not mention specific alternatives in the announcement, the cloud platform ecosystem offers various coding assistance tools. Replit provides an AI-powered development environment with coding assistance capabilities, though with a different architecture and deployment model than Beam.

Other code generation and assistance solutions exist in the market, though the specific combination of open weights, mixture-of-experts architecture, and AWS-native deployment options distinguishes Beam's offering. The Apache 2.0 license allows for customization and control not available with proprietary alternatives.