Beam is focused on coding and agentic use cases. It is for teams that want a frontier-scale model on their own hardware, infrastructure, or cloud, and want to customize it to their own use cases. Beam ships with its own dedicated open source Rust based harness that delivers the best performance from the model.

Beam has a native transformers implementation, as well as downstream libraries like vLLM and SGLang. We’ve worked with the Reflection team to bring you inference, SFT (supervised fine tuning), and RL (reinforcement learning) recipes for you to build with this model for your needs. On top of day-0 support in transformers, SGLang and vLLM, this post shows you how to run it, how to post-train it, and what we built with it.

Reflection is going to give out $10,000 Hub credits to the best projects that use Beam. All you have to do is make a thing, and share it on the Hub:

If you build something with Beam, then share it on the Hub for a chance to win $10,000. All you have to do is build the space, model, or dataset repo and share it on the model’s repo page. We’ll collect all the links, select the best, and send out the credits to your Hub accounts.

Beam is a text-only model built for long-context (1M context length) reasoning and agentic work. It is a sparse MoE model, where each token is computed by a small fraction of the network, so it runs at the speed of a much smaller model while keeping the capacity of a large one.

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