Welcome Beam: a 501B open model you can own, customize, and scale

Beam by Reflection is out on Hugging Face! It is the first model from the new US lab, Reflection, and it’s open! It's a mixture-of-experts (MoEs) model with 501B parameters (23B active per token), supports text as input, has a context window of 1M tokens, and is released under the Apache 2.0 license.

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.

TL;DR

• 501B-parameter MoEs LLM with 23B active per token. 52 layers, 256 experts per MoE layer, 8 routed per token.
• Text in, text out with context window of 1M tokens.
• Checkpoint formats: BF16, FP8, and NVFP4
• Reasoning that can stay in context across turns (preserve_thinking), tool calling with a dedicated grammar. The model supports five reasoning values: "low", "medium" (default), "high", "xhigh", and "max".
• Response template in the tokenizer.
• Speculative decoding drafter model with DFlash2
• Day-0 libraries: transformers, SGLang, vLLM. Full-parameter RL with Miles.
• Ships with a Rust-based custom harness.
• Apache 2.0.

Build with Beam

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:

What is Beam?

Attention

Inside the attention block (both layer types)

Mixture of Experts

If you want to deploy Beam yourself, check out the vLLM or SGLang recipes below.

Complete tool-calling example