---
title: "Welcome Beam: a 501B open model you can own, customize, and scale"
description: "Reflection released Beam, a 501B-parameter mixture-of-experts model with 23B active parameters per token, under the Apache 2.0 license, targeting coding and agentic use cases. The model supports a 1M-token context window using hybrid attention with 43 sliding-window and 9 global layers, and a residual stream that scales sublayer outputs by 1/sqrt(k) to maintain constant magnitude. Its MoE router uses a group-limited scheme with 256 experts, 8 routed per token, and the BF16 checkpoint occupies about 1 TB. Benchmarks show strong performance: 81.6% on SWE-bench Verified, 97.7% on AIME 2026, and 90.6% on GPQA Diamond, with minimal degradation in FP8 and NVFP4 quantizations. The model ships with a Rust-based harness, day-0 support in transformers, vLLM, and SGLang, and includes SFT and RL recipes, with a $10,000 Hub credits competition for community projects."
---

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

[Hugging Face](https://yomu.fyi/company/hugging-face) · ben burtenshaw, merve, Pedro Cuenca · Oct 16, 2026

**Type:** Announcement

## Summary

Reflection released Beam, a 501B-parameter mixture-of-experts model with 23B active parameters per token, under the Apache 2.0 license, targeting coding and agentic use cases. The model supports a 1M-token context window using hybrid attention with 43 sliding-window and 9 global layers, and a residual stream that scales sublayer outputs by 1/sqrt(k) to maintain constant magnitude. Its MoE router uses a group-limited scheme with 256 experts, 8 routed per token, and the BF16 checkpoint occupies about 1 TB. Benchmarks show strong performance: 81.6% on SWE-bench Verified, 97.7% on AIME 2026, and 90.6% on GPQA Diamond, with minimal degradation in FP8 and NVFP4 quantizations. The model ships with a Rust-based harness, day-0 support in transformers, vLLM, and SGLang, and includes SFT and RL recipes, with a $10,000 Hub credits competition for community projects.

## Context

Beam is the first open model from Reflection, a new US AI lab, released on Hugging Face. It is a 501B MoE model designed for long-context reasoning and agentic tasks, with a focus on coding. The model is fully open under Apache 2.0, allowing customization and self-hosting.

## Approach / What changed

The model uses a sparse mixture-of-experts architecture with 256 experts, 8 active per token, and hybrid attention with 43 sliding-window and 9 global layers. It includes a Rust-based harness for optimal performance and supports multiple quantization formats (BF16, FP8, NVFP4). The post provides recipes for inference, supervised fine-tuning, and reinforcement learning using transformers, vLLM, and SGLang.

## Takeaways

- Beam is a 501B MoE model with 23B active parameters per token, offering the capacity of a large model with the speed of a smaller one.
- The model achieves strong results on coding and reasoning benchmarks, including 81.6% on SWE-bench Verified and 97.7% on AIME 2026.
- Beam is fully open under Apache 2.0, with day-0 support in major inference libraries and a Rust-based harness for self-hosting.

## FAQ

### What hardware is needed to run Beam?

The NVFP4 checkpoint requires about 280 GB and runs natively on Blackwell GPUs; FP8 checkpoints are suitable for Hopper and Ampere GPUs. Two B200 GPUs (360 GB) can hold the weights.

### How does Beam handle long contexts?

Beam uses hybrid attention: 43 sliding-window layers cache only 512 tokens each, while 9 global layers cache the full sequence, reducing KV cache growth to 36 KiB per token.

### What quantization formats are available?

Beam checkpoints are available in BF16, FP8, and NVFP4 formats. NVFP4 stores most parameters in 4-bit precision for Blackwell GPUs, while FP8 is for older GPUs.

**Tags:** [AI](https://yomu.fyi/topic/ai), [Coding Agents](https://yomu.fyi/topic/coding-agents), [LLMs](https://yomu.fyi/topic/llm), [Mixture of Experts](https://yomu.fyi/topic/mixture-of-experts), [Open Source](https://yomu.fyi/topic/open-source)

- Source: [Hugging Face](https://huggingface.co/blog/reflection-beam)
- Source URL: https://huggingface.co/blog/reflection-beam
- Ingested by Yomu: 2026-10-16T15:00:14.378Z

[Read original post](https://huggingface.co/blog/reflection-beam)