Reflection AI Releases Beam 1.0: 501B Open-Weight MoE Model Arrives on OpenRouter and Hugging Face

The Three-Question Framework

What Happened?

On October 16, 2026, Reflection AI officially published public model weights for Beam 1.0 (501B total, 23B active parameters) under the Apache 2.0 license on Hugging Face, alongside active hosted endpoints on OpenRouter across four commercial providers.

Why It Matters

Beam 1.0 delivers an open-weight Western mixture-of-experts model optimized specifically for long-horizon agentic workflows, featuring a 1-million-token context window at $1.00 per million input and $3.00 per million output tokens on OpenRouter.

What Could Happen Next

Independent benchmark organizations including Artificial Analysis, SWE-bench Verified, and LMSYS Chatbot Arena are processing official submissions, while quantization contributors compile community GGUF, FP8, and NVFP4 checkpoints.

Executive Key Takeaways

Beam 1.0 activates 23 billion parameters per token out of 501 billion total parameters across a sparse mixture-of-experts topology.

Provides a native 1,048,576-token context window with hosted OpenRouter pricing at $1.00 prompt, $3.00 completion, and $0.10 cache read per million tokens.

Pretrained across 23.8 trillion tokens using 6,144 Nvidia GB300 GPUs in under four weeks.

Checkpoints released on Hugging Face (reflection/Beam-1.0-501B-A23B) under the permissive Apache 2.0 license with FP8 and NVFP4 distribution formats.

Source: OpenRouter API model catalog addition (reflection/beam-1.0-501b-a23b) on October 16, 2026, and model repositories published on Hugging Face by Reflection AI.

NextVector previously tracked Reflection AI's initial architectural preview of Beam on October 8, noting that model entry into the curated catalog remained pending until public weights and live inference endpoints were verified. With the arrival of live endpoints on OpenRouter backed by four independent infrastructure providers and public repository releases on Hugging Face, Beam 1.0 is now fully tracked in the NextVector model directory as model-beam-1-0-501b-a23b.

The model was pretrained on 23.8 trillion tokens of synthetic reasoning and text data on a cluster of 6,144 Nvidia GB300 GPUs over a four-week training window. The architecture pairs 501 billion total parameters with 23 billion active parameters per token, utilizing fine-grained expert routing designed to reduce memory bandwidth overhead during multi-turn agentic coding sessions.

On OpenRouter, Beam 1.0 is listed at $1.00 per million input tokens, $3.00 per million output tokens, and $0.10 per million cached tokens, positioning it directly against frontier MoE models like DeepSeek-V3.2 and Meta Muse Spark Open. The release includes full precision BF16 weights as well as hardware-accelerated FP8 and NVFP4 builds developed for Nvidia Blackwell infrastructure.

What remains to be established are independent third-party evaluations. While Reflection reported internal benchmarks of 80.9 on SWE-bench Verified and 90.5 on GPQA Diamond, community benchmarks from LMSYS Arena and Artificial Analysis have not yet posted verified blind scores.

The Three-Question Framework

What Happened?

Why It Matters

What Could Happen Next

Executive Key Takeaways

Primary Sources & Citations