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Beam

Highly efficient agentic 501B parameter open model

Open Source
Artificial Intelligence
Visit WebsiteSee on Product HuntTwitter ⧉Hugging Face ⧉

Hunted byChris MessinaChris Messina

Beam is a sparse Mixture-of-Experts model with 501 billion total parameters, 23 billion active, built for coding, reasoning, and agentic workloads.

Top comment

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.

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 an open source Rust-based custom harness.

  • Apache 2.0.

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About Beam on Product Hunt

“Highly efficient agentic 501B parameter open model”

Beam launched on Product Hunt on October 19th, 2026 and earned 127 upvotes and 1 comments, placing #6 on the daily leaderboard. Beam is a sparse Mixture-of-Experts model with 501 billion total parameters, 23 billion active, built for coding, reasoning, and agentic workloads.

Beam was featured in Open Source (69k followers) and Artificial Intelligence (481.4k followers) on Product Hunt. Together, these topics include over 147.5k products, making this a competitive space to launch in.

Who hunted Beam?

Beam was hunted by Chris Messina. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.

Want to see how Beam stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.