A company that raised over $4 billion at a $25 billion valuation while shipping nothing publicly has finally shown its hand. On October 5, Reflection AI announced Beam, its first open-weight model: a sparse Mixture-of-Experts system with 501 billion total parameters and 23 billion active per token, built for coding, reasoning, and agentic workloads. A Hacker News commenter flagged just how unusual that is: per public sources, this stealth outfit had never released a single public product until now.

The hard numbers

Beam was pretrained on 23.8 trillion tokens from web and proprietary licensed datasets. The more interesting part is reinforcement learning: Reflection ran a four-week high-compute RL campaign on 10.5K NVIDIA GB300 GPUs, generating more than 100 million rollouts with a maximum context length of 256K. Training and grading consumed roughly 1.3 billion sandboxes, backed by a pool of one million coding, agentic, and STEM environments. The company calls it "one of the largest scale RL runs conducted by any open lab to date" — their own framing, worth noting. For scale, Reflection's comparison puts Inkling at 30M rollouts and MiMo at 753K.

One engineering detail stands apart: Beam was trained with fully asynchronous policy gradients. Reflection claims numerics stayed stable even when samples were generated more than a day earlier — 107 weight versions behind the current policy. Solving staleness at that scale may matter more than any single benchmark score.

Benchmarks: honest, not triumphant

The published tables span coding, agentic, reasoning, and general benchmarks:

  • DeepSWE v1.1: Beam 44.4 vs GLM 5.2 at 44.0, Kimi K3 at 68.0, DeepSeek V4.1 Flash at 74.2
  • Terminal Bench v2.1: Beam 80.1, just under GLM 5.2's 81.0; Kimi K3 88.3, DeepSeek V4.1 Flash 90.6
  • SWE Bench Pro v2-Hard: Beam 77.2, well above Inkling's 56.9, below GLM 5.3's 84.3 and Kimi K3's 88.2
  • AIME 2026: Beam 97.8 vs GLM 5.2's 99.2
  • HLE without tools: 36.2, behind GLM 5.3's 42.3 and Kimi K3's 46.9

Reflection's own framing is blunt: competitive with GLM 5.2 and approaching Qwen 3.8-Max on coding and agentic tasks, while "Kimi K3 remains ahead on raw capability." No pretend crown here.

The actual pitch: inference efficiency

Beam's real claim is efficiency — scores comparable to GLM-5.2 on advanced reasoning benchmarks at 3–4× less inference compute, with even bigger gaps against 2T+ families like Qwen 3.8-Max. The mechanism is unglamorous: 23B active parameters are simply cheaper per token than 2T-class models. Reflection estimates FLOPs as roughly 2 × active parameters × generated tokens, drawing comparison data from Artificial Analysis and DataCurve.

The model ships with a reasoning-effort parameter: lower settings bias toward shorter answers, higher settings allow longer reasoning. During RL, a controllable length penalty taught it to solve tasks with fewer tokens first, then expand reasoning where it paid off. One notable generalization: browsing ability improved even though browsing tasks were absent from the RL mixture; given web access, the model organically learned to search and query other LLMs and use OCR APIs to read documents.

Weights land later this month

Mind the release state: Beam is still in final red-teaming, with early access limited to a waitlist. Weights, technical report, model card, and a full stack for running, evaluating, and fine-tuning arrive "later this month" under Apache 2.0 — the official blog's wording, which trumps aggregator pages already tagging Beam as "downloadable."

On safety, Reflection used deliberative alignment — baking the safety policy into the model's reasoning — and says its reward forecasting hit r = 0.79 versus 0.46 for a Best-of-N ceiling. The company promises to open-source the safety evaluation suite it built and used internally.

So what

The open-weights scoreboard is shifting from "biggest parameter count" to "cheapest inference at frontier capability." A 501B-total / 23B-active MoE is the most explicit bet yet that enterprises care more about cost per token than leaderboard rank — a workhorse pitch, not a trophy pitch. Circle the end of this month: the day independent teams can actually download the weights and re-run these numbers is the day this stealth company gets a real grade.