Reflection Beam Is Out: 501B Open Weights, Apache 2.0

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Reflection AI released the open weights of Beam on October 16, 2026—its first open-weight model, a 501-billion-parameter sparse mixture-of-experts system under Apache 2.0. VentureBeat (Matt Marshall, 6:00 am PT) reported that Reflection “today released the weights,” a day after Meta’s Muse Spark Open launch and weeks after Reflection’s October 5 preview promised Apache 2.0 weights “later this month.”

What happened

According to Unite.AI (Theo Nash, October 16), Beam’s weights landed on Hugging Face under Apache 2.0, with Baseten offering day-0 support through Model APIs and Dedicated Inference. Reflection also opened a public beta of its first-party API at platform.reflection.ai, which Unite.AI says is free to start. The October 5 Reflection blog had introduced Beam as a text-only sparse MoE with 501 billion total parameters and 23 billion active per token, built for coding, reasoning, and agentic workloads—with midtraining extending context up to 1 million tokens.

Beam arrives amid a wider U.S. open-weight push. Meta’s Muse Spark Open (816 billion total / 42 billion active parameters) landed a day earlier; VentureBeat frames Meta’s pitch around capability and Reflection’s around token efficiency. Nvidia remains an investor and partner; Reflection CTO Ioannis Antonoglou told VentureBeat that a Financial Times report of acquisition talks was “baseless rumors,” while calling Nvidia “a close partner and investor” and saying the company aims to stay independent.

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Key details

Architecture and training scale

Per Reflection’s October 5 technical post—still the primary architecture source—Beam was pretrained on 23.8 trillion tokens. Its high-compute reinforcement learning run generated more than 100 million rollouts on 10.5K NVIDIA GB300 GPUs over four weeks; training and grading used roughly 1.3 billion sandboxes. Reflection applied a length penalty during RL so the model would solve tasks without unnecessary tokens, and users can adjust a reasoning-effort setting for shorter or longer answers.

Benchmarks Reflection cites

Reflection attributes these scores to its own evaluations (also cited via Unite.AI / Baseten coverage): Terminal-Bench 2.1 at 80.1%; SWE-bench Verified at 80.9%; GPQA Diamond at 90.5%; AIME 2026 at 97.8%; and MCP Atlas at 78.7%. On advanced reasoning, Reflection claims Beam achieves scores comparable to GLM-5.2 while using 3–4× less inference compute—an estimate based on active parameters and generated tokens, not a measured dollar bill, so treat it as the company’s comparison.

Hosting, pricing, and partners

Unite.AI lists launch platforms including Amazon Web Services, Baseten, OpenRouter, Google Cloud, Microsoft Azure, Modal, NVIDIA NIMs, Together AI, Vercel, and others, with Dell and HPE for on-premises and air-gapped installs. Hugging Face Transformers, vLLM, and several other serving stacks get day-0 support. Baseten’s Model API pricing, as stated by Unite.AI citing Baseten’s model library, is $1.30 per million input tokens, $4.30 per million cache input tokens, and $0.13 per million output tokens—report those figures as published; Reflection’s own hosted beta remains free while the company sets longer-term pricing, per VentureBeat.

Safety, Mirror CLI, and early users

Reflection published a Beam Safety Report with the weights release. VentureBeat says outside testers included Gray Swan, Irregular, and SecureBio. Reflection also open-sourced Mirror CLI, a terminal agent harness (Python frontend, Rust agent core) it says it uses internally. One early-user quote Reflection provided to VentureBeat came from Oura’s Health Intelligence Team, which said Beam had “fewer context leaks and was much more grounded in its answers than other frontier models we evaluated.”

Why it matters

  • Apache 2.0 open weights, not a preview. The October 4 “preps open-weight” story was about intent; today the weights are public, so teams can download, self-host, and fine-tune under a permissive license.
  • Efficiency pitch vs bigger rivals. With 23B active parameters versus Meta Muse Spark Open’s 42B active, Reflection is selling fewer tokens per finished coding or agent task—not just a lower sticker price per million tokens.
  • Day-0 ecosystem. Partner hosting (Baseten, OpenRouter, cloud providers) plus Transformers/vLLM support lowers the friction that often follows a Hugging Face dump alone.
  • Enterprise coding agents. Mirror CLI, on-prem paths via Dell/HPE, and a free public beta API target teams that want owned coding agents—including air-gapped setups—rather than only chat APIs.
  • Safety paperwork up front. A published safety report plus named outside testers give buyers something concrete to review alongside the model card.

What's next

  • Independent cost tests: VentureBeat says it is measuring cost per finished task for Beam against Muse Spark Open, DeepSeek V4.1 Flash, and GLM-5.2—watch those results before treating Reflection’s 3–4× compute claim as settled.
  • First-party pricing: Reflection’s hosted service is free in public beta; permanent API pricing and partner rate cards may still move.
  • Follow-on models: Unite.AI notes Beam as the first in a Reflection family, with multilingual and multimodal capabilities planned; Reflection also said self-distillation recipes were coming in the following days after the October 5 preview cycle.
  • Competitive scoreboard: Chinese open models still lead some agent benches on Reflection’s and Meta’s own published tables; Beam’s bet is winning on tokens-per-task and deployability for U.S. and Western buyers.

FAQ

What is Reflection Beam?

Beam is Reflection AI’s first open-weight model: a text-only sparse mixture-of-experts system with 501 billion total parameters and 23 billion active per token, aimed at coding, reasoning, and agentic work. On October 16, 2026, Reflection released the weights under Apache 2.0 on Hugging Face and opened a public beta API at platform.reflection.ai.

How does Beam compare to Meta Muse Spark Open?

Muse Spark Open launched a day earlier with 816 billion total and 42 billion active parameters; Meta emphasizes capability. Beam is smaller at the active-parameter layer (23B) and pitches token efficiency. Reflection has not published a direct head-to-head with Muse Spark Open; both labs’ own Terminal-Bench 2.1 scores still trail top Chinese open models on that test.

Where can developers run Beam, and what does it cost?

Weights are on Hugging Face under Apache 2.0 for self-hosting, with day-0 support in Transformers, vLLM, and other stacks. Reflection’s public beta API is free to start per Unite.AI. Baseten lists Model API prices of $1.30/M input, $4.30/M cache input, and $0.13/M output as reported by Unite.AI; other partners set their own rates.

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