Apache

Beam

Reflection AI launched Beam, its first open-weight model, in early access on October 5.The model is wrapping up red-teaming, and the weights, technical report, and model card follow later this month.
The weights ship under an Apache 2.0 license, Reflection says, with the full stack to run, evaluate, and fine-tune Beam.
<h3 id="span-style-font-weight-400-glm-5-2-parity-comes-with-3-4-less-inference-compute-span">GLM-5.2 parity comes with 3–4× less inference compute</h3>
Beam is a sparse mixture-of-experts model with 501 billion parameters and 23 billion active parameters per token. It was pre-trained on 23.8 trillion tokens, and its context length reached 1 million tokens during mid-training.
Reflection says Beam matches Z.ai’s GLM-5.2 on advanced reasoning tests while using “3–4× less inference compute.” Claims not verified independently.
GLM-5.2 has around 744 billion parameters, 40 billion active. Reflection puts Kimi K3 ahead in raw capability and pitches Beam’s advantage as inference efficiency.
Against Thinking Machines Lab’s Inkling, released in July, Reflection’s tables show Beam ahead on four coding benchmarks with scores for both models.
On SWE Bench Pro v2-Hard, Beam scored 77.2 to Inkling’s 56.9. Inkling is multimodal, while Beam is text-only.
<figure style="width: 100%" class="wp-caption alignnone"><img src="https://www.cryptopolitan.com/wp-content/uploads/2026/10/2026-10-06-reflection-ai-beam-early-access-open-weights-01.png" alt="Reflection AI's Beam opens to early access, with open weights due later in October." width="596" height="476">
 <figcaption>
 Beam and Inkling coding benchmark scores from Reflection’s October 5, 2026 <a href="https://reflection.ai/blog/introducing-beam" rel="noopener noreferrer nofollow">announcement</a>.
 </figcaption>
</figure>
<h3 id="span-style-font-weight-400-10-500-nvidia-gb300-gpus-ran-for-four-weeks-of-rl-span">10,500 Nvidia GB300 GPUs ran for four weeks of RL</h3>
Reflection’s RL run used 10,500 Nvidia GB300 GPUs for four weeks, produced more than 100 million rollouts, and utilized about 1.3 billion sandboxes for training and grading.
It’s one of the largest RL runs by any open lab, the company says. Inkling, by Reflection’s count, trained on 30 million rollouts.
Reflection was set up in 2024 by Misha Laskin and Ioannis Antonoglou, who are both former Google DeepMind researchers. It has raised about $4.7 billion from investors, including Nvidia, Sequoia Capital, and Lightspeed Venture Partners, most recently at a $25 billion pre-money valuation.
A year ago, it raised $2 billion at an $8 billion valuation, <a href="https://www.cryptopolitan.com/reflection-ai-raises-2b-deepseek-rival/" rel="noopener noreferrer nofollow">Cryptopolitan</a> reported. Its SpaceX deal alone runs up to $6.3 billion, at $150 million a month for Nvidia GB300 capacity at Colossus 2 near Memphis, <a href="https://www.cryptopolitan.com/reflection-ai-billion-compute-deal-spacex/" rel="noopener noreferrer nofollow">Cryptopolitan</a> reported in June.
Beam also fuels Reflection’s “AI factory” pitch, which lets institutions train its models on their own data and run them on their own compute. Shinsegae Group is trying out a sovereign edition in South Korea.
“They’re kind of like rocket ships,” Laskin has said of his models’ path to the frontier. “To build a big rocket ship, it takes time.”
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Reflection AI opens early access to Beam ahead of an October open-weight release

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