Reflection Announces Beam, a 501B Open-Weight Model, but Has Not Named a Licence

Reflection AI announced Beam on Monday, a 501-billion-parameter mixture-of-experts model with 23 billion active parameters, and said it will release the weights later in October. The company's launch post does not state a licence.

As of Tuesday, access is by waitlist at platform.reflection.ai. The post lists no API price. The weights, a technical report, a model card and developer artifacts are all described as coming "later this month".

What Reflection says Beam is

Beam was pretrained on 23.8 trillion tokens drawn from the web and what the post calls proprietary licensed datasets. Its context window is 256,000 tokens during reinforcement learning, extended to 1 million tokens of effective length.

Pretraining ran on 6,144 Nvidia GB300 NVL72 GPUs in under four weeks, with a goodput of 92.3%. The reinforcement-learning run used 10,500 GB300 GPUs for four weeks and produced more than 100 million rollouts across roughly 1.3 billion sandboxes.

SiliconANGLE reported that Reflection raised money at a $25 billion valuation a few months ago, and that it signed a $6.3 billion deal with SpaceX to rent GB300 NVL72 systems. Misha Laskin and Ioannis Antonoglou are named there as its founders.

The scores are Reflection's own

Every figure below comes from the launch post. No independent party is named as having run them.

Benchmark	Beam score
SWE Bench Verified	80.9
Terminal Bench v2.1	80.1
SWE Bench Pro v2-Hard	77.2
SWE Bench Pro v1	65.5
GPQA Diamond	90.5
AIME 2026	97.8

1. SWE Bench Verified80.9 score
2. Terminal Bench v2.180.1 score
3. SWE Bench Pro v2-Hard77.2 score
4. SWE Bench Pro v165.5 score

Source: Reflection AI launch post, accessed 2026-10-06

Reflection calls Beam "competitive with larger open models like GLM 5.2 and approaching Qwen 3.8-Max on coding and agentic tasks". It says Kimi K3 remains ahead on raw capability, and that Beam's edge is efficiency: on advanced reasoning benchmarks it matches GLM-5.2 while using 3 to 4 times less inference compute.

In one test, Reflection recreated a viral land-and-water puzzle on a 180 by 90 grid of 16,200 points. Beam got 95.5% of points right, which the post places between Opus 5 at 92.5% and Fable 5 at 97.8%.

What the post leaves out

The post names no licence. Open-weight releases differ on whether commercial use, fine-tuning for resale or redistribution is allowed, and Reflection's own marketing calls Beam "open-weight" without saying which terms apply. SiliconANGLE headlined it open source, a label that needs a licence to check.

The post also gives no download size and no minimum hardware. At 501 billion parameters, the weights alone will not fit on a single consumer GPU, but Reflection has not said what precision it will ship.

Efficiency claims such as the 3 to 4 times figure depend on the benchmarks and token budgets Reflection chose, and a technical report is the earliest place to check them.

Reflection said the weights, report and model card will be published by the end of October. The licence text, once the weights are up, decides whether Beam belongs alongside Aleph Alpha's Apache-licensed Kolibri-1 or Cloudflare's Clef models.