Reflection AI releases open-source model Beam and benchmarks against Chinese models

1. 21:05
   网页Axios· Discussion

   "I don't want an oligopoly": New open-weight AI models mount a comeback against China(opens the original post in a new tab)
2. 20:11
   The Decoder

   Reflection releases open-weight model Beam, claiming performance surpasses all open-source models outside China

   Reflection releases its first open-weight model Beam, using a MoE architecture that activates 23 billion of 501 billion total parameters per token, focusing on coding, logical reasoning, and agent tasks at low compute. Reflection claims Beam reaches GLM 5.2-level performance on reasoning tasks with 3 to 4 times less compute, with a SWE-bench Verified score of 80.9, but raw performance still trails Kimi K3. Model weights will be released under Apache 2.0 later this month, currently still in final safety testing.

3. 15:00
   网页South China Morning Post · Tech· Discussion

   Nvidia-backed Reflection AI challenges Chinese dominance in open-weight models(opens the original post in a new tab)
4. 14:35
   RedditReddit r/LocalLLaMA· Discussion

   Reflection’s Beam 501B-A23B - open-weight model release this month(opens the original post in a new tab)
5. 14:28
   latent.space

   Reflection releases Beam: a 501B-A23B open-source American model.

   Reflection releases Beam, a text MoE model with 501B total parameters and 23B active, targeting coding, agent, and research tasks, trained from scratch, with full weights to be released under Apache 2.0 this month. Its pretraining used 23.8T tokens, with about four weeks of pretraining and four weeks of RL on approximately 10,500 GB300 units, claiming a SWE-bench Verified score of 80.9. Artificial Analysis expects it to be one of the most token-efficient open-source models at the same intelligence level, but some benchmarks still lag behind models like DeepSeek V4 Flash.

6. 13:22
   Help Net Security AI

   Reflection AI releases Beam, an open-weights model with 501 billion parameters; it lags behind leading open-source models on coding benchmarks but claims lower inference compute.

   Reflection AI releases Beam, an open-weight model with 501 billion parameters, using a MoE architecture with 23 billion active parameters per token, and plans to publicly release the weights under the Apache 2.0 license later this month. On Terminal Bench v2.1, Beam scores 80.1, below GLM 5.2's 81.0, Kimi K3's 88.3, and DeepSeek V4.1 Flash's 90.6; on DeepSWE v1.1 it scores 44.4. Reflection states that Beam's advanced reasoning benchmarks are comparable to GLM 5.2, with three to four times less inference compute usage; this estimate is based on a formula and does not account for prompt processing and attention overhead. Safety evaluations and red teaming results will be released alongside the weights.

7. 12:32
   RedditReddit r/LocalLLaMA· Discussion

   Western open-source AI Reflection finally shows up(opens the original post in a new tab)
8. 08:46
   网页SiliconANGLE · AI· Discussion

   Reflection AI debuts open-source Beam model with 501B parameters(opens the original post in a new tab)
9. 08:42
   网页IT之家· Discussion

   Taking on DeepSeek and Kimi, NVIDIA-backed Reflection AI releases its first open-weights AI model, Beam(opens the original post in a new tab)
10. 06:27
    RedditReddit r/LocalLLaMA· Discussion

    Introducing Beam: Reflection’s 501B open-weight model — Reflection(opens the original post in a new tab)
11. 05:22
    网页The Information· Discussion

    Reflection AI Announces First Open Source Model, Beam(opens the original post in a new tab)
12. 05:19
    XX @ArtificialAnlys· Discussion

    Artificial Analysis has been given access by Reflection and is independently benchmarking Beam(opens the original post in a new tab)
13. 04:00
    XX @rohanpaul_ai· Discussion

    The much anticipated open-source model from Reflection AI just dropped.(opens the original post in a new tab)
14. 03:43
    XX @ClementDelangue· Discussion

    Let's go https://huggingface.co/reflection!(opens the original post in a new tab)
15. 03:25
    网页Techmeme· Discussion

    NYC-based Reflection unveils Beam, an open model it says rivals GLM 5.2 on reasoning while using 3x-4x less compute and approaches Qwen3.8-Max on agentic tasks (Semafor)(opens the original post in a new tab)
16. 03:20
    XX @natolambert· Discussion

    Congrats to Reflection folks -- it's hard to get the first model out, and we hope you can rapidly accelerate contributions to the ecosyst...(opens the original post in a new tab)
17. 03:00
    Financial Times · 人工智能

    Reflection AI launches Beam model, benchmarking against Chinese open-source models

    Reflection AI releases new model Beam, claiming its performance is comparable to China's leading open-source models. The startup's move aims to strengthen the US's ability to compete with China in the open-source model space.

18. 03:00
    网页Bloomberg Technology· Discussion

    Nvidia-Backed Reflection Unveils Open AI Model, Taking on China(opens the original post in a new tab)

1. 13:33
   RedditReddit r/LocalLLaMA· Discussion

   Reflection AI Is About to Release a US Open-Weight Model to Take On DeepSeek and Qwen(opens the original post in a new tab)
2. 10:02
   网页IT之家· Discussion

   Sources say Reflection and several other Western companies will release open-weights AI models this month(opens the original post in a new tab)
3. Time not given
   Reflection

   Reflection announces the Beam model, with weights to be opened later this month.

   Reflection 公布首个计划开放权重的模型 Beam，采用稀疏 MoE 架构，总参数501B、激活23B，面向编码、推理与智能体任务。公司称模型在23.8万亿token上预训练，随后用10.5K张NVIDIA GB300进行四周强化学习训练，生成超过一亿次rollout，最大上下文256K token。Beam仍在最终红队测试与评估，权重、技术报告和模型卡计划本月晚些时候发布，尚不能据此称权重已经开放。

1. 20:49
   网页Axios· Discussion

   Scoop: A powerful new model from startup Reflection is set to shake up the AI race(opens the original post in a new tab)

1. Date not given
   Reflection

   Reflection releases the Open Safety Framework, defining eight categories of systemic risk and release thresholds.

   Reflection AI releases the Open Safety Framework, publicly committing to the identification, evaluation, and mitigation of systemic risks from frontier and open-source models, covering catastrophic risks as defined by California's TFAIA and systemic risks under the EU AI Act. The framework lists eight categories of risk, including power concentration, biological weapons, chemical weapons, cyberattacks, loss of control, and, listed as research-type risks, radiological weapons, nuclear weapons, and harmful manipulation. Release decisions balance concentration risks against proliferation risks, with open release as the default, but a backstop mechanism is activated when the direct harm of a capability could be both catastrophic and irreversible. The framework sets Limited and Critical thresholds for established risks; Critical requires an affirmative safety case after mitigation verification before release, and establishes accountability structures including a governance committee chaired by the CEO, a governance council, and an AI governance lead, while reserving up to 30 days for voluntary testing by the U.S. government.

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