AnswersPublished26 sources
Reflection AI’s Beam: What the Open-Weight Model Claims—and What We Know
Announced October 5, 2026, Beam is Reflection AI’s text model with 501 billion total parameters and 23 billion active per token, built for coding, reasoning and agent tasks. Reflection plans to release the weights under Apache 2.0 later in October; early access is offered through a waitlist.
Published byEdited with GPT-6 LunaImages generated with GPT Image 2
Research answer
Key takeaways
- Announced October 5, 2026, Beam is Reflection AI’s text model with 501 billion total parameters and 23 billion active per token, built for coding, reasoning and agent tasks.
- Reflection plans to release the weights under Apache 2.0 later in October; early access is offered through a waitlist.
AI Prompt
openai.comCreate a landscape editorial hero image for this Studio Global article: What is Reflection AI’s Beam model, announced on October 5, 2026, and how do its architecture, training, context window, intended uses, comp. Article summary: Reflection AI announced Beam on October 5, 2026, as its first planned open-weight model: a text-only system aimed at coding, reasoning and AI-agent work. It is positioned as a U.S. alternative to leading Chinese open mod. Topic tags: general, news, general web, user generated. Style: premium digital editorial illustration, source-backed research mood, clean composition, high detail, modern web publication hero. Use reference image context only for broad subject, composition, and topical grounding; do not copy the exact image. Avoid: logos, brand marks, copyrighted characters, real person likenesses, fake screenshots, UI text, readable text, watermarks, charts w
Reflection AI announced Beam on October 5, 2026, as its first open-weight model. The text-only system targets coding, reasoning and AI-agent work, with Reflection positioning it as an alternative to leading open models from China and the West. Its headline performance and efficiency comparisons should be treated as company claims until the weights and evaluation details can be checked independently. 3
7
13
Beam’s architecture, training and context
Beam is a sparse mixture-of-experts model: it has 501 billion parameters in total, with about 23 billion active for each token. Reflection says it pretrained the model on 23.8 trillion tokens and then used high-compute reinforcement learning; the company’s announcement says that training run generated more than 100 million rollouts. 14
Reflection reports a context window of up to 1 million tokens. There is a practical caveat: third-party reporting on the early beta interface describes a 256,000-token limit. The announced model context and the limit users can access through a particular service may therefore differ; the available information does not establish that the beta limit applies to every deployment. 3
7
17
18
What the benchmark comparisons say
Reflection reported an 80.9% score on SWE-bench Verified. On Terminal-Bench v2.1, a separate reported comparison puts Beam at 80.1, below GLM-5.2 at 81.0, Kimi K3 at 88.3 and DeepSeek V4.1 Flash at 90.6. These results suggest Beam may be competitive on some tasks, but they do not support a broad claim that it outperforms leading Chinese or Western open models. 12
20
Reflection also says Beam matches GLM-5.2-class reasoning on selected benchmarks. The company claims its estimated generation compute is roughly three to four times lower than GLM-5.2’s and more than four times lower than that of leading Western open models in its comparisons. Those are estimates, not independent measurements showing that every user will see the same reduction in serving cost or latency. 13
16
The weights are not yet available for independent testing, so readers should treat the benchmark results and compute comparisons as preliminary. Differences in evaluation setup and serving conditions can affect model comparisons; the reported figures alone do not establish how Beam will perform across other tasks or deployments. 7
13
Release plans and access
Reflection plans to release Beam’s weights under the Apache 2.0 license later in October 2026, but the sources available do not give an exact release date. Early access is available through a waitlist. Reporting also describes plans for supporting materials, including a technical report and model card; until those materials are published, the scope of the release remains uncertain. 2
3
21
Open weights would let developers run and adapt the released model, but that possibility is not the same as a verified, ready-to-deploy system for a particular organization. Beam is aimed at developers, businesses and public-sector customers; the available launch reporting does not establish a specific agency deployment or a model customized on an institution’s private data. 1
5
31
How Beam fits Reflection AI’s strategy
Beam is a model launch backed by a large infrastructure push. Reporting describes a $6.3 billion SpaceX agreement involving Nvidia GB300 systems, as well as a separate compute agreement with Nebius. Those deals indicate the scale of infrastructure associated with Reflection’s ambitions, but they do not independently validate Beam’s performance or its estimated inference savings. 4
8
For now, the clearest way to assess Beam is to separate its published specifications from its unverified comparisons: the architecture, training scale and intended workloads have been announced, while the model’s relative benchmark performance and real-world efficiency remain claims to test once the weights and fuller evaluation materials are available. 7
13
14
Studio Global AI
Continue your research
This page includes a source-backed answer you can continue inside Studio Global.
People also ask
What is the short answer to "Reflection AI’s Beam: What the Open-Weight Model Claims—and What We Know"?
Announced October 5, 2026, Beam is Reflection AI’s text model with 501 billion total parameters and 23 billion active per token, built for coding, reasoning and agent tasks.
What are the key points to validate first?
Announced October 5, 2026, Beam is Reflection AI’s text model with 501 billion total parameters and 23 billion active per token, built for coding, reasoning and agent tasks. Reflection plans to release the weights under Apache 2.0 later in October; early access is offered through a waitlist.