Reflection AI Beam: Specs, Benchmarks, and Hardware Needs
Reflection AI's Beam is a 501-billion-parameter open-weight model that activates only 23 billion parameters per token. It targets coding, reasoning, and agentic workloads. Reflection announced it on October 5, 2026 and says the weights will ship under an Apache 2.0 license later this month.
Last updated: October 11, 2026. The weights are not downloadable yet. We will update this post when they are released.
What is Reflection AI Beam?
Beam is the first open-weight model from Reflection AI, a startup founded in 2024 by former Google DeepMind researchers Misha Laskin and Ioannis Antonoglou. It is a sparse Mixture-of-Experts (MoE) model, meaning only a fraction of its parameters run for each token. Reflection claims this makes it cheaper to serve than dense models of comparable quality.
Reflection is positioning Beam as a Western counterpart to Chinese open-weight models like DeepSeek, Qwen, GLM, and Kimi, and is going after enterprise, government, and developer adoption.
Beam specs at a glance
| Spec | Beam (as announced) |
|---|---|
| Developer | Reflection AI |
| Announced | October 5, 2026 |
| Architecture | Sparse Mixture-of-Experts, text-only |
| Total parameters | 501B |
| Active parameters per token | 23B |
| Context window | Up to 1 million tokens |
| Pretraining data | 23.8 trillion tokens |
| Focus | Coding, reasoning, agentic workloads |
| License | Apache 2.0 (at weights release) |
| Availability | Early-access waitlist; weights due later in October 2026 |
All figures come from Reflection's announcement and launch coverage. None have been independently verified.
Is Beam really open source?
Beam is open-weight, not open source in the full sense. Reflection plans to release the model weights, a technical report, a model card, and tooling to run, evaluate, and fine-tune it. The training data and full training pipeline are not included.
The Apache 2.0 license is the part most builders care about. It permits commercial use, modification, fine-tuning, and redistribution with few restrictions. The caveat: as of this writing, the weights have not been published, so the license is a promise, nothing more.
Beam benchmarks: how it compares
Reflection published a scorecard comparing Beam with several open models. These are company-reported numbers. NR means the model's score was not reported.
| Benchmark | Beam | GLM 5.2 | GLM 5.3 | Kimi K3 | Qwen 3.8 Max |
|---|---|---|---|---|---|
| SWE-Bench Pro v1 | 65.5 | 62.1 | NR | NR | 67.7 |
| SWE-Bench Pro v2-Hard | 77.2 | NR | 84.3 | 88.2 | NR |
| DeepSWE v1.1 | 44.4 | 44.0 | 61.0 | 68.0 | 51.0 |
Beam edges out GLM 5.2 on SWE-Bench Pro v1 and roughly ties it on DeepSWE. On the harder coding tests, Kimi K3 and GLM 5.3 score clearly higher, and Reflection itself says Kimi K3 remains ahead on raw capability.
Reflection's pitch is efficiency: it says Beam matches GLM-5.2 on advanced reasoning benchmarks while using 3 to 4 times less inference compute. That estimate leaves out parts of the serving workload, so treat it as a hypothesis until the weights drop and someone measures it independently.
Company-reported benchmarks set expectations, not facts. The efficiency claim only matters once independent labs reproduce it on real workloads.
Shayan Ali
How Beam compares with other new open models
Thinking Machines' Inkling was released in July and is multimodal, while Beam is text-only. In Reflection's own table, Beam outscores Inkling on the coding tests where both report results.
Mistral Large 4 is a 1-trillion-parameter preview released a day after Beam, with weights also promised by the end of October.
DeepSeek V4 activates about 49B parameters per token versus Beam's 23B. That gap is what Reflection is pointing at when it talks about efficiency.
Can you run Beam on a VPS? Hardware requirements
A standard VPS cannot run Beam. The reason is a common MoE misunderstanding: only 23B parameters are active per token, so the compute per token resembles a mid-size model, but all 501B parameters still have to sit in GPU memory because any expert can be called.
Here is a weights-only memory estimate (parameters times bytes per parameter). Reflection has not published official hardware requirements, and the KV cache for long contexts adds substantially on top.
| Precision | Approx. weight memory | Minimum 80 GB GPUs (weights only) |
|---|---|---|
| BF16 / FP16 | ~1,002 GB | 13 |
| FP8 | ~501 GB | 7 |
| 4-bit quantized | ~250 GB | 4 |
In practice, plan for a multi-GPU node (8× H100 or H200 class for FP8) rather than a single card. The model card, expected with the weights, should confirm real memory targets.
Practical ways to use Beam
- Hosted access. Reflection says Beam will launch through hyperscalers and neoclouds, with integrations into open-source libraries. For most small teams this will be cheaper than owning GPUs. Compare per-token prices across providers once they publish rates.
- Rented GPU cloud. Rent an 8-GPU node by the hour, load the weights, and serve them with an open-source inference engine. Works well for testing and bursty workloads.
- A VPS as the gateway. Run your app, API gateway, rate limiting, logging, and caching on an ordinary VPS, and send inference to whichever hosted or GPU endpoint you choose. This is the setup most developers will actually end up with. For providers suited to this role, see our guide to Best Offshore VPS Hosting in 2026.
Who is Reflection AI, and why does Beam matter?
Reflection started with autonomous coding agents and raised $2 billion at an $8 billion valuation in late 2025, led by Nvidia. It has since been reported to be in talks to raise $2.5 billion at a $25 billion pre-money valuation. It has also signed a deal with South Korea's Shinsegae Group for a 250-megawatt data center running Korean-language models, a deal the US Commerce Department is reportedly viewing as a blueprint for AI exports.
Beam is the first product that puts that valuation to the test. The US has generally trailed Chinese labs on open-weight models, and Beam is being watched as a gauge of whether an American lab can close the gap.
What to check when the weights are released
- Does the license on the Hugging Face repository actually say Apache 2.0?
- What does the model card list as memory and hardware requirements?
- Do independent evaluators confirm the 3 to 4× inference efficiency claim?
- Which hosted providers offer Beam, and at what price per million tokens?
- How does it perform on your own repositories and workflows, not just public benchmarks?
Frequently asked questions
Q: When will Reflection Beam's weights be released? A: Reflection says later in October 2026, after final red-teaming and evaluations. No exact date yet.
Q: Is Reflection Beam free to use commercially? A: Reflection says the weights will use the Apache 2.0 license, which allows commercial use. Confirm the license file when the weights ship.
Q: How many parameters does Beam have? A: 501 billion total, with 23 billion active per token.
Q: Is Beam better than GLM-5.2? A: By Reflection's own numbers, it is competitive: slightly ahead on some coding benchmarks and roughly tied on others, with a claimed 3 to 4× inference cost reduction. Independent tests have not confirmed this.
Q: Is Beam better than Kimi K3? A: No, going by Reflection's own table. Kimi K3 scores higher on the harder coding benchmarks, and Reflection acknowledges it leads on raw capability.
Q: Can I run Beam on my own server? A: Only with a multi-GPU node. Even at 4-bit quantization the weights alone are roughly 250 GB, so a normal VPS or a single consumer GPU will not cut it.
Q: Does Beam support images? A: No. Beam is text-only.
Bottom line
Beam is the strongest US entry in the open-weight race against Chinese models so far, but right now it is an announcement, not a downloadable model. The efficiency claim is the part worth watching, and also the part that needs independent measurement. Wait for the weights, the model card, and third-party benchmarks before building on it.
Sources
- Reflection AI, "Introducing Beam: Reflection's 501B open-weight model" (October 5, 2026): https://reflection.ai/blog/introducing-beam
- TechCrunch coverage of the Beam launch (October 2026)
- Technology.org, "Nvidia-Backed Reflection AI Launches Beam" (October 6, 2026): https://www.technology.org/2026/10/06/reflection-ai-beam-open-weight-model-501b/
If you are evaluating self-hosted inference for large MoE models or comparing GPU cloud pricing, feel free to reach out.
