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Always glad to see more open-weight models, but this caption on the 2nd demo image had me do a double-take: "Land or Water Generalization Experiment: We recreated the viral X puzzle by asking Beam to create a fixed 180×90 grid for longitudes -179° to 179° and latitudes -89° to 89°, with 16,200 points. This puzzle is a few days old, so could not appear in the training data, thus testing the model’s generalization. Beam gets 95.5% coverage right, putting us between Opus 5 (92.5%) and Fable 5 (97.8%), which shows how well it generalizes to novel new tasks."

Oof, no, this "puzzle is a few days old" is incorrect even if it's a social media trend just recently. Asking a model to generate a world map in this way is _at least_ from August 2025 as it appeared on LessWrong at that time: https://www.lesswrong.com/posts/xwdRzJxyqFqgXTWbH/how-does-a...

> Beam is a sparse Mixture-of-Experts model with 501 billion total parameters, 23 billion active, built for coding, reasoning, and agentic workloads.

> Beam’s capabilities come from major investments in both pretraining and reinforcement learning (RL). We pretrained the model on 23.8 trillion diverse, curated, high-quality tokens from the web and proprietary licensed datasets, matching or outperforming available similar-sized open base models. In parallel, we developed the algorithms, training environments, and infrastructure needed to sustain high-compute RL at exceptional scale. Our high-compute RL run generated over 100 million rollouts on 10.5K NVIDIA GB300 GPUs over 4 weeks of training.

Early access, no weights no tech details, just a sign up here for info

What kind of company or organization is Reflection?

I think it is ever more important to realize who is releasing models rather than what the models do and how they compare.

Because models iterate at breakneck speed, looking at today's benchmarks is only useful for someone using the models today. Whereas if one builds a product on top of it, or commits to one for a project or team, the company or organization behind it, is far more important. Will they exist in a few months? Do they need a business-model? Are they subsidizing usage with venture capital and how long can they keep this up?

Is reflection a company? University lab? NGO?

I thought it would be interesting to look at some key figures vs another contemporary model in the same weight class (DeepSeek V4.1 Flash)

                                DS V4.1F            Beam
    LM total params             552B                501B
    LM active params (prefill)  8B                  23B
    LM active params (decode)   16B                 23B
    N-gram/PLE params           196B                0
    Pretrain tokens             45T                 28T
    Disk KV bytes/token (FP4)   890                 No information
    Vision                      Yes (pretrain)      No
    Weights available           Yes (launch day)    "This month"
    Weights licence             MIT                 Apache 2.0

Bigger and still worse than existing free Chinese models that are smaller? Open weight models are nice, but at this point it seems western models are very far behind Chinese ones, despite Chinese companies publishing a lot of their findings. I hope we get more open models and more providers, as being stuck with a model from China or US with no competition is risky.

Google does do a great job with Gemma models. It's one of the few language models actually good at language. OpenAI's top closed models can't even write norwegian correctly.

I feel like this is a marketing miss. If they had held their announcement until the model was released, I would have grabbed it and started running it through my benchmarks. It probably doesn’t get a place in the rotation based on their own description of its performance, but now the weights live on the server, I’m probably following them on HF and I will remember to check in every time I ls the models folder. With the announcement only, none of that happens and I’m likely to forget about this by the time it actually gets released.

The email harvest move just doesn’t fit where we/they are in the cycle. There are established players and a buffet of models to choose from (plus a ton of empty hype). The first move at this point for any new entrant should be to show, not tell. Even an API only release with the promise to open weight would be better (actually probably all around better since most people can’t run this locally).

I wish this lab and all the labs releasing the best. It’s a brutal landscape to sink millions of dollars into for a guaranteed “behind x model from a year ago” evaluation. But, I do believe there is genuine innovation left to uncover.

This appears to be larger than DeepSeek v4.1 Flash, more expensive to run, and worse on every measured metric.

Am I missing something?

Is it worse than the top open-weight Chinese models? Yes, it is, but at least the West has joined the party, and hopefully they will iterate on this and keep up the pace. The Chinese labs will certainly release new and powerful versions soon, so it's all about relative pace right now.

I remember being in the room with pretraining day 1 to help monitor the training job launch. Watching this model train from day 1 has been an amazing experience!

I don't find open-weight models that impressive anymore. MiMo-V2.6 already showed that you can have a not so crazy architecture and enough compute, the bottleneck is then just the data. OAI and Anthropic are largely the frontier models because of the synthetic data they made. They have a large customer base and have the user's data as well as knowing what tasks their customers use the models for and what domain they should get synthetic data for.

Does this one also routes to Claude under the hood like Reflection 70B did? I recall they even run a basic regex to remove "Claude" from the output. Then they promised to be completely transparent on the postmortem (they claim they had no idea what had happened), but the postmortem never came

Releasing open-weight models at this scale is a massive milestone for the community. Access to transparent model internals is foundational for trustworthy systems.

Beggar choosing: my kingdom for more 90B-133B MoE local models. Especially with disk offload, that is a function/performance sweet spot for Mac workstations with 64GB-128GB of RAM.

Instead of yet another mediocre but fully-made-in-the-West open model (alongside Mistral, Trinity, Poolside, Inkling, etc etc) I'd really love for a Western neloab start the same way Qwen did: by focusing on post-training. Qwen's first release was a Llama 1 finetune [1]! Once they made it useful, they started working their way back in the stack to also do their own pretraining, etc. Starting with pretraining feels like such a waste: there's millions of dollars of crystallized compute and data sitting around in the Chinese model weights. Why not start with one of those, and only work your way back to pretraining once you've released something you can prove is useful?

1: https://en.wikipedia.org/wiki/Qwen

> Where frontier open models like Kimi K3 remain ahead on raw capability, Beam's advantage is efficiency at inference time.

It's great to see a company that acknowledges it still needs improvement instead of making false claims.

Hey since it's an open-weight model, I would love to know: how much model safety alignment have you done? Did you do any sort of post-training and what restrictions are in place

Ideally i would like to place my own restrictions and align from scratch, currently I am resolved to do harness alignment using tools like Prismor but would love to do my own post training alignment

I am really curious why after Qwen-3.8-flash and deepseek-v4.1-flash newer open weight models (or proprietary but they don't tell) don't use n-grams. It looks (to me) like they are a cheep way to add more knowledge to the model.

What do I mean by cheap? You can rely on the SSD to retrieve the relevant tokens as no computation is needed meaning you can leverage storage (or cpu ram if you don't have unified memory) to serve part of the model which (to my understanding) is much cheaper to get than GPU RAM.

Anyone know what am I missing? Or is it that the pace of iteration for labs slow enough that they can't actually leverage it yet?

What kind of machine does one need to run this model? What's the usual setup someone would have for running this?

the performance chart puts the better open source models behind the fold making it seem like it outperforms them... but it doesn't! all for open source models but this announcement is misleading

I'm on the waiting list... Couldn't find any download option, so I suppose it is only obtainable through their API. Strange way to distribute open weights model.

Any time a new lab shows up, folks complain about how their models are worse. Really? It would be nice if a new comer comes from no where and beats everyone, but that's rarely the case. The good thing is that other labs/people are figuring out how to build this, and if they keep at it then this is as bad as it gets for them and it would hopefully get better. A new entrant to the market is good for everyone.

Someone should name their next model "Workhorse" just for SEO reasons.

It's interesting how the industry converged to this very term, given that very less work is being done by horses since quite a while.

I'm a big fan of open-weight models.

It's true that no benchmark communicates the whole picture, and we won't really know how it behaves until weights are out, but the performance here doesn't seem particularly groundbreaking just based on the benchmark.

The meaningful test begins after the weights arrive: fixed harness, network disabled, repeated trials, and real latency, memory, energy and cost per completed task.

Very little in terms of the layers they use. Calling it now, they are using global layers everywhere, making the model basically unusable due to high KV Cache use.

If Beam "rivals GLM 5.2 on reasoning" does it mean it's as good as GLM 5.3 Flash? (a much smaller model)

Open-weight at 501B is huge. What was the eval setup - was web search disabled to test generalization?

Nitpicking but I really wish this benchmarks table were easier to read. Should show which columns win in each row and should not require horizontal scrolling to see across.

Minimum hardware to run this model?? doable in consumer hardware?

That was exciting... Will come back later when the weights are out and gguf'd.

Access is currently limited. We'll contact you if early access becomes available.

Open model that is not yet open or widely accessible via API. Primarily comparing to non-SOTA models like Inkling and GLM 5.2. Included comparison to GLM 5.3 and DeepSeek V4.1 Flash in the table, but not in the charts (I assume they would make them look bad). Also no results from AA Index or Arena.

very curious to see more about what kinds of hardware you can run this on and the perf. characteristics… on the face of it, it seems like optimizing for inference speed might(?) be good for running on smaller hardware, but i suppose it could be the other way around and it is actually much resource-hungrier for the number of parameters, etc. …

Noticed some good questions made dead at the bottom of this thread. Odd...

ok so they are comparing themselves to and claim to be beating GLM's last generation GLM 5.2 model, GLM 5.3 Flash is a monster, this is honestly embarassing

Am I the only one who thought of the BEAM VM after the first word of thee title?

If you don't buy into "America good, China bad" narrative, this new entrant & release by Inclusion Ai is a lot more exciting by every measurable metric.

https://github.com/inclusionAI/Ling

This is like an ad for how great Deepseek V4.1 Flash is.

Congrats to the reflection team

I am a great fan of open weights model, but off late I am starting to loose track on the capabilities of the latest models released and now a days most of the open weights models are above 100 B params which is not going to run in our laptops. What happened to Jev hype? A 501B Beam model is not going to help with it (Non AR Schema driven responding under 1 second).

I am more interested with a SOTA Frontier 8B-10B model. Is this even possible?

fauxpen like OpenAI? says open but no weights. Feels like a bid to get a buyout before the weights go public. Doesn't even have technical details.

Suppose I inherited a data center spanning several hundred acres full of GPUs and free electricity.

Where do I get the data?

I mean, this many models. They have to start somewhere.

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pretty wild that, per public sources, Reflection AI raised over $4 billion and hit a $25 billion valuation while operating in total stealth, without ever releasing a single public product until now. Beam (501B) seems be their first-ever model drop. Or am I missing something?