# Reflection AI's Beam, explained: the open-weight model that announced early and shipped two weeks later > Reflection AI's 501-billion-parameter open-weight model Beam: verified specs, independent benchmarks against Kimi K3 and DeepSeek, and what actually shipped on 16 October. _Markdown version of https://gemini-notebook-hub.online/guides/reflection-ai-beam-explained — for AI and LLM crawlers. Content is identical to the web page._ --- _12 min read · updated 18 Oct 2026_ Reflection AI, an Nvidia- and Sequoia-backed startup founded by two former Google DeepMind researchers, announced its first open-weight model, Beam, on 5 October 2026, with only a waitlist to show for it. Most of the coverage still online was written in that gap, when "open-weight" meant a promise rather than a download. Reflection actually shipped the weights, a safety report and API access from 40-plus partners on 16 October. Here's what Beam is, how it benchmarks against the Chinese open models it's positioned against, and whether the gap between announcement and release changes how much to trust the rest of the claims. --- > **Quick answer** > **Beam is a 501-billion-parameter sparse mixture-of-experts model (23 billion active per token)** from Reflection AI, announced 5 October 2026 and released as open weights under **Apache 2.0** on 16 October. Weights are live on Hugging Face in BF16, FP8 and NVFP4, with a first-party API in public beta and 40-plus launch partners including AWS, Google Cloud, Oracle, Cloudflare and OpenRouter. > **Independently, it scores 30 on Artificial Analysis's Intelligence Index** (median for comparable models: 19), ranked 19th of 120 models tracked at the time of writing. It's fast (227.9 output tokens/second, 2nd of 77 models measured) and relatively cheap to run per task, but it is not the strongest open-weight model available: Moonshot's Kimi K3 beats it on most reasoning and agentic benchmarks, and Reflection's own published table admits as much. > **It needs serious hardware.** At BF16 the weights alone are roughly 1TB; even at FP8 you need something like a single 8xH200 node. There's no realistic way to run this on a laptop or a single consumer GPU. > **It is not affiliated with the 2024 "Reflection 70B" controversy**, where a different company's hosted "70B" model turned out to be secretly routing requests to Claude. Reflection AI (2024, founded by Misha Laskin and Ioannis Antonoglou) is an unrelated company, a mix-up that comes up often enough in developer forums to be worth stating plainly. ![Artificial Analysis's benchmark page for Beam 1.0, showing an Intelligence Index score of 30 ranked 19th of 120 models, 227.9 output tokens per second ranked 2nd of 77, a cost of $0.57 per Intelligence Index task, and a 1 million token context window.](/blog/reflection-beam-aa-benchmarks.png) _Artificial Analysis's independent benchmark page for Beam 1.0. artificialanalysis.ai, October 2026._ ## What Beam actually is Beam is a sparse mixture-of-experts model: 501 billion total parameters, but only 23 billion of them activate for any given token, spread across 52 layers with interleaved local and global attention and aux-loss-free load balancing between experts. It's text-only, in and out: no image generation, and it can only reason about visual content if that content is first converted to text by something else. [Reflection's own announcement](https://reflection.ai/blog/introducing-beam) describes it as aimed squarely at coding, reasoning and agentic work, not general chat. The pretraining data is the part Reflection spends the most words on. The company says it started from the usual raw internet crawl and discarded roughly 95% of it through curation, landing on about 1.8 trillion tokens of what it calls genuinely high-quality material that conventional filters would have missed, folded into a total pretraining run of 23.8 trillion tokens. The context window is 1 million tokens, but that number is reached through a mid-training extension step; the reinforcement-learning phase that follows is itself capped at 256,000 tokens, a distinction worth knowing if you're evaluating Beam specifically for very-long-context agentic tasks rather than just long single documents. ## The training run, in numbers most model announcements don't publish Reflection is unusually specific about how Beam was built, and the two training phases used different hardware. Pretraining ran on **6,144 Nvidia GB300 NVL72 GPUs for under four weeks**, reaching 92.3% goodput (the share of GPU time actually producing useful training progress) late in the run, with 9 semi-automatic rewinds triggered by gradient spikes or suspected silent data corruption. Reinforcement learning then ran separately on **10,500 GB300 GPUs over four weeks**, generating more than 100 million rollouts, with roughly 80 million of those directed specifically at a "reasoning expert" subcomponent. At peak, the RL run sustained around 110,000 average concurrent rollouts and up to 170,000 concurrent sandboxes, 1.3 billion total sandbox invocations against roughly 1 million mostly-synthetic RL environments. Weight updates reportedly reached the live inference fleet in a median of about 12 seconds, and the run absorbed 71 inference incidents without halting training, recovering in a median of 8 minutes each time. Those two GPU counts, 6,144 for pretraining and 10,500 for RL, show up in different third-party articles as if they contradict each other. They don't: one is the pretraining cluster, the other is the much larger reinforcement-learning cluster that ran afterward, and conflating them is a mistake worth watching for if you're cross-checking other coverage of this launch. ## Announced 5 October, actually released 16 October This is the detail most existing coverage of Beam gets stale on, because most of it was written in the gap. Reflection's [5 October announcement](https://reflection.ai/blog/introducing-beam) opened early access through a waitlist only, with the company saying weights, a technical report and developer tools would follow "in the coming weeks" while the model finished what it described as final security checks. Eleven days later, [Reflection published a second post, "Beam is now available," dated 16 October 2026](https://reflection.ai/blog/beam-is-available), confirming the full release: open weights on Hugging Face (BF16, FP8 and NVFP4 formats, plus a separate speculative-decoding drafter model), a confirmed **Apache 2.0 license**, a first-party API at platform.reflection.ai in public beta described as "free to start," and a safety technical report covering a multi-teacher on-policy distillation approach to safety training. The release also arrived with more than 40 launch partners already wired up: cloud platforms (AWS, Google Cloud, Microsoft Foundry, Oracle, Cloudflare, CoreWeave, Databricks), inference marketplaces (Together AI, OpenRouter, Baseten, Fireworks, Nebius, Modal), Nvidia's own NIM microservices, local-inference tools (Ollama, LM Studio), on-prem and sovereign/air-gapped deployment partners (Dell, HPE), and day-zero support in the open-source serving stacks developers actually use (Transformers, vLLM, SGLang, Unsloth, Axolotl). Vercel ran it free on its AI Gateway for the launch window. That's a genuinely broad distribution push for a two-week-old company announcement, and it's the single biggest reason to treat the 16 October release, not the 5 October teaser, as the real launch date when you're citing this model elsewhere. ## How it benchmarks, independently and by Reflection's own table Artificial Analysis, which runs every model through the same standardized Intelligence Index rather than taking a vendor's self-reported score, puts Beam at **30**, against a category median of 19, placing it 19th of 120 models tracked. That's a solid above-median result. It's also notably fast (227.9 output tokens per second, 2nd of 77 models measured) and relatively concise (79 million tokens to run the full Index, versus a 130-million-token median), which keeps its per-task cost down to $0.57 despite per-token pricing ($0.70 input, $2.10 output per million tokens) that Artificial Analysis itself flags as "somewhat expensive" against the field. ``` Benchmark (Reflection's own table, Beam GLM-5.2 Kimi K3 Qwen 3.8 DeepSeek updated 8 Oct 2026) Max V4.1 Flash ----------------------------------------------------------------------------------------- SWE-Bench Pro v1 65.5 62.1 NR 67.7 NR Terminal-Bench v2.1 80.1 81.0 88.3 86.6 90.6 DeepSWE v1.1 44.4 44.0 68.0 51.0 74.2 GPQA Diamond 90.5 91.2 93.5 92.6 90.9 tau3 banking (agentic tool use) 38.0 37.1 37.1 55.2 NR BrowseComp 77.4 NR 91.2 NR NR HLE, no tools 36.2 40.5 46.9 43.6 39.1 Figures are Reflection AI's own published comparison table. NR means Reflection didn't report that model on that benchmark. SWE-bench Verified (Beam: 80.9) is omitted here because Reflection's table only compares it against two other models on that specific benchmark, not this set. ``` Read that table the way Reflection itself frames it, because the company is candid about where Beam loses. Its headline claim is that Beam performs roughly on par with Z.ai's **GLM-5.2** on advanced reasoning at three to four times less inference compute, a figure Reflection labels explicitly as an estimate (computed as 2 × active parameters × mean generated tokens, using third-party token counts), not a measured dollar cost. The comparison is also against GLM-5.2, not Z.ai's newer GLM-5.3, which scores higher than Beam across nearly every row in the table above. And Reflection states directly, without hedging, that **Moonshot's Kimi K3 leads on raw capability**: Kimi K3 is a 2.8-trillion-parameter natively multimodal model with an Artificial Analysis Intelligence Index around 57, roughly level with Claude Opus 5.5 and well ahead of Beam's 30. Beam's pitch is efficiency and US provenance, not winning head-to-head on capability. One number worth flagging before you trust Beam with anything factual: on Artificial Analysis's AA-Omniscience benchmark, which measures how often a model states something false with confidence, Beam's hallucination rate runs around 47%, against a roughly 33% correct-answer rate on the same test. That's a meaningfully worse ratio than you'd want for research or citation-heavy work, and it's a good argument for pairing Beam with retrieval or source-grounding (the kind of thing [Gemini Notebook does natively](/guides/notebooklm-vs-chatgpt)) rather than trusting its unaided factual claims. ## Is Beam really the top "Western" open-weight model? Not quite Several launch-week articles framed Beam as the leading US-built answer to Chinese open-weight labs, without checking what else had shipped in the same window. On Artificial Analysis's own Intelligence Index, **Meta's Muse Spark Open**, an 816-billion-parameter open-weight model (42 billion active) released under Meta's own Muse Model License, scores **39**, ahead of Beam's 30. That makes Beam the second-place US-origin open-weight model by this measure, not the first. (Muse Spark Open is a different model from [Meta's Muse Wonder](/guides/meta-muse-wonder-explained), a closed frontier model Meta shipped the same month; the two are easy to conflate because of the shared "Muse" branding, but one is open-weight and one isn't.) It's a small correction, but it's the kind of thing worth getting right if you're choosing a model specifically because of its origin rather than its benchmark scores. ## Can you actually run this yourself Not on a laptop, and not on a single consumer GPU. Because Beam is a mixture-of-experts model, only 23 billion of its 501 billion parameters activate per token, which keeps inference compute down, but all 501 billion parameters still need to sit in memory regardless of which experts are active for any given request. At full BF16 precision that's roughly 1TB of weight memory; FP8 or INT8 brings it down to roughly 500-600GB; the most aggressive NVFP4/INT4 quantization gets you to somewhere around 250-360GB. The practical floor for self-hosting is a single 8xH200 (141GB per GPU) node running FP8, or two 8xH100 (80GB) nodes split with tensor and pipeline parallelism. This is a datacenter-tier model, not a hobbyist one, which is worth knowing before you go looking for a way to run it at home. If you just want to use it rather than host it, pricing varies noticeably by provider: OpenRouter lists it around $0.25 input / $1.00 output per million tokens, while Artificial Analysis's own measured median across providers is closer to $0.70/$2.10. Treat any single number here as a snapshot rather than a fixed price, check the live rate on whichever provider you're actually using, since Beam launched with more than 40 hosting partners and they aren't all pricing it the same. ## What developers are actually saying The Hacker News thread on Beam's original announcement (556 points, 173 comments) is a useful read of the room, and it ran almost entirely before the 16 October release. The most-upvoted substantive criticism compared Beam directly to DeepSeek's V4.1 Flash: one commenter wrote that Beam "appears to be larger than DeepSeek v4.1 Flash, more expensive to run, and worse on every measured metric," a claim the benchmark table above partially bears out on agentic and coding tasks specifically. A recurring theme, unsurprising for an announcement with no weights attached yet, was variations on "talk is cheap, show me the weights," alongside pointed pushback that a company shouldn't market itself as open while gating the actual release. Other threads covered genuinely useful practical ground: estimates that running Beam locally at 4-bit quantization would need something like two 128GB unified-memory machines, and notes that Beam has no vision support where some competitors do. A separate, recurring strand of the discussion was skepticism about the "Western alternative to Chinese labs" framing itself, with some commenters arguing national origin was doing more work in the pitch than the benchmark numbers were. On r/LocalLLaMA, moderators reportedly pulled the initial 5 October announcement thread for violating the subreddit's no-vaporware rule, since no weights were public yet. Once benchmarks and, later, the actual weights landed, the more common framing there was that Beam sits roughly alongside GLM-5.2, with the main draw for serious users being data sovereignty and non-Chinese provenance for deployments where that specifically matters, rather than Beam being the strongest available option on pure capability. ## Should you use it - **You need a non-Chinese, genuinely open-weight model for a sovereignty-sensitive deployment** (government, regulated enterprise, air-gapped environment): a strong fit, and arguably Beam's clearest differentiator, with Dell and HPE already set up as on-prem partners. - **You're choosing an open-weight model purely on capability**: a weaker fit. Kimi K3 leads on raw reasoning and agentic benchmarks by Reflection's own published numbers, and Meta's Muse Spark Open scores higher on Artificial Analysis's Intelligence Index. - **You want to self-host on your own hardware**: only realistic with datacenter-class GPUs (minimum roughly a single 8xH200 node); not an option on consumer hardware. - **You need factual reliability for research or citation-heavy work**: a weak fit on its own. The roughly 47% hallucination rate on AA-Omniscience is a real liability; pair it with retrieval or source-grounding rather than trusting unaided answers. - **You want fast, relatively cheap agentic coding runs and can tolerate it trailing the absolute top tier**: a reasonable fit, given its 2nd-of-77 output speed and below-median cost per Intelligence Index task. ## People also ask ### Is Reflection AI's Beam actually open source? It's open-weight, not fully open-source. The weights, tokenizer and a reference implementation are public under Apache 2.0, confirmed by the company at the 16 October 2026 release. The training data and full training code are not released, which is the standard distinction between "open-weight" and "open-source" for large models. ### Can I run Beam on my own computer? Not on a consumer GPU or a laptop. Even at the most aggressive quantization (NVFP4/INT4), the full model needs roughly 250-360GB of memory because all 501 billion parameters must stay resident regardless of how many activate per token. The realistic minimum is a single 8xH200 datacenter node. ### Is Beam better than DeepSeek, Qwen, or Kimi K3? Not on raw capability. Reflection's own published benchmark table has Kimi K3 and DeepSeek V4.1 Flash beating Beam on most agentic and coding benchmarks (Terminal-Bench, DeepSWE, BrowseComp). Beam's pitch is inference efficiency and US/non-Chinese provenance, not winning head-to-head on scores. ### Is this the same "Reflection" that had the fake-benchmark scandal in 2024? No. That was a different company, Matt Shumer's HyperWrite, whose hosted "Reflection 70B" model turned out to be routing requests to Claude rather than running the claimed model. Reflection AI, founded in 2024 by former Google DeepMind researchers Misha Laskin and Ioannis Antonoglou and backed by Nvidia, Sequoia and Lightspeed, is an unrelated company. ### When were Beam's weights actually released? Reflection announced Beam on 5 October 2026 with early access via waitlist only. Full open weights, the technical and safety reports, and partner integrations went live on 16 October 2026, nearly two weeks later, a gap much of the launch-week coverage predates. ### Is Beam the best open-weight model made outside China? Not by Artificial Analysis's own Intelligence Index. Meta's Muse Spark Open, an 816-billion-parameter open-weight model released under Meta's own license, scores 39 against Beam's 30 on the same independent benchmark, making Beam the second-ranked US-origin open-weight model rather than the first.