Reflection @reflection_ai

Reflection @reflection_ai 's Beam, day 0 on TokenSpeed. 8× NVIDIA GB200 · NVFP4 + DFlash 2 🔹 463 tok/s for one user 🔹 223 tok/s/user at 8 concurrent users Measured on multi-turn agentic coding workloads. Full 1M-token context supported. 📖 lightseek.org/blog/tokenspee… ⭐️ github.com/lightseekorg/t…

Run Reflection's Beam at scale in your own account for full sovereignty but without managing infra with Ray Serve LLM on Anyscale!

Jeffrey Wang @jeffreyycwang

a day ago

Excited to announce Day 0 support for Reflection's Beam model on Ray Serve LLM! Beam is Reflection's first open-weight model, advancing the frontier of Western AI in the 500B-parameter class. Built as a sparse MoE model, Beam is optimized for coding, agentic workloads, and

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Beam from @reflection_ai is free on AI Gateway for a limited time. Until today it ran in stealth as 𝚜𝚝𝚎𝚊𝚕𝚝𝚑/𝚌𝚊𝚝-𝚓𝚊𝚖. It's a low-latency US open-weight model. 𝚛𝚎𝚏𝚕𝚎𝚌𝚝𝚒𝚘𝚗/𝚋𝚎𝚊𝚖-𝚏𝚛𝚎𝚎 vercel.com/changelog/refl…

@ClementDelangue 👋👋

Reflection AI’s Beam 1.0 is out today, a 501B MoE with 23B active under Apache 2.0. We ran a brief RL post-training run on it in AC2 that tripled its score on a challenging long-horizon agentic coding task in 40 steps, 0.07 to 0.215. We’re offering day 0 support in AC2 for inference and post-training.

Reflection @reflection_ai

a day ago

Beam is now available across platforms, with open weights under the Apache 2.0 license - in collaboration with an ecosystem of 40+ partners. We're releasing: - Weights, tokenizer, and reference implementation - BF16, FP8 and NVFP4 weights and a DFlash2 drafter - The open source

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America’s next top model, Reflection’s Beam, is now live on Together AI. Trained on Together GPU Clusters, Beam is Reflection’s first open-weight model. • ~501B-parameter MoE, 23B active • Built for coding + long-horizon agentic work • 1M-token context window • Released under an Apache 2.0 release Try Beam now together.ai/models/reflect…

Congrats to @reflection_ai on the Beam launch. Excited to see what builders create 🫡

Reflection @reflection_ai

a day ago

Beam is now available across platforms, with open weights under the Apache 2.0 license - in collaboration with an ecosystem of 40+ partners. We're releasing: - Weights, tokenizer, and reference implementation - BF16, FP8 and NVFP4 weights and a DFlash2 drafter - The open source

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We’re excited to be day 0 partners for @reflection_ai’s first open-weight model! For a limited time, Beam is FREE on Ollama! In partnership with @reflection_ai this model is hosted in the US with full ZDR. It has a 1M token context window and is designed for software engineering, reasoning, and long-running agentic tasks. Run Beam on Ollama’s Cloud: ollama run beam Claude Code ollama launch claude --model beam:cloud Codex ollama launch codex --model beam:cloud OpenCode ollama launch opencode --model beam:cloud Free to use for a limited time only.

Reflection @reflection_ai

a day ago

Beam is now available across platforms, with open weights under the Apache 2.0 license - in collaboration with an ecosystem of 40+ partners. We're releasing: - Weights, tokenizer, and reference implementation - BF16, FP8 and NVFP4 weights and a DFlash2 drafter - The open source

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Reflection Beam is now available on Modal. Beam is a 501B-parameter open-weight MoE (23B active) from @reflection_ai built for coding, agentic workflows, and reasoning. It supports a 1M token context window, with token efficiency a core focus. In early access, the Oura Health Intelligence team found that "Beam had fewer context leaks and was much more grounded in its answers than other frontier models we evaluated.”

🎉 Reflection-Beam-1.0 from @reflection_ai has Day-0 support in vLLM. 501B total parameters, 23B active per token, and a 1M-token context window. Beam combines hybrid attention with Harmony-format reasoning and tool use. Beam also has Day-0 RL support in SkyRL (@NovaSkyAI): LoRA, BF16 full fine-tuning and MXFP8 RL, with vLLM rollouts. We worked with @reflection_ai and @Inferact on the serving path, from fused kernels to DFlash. Here is what is under the hood. 🧵

Reflection @reflection_ai

a day ago

Beam is now available across platforms, with open weights under the Apache 2.0 license - in collaboration with an ecosystem of 40+ partners. We're releasing: - Weights, tokenizer, and reference implementation - BF16, FP8 and NVFP4 weights and a DFlash2 drafter - The open source

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Beam from @reflection_ai is now available on Cloudflare. The newest open-weights US model, designed for token efficiency and agentic workloads. Try it out today. developers.cloudflare.com/ai/models/refl…

SGLang powers Beam’s production API, with day-0 support now available to everyone! Built for software engineering and long-horizon agents, Beam delivers frontier-level inference efficiency with more intelligence per token. Serve it on the same production-proven stack: docs.sglang.io/cookbook/autor… Read more: lmsys.org/blog/2026-10-1…

Reflection @reflection_ai

a day ago

Beam is now available across platforms, with open weights under the Apache 2.0 license - in collaboration with an ecosystem of 40+ partners. We're releasing: - Weights, tokenizer, and reference implementation - BF16, FP8 and NVFP4 weights and a DFlash2 drafter - The open source

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Beam (501B MoE) by Reflection is now live in Bionic! US-hosted and ZDR. Let’s go! 🚀👾

Reflection @reflection_ai

a day ago

Beam is now available across platforms, with open weights under the Apache 2.0 license - in collaboration with an ecosystem of 40+ partners. We're releasing: - Weights, tokenizer, and reference implementation - BF16, FP8 and NVFP4 weights and a DFlash2 drafter - The open source

151 192 896 363K 226

Reflection's Beam is available on Nebius Token Factory from Day 0 Reflection’s first open-weight model is built for coding agents, advanced reasoning, software engineering, and autonomous agentic workflows. Put it to work fixing bugs, refactoring code and using tools. Try it out today: tokenfactory.nebius.com/?modals=endpoi…

We are thrilled to serve @reflection_ai's first open-weight model, Beam, on day 0. At 500B parameters, Beam is optimized for the frontier workloads in the enterprise today: writing and maintaining code, executing long-horizon, multi-step tasks with tools. Check it out on Baseten Model APIs: baseten.co/library/beam/

During @reflection_ai’s private preview of Beam, I built a haunted escape room to put its coding and reasoning to the test. I created a game called “Code Your Way Out”, and it's one room with three linked puzzles. Beam has to repair a brass orrery by lining its rings up with a constellation, route the light, then program its escape through a sealed hatch. Beam never sees the room. Each turn, it gets text observations and writes Python; the code runs in a @CoreWeave Sandbox, and the game engine validates every action before the explorer robot moves. Beam was out in about 30 seconds. Super bullish on this model and really excited to see builders get this model in their hands.

In this demo, Beam was prompted to create a 3D game in p5.js where an astronaut is free falling towards earth and must dodge or hit asteroids. Although Beam is not multimodal, its reinforcement learning (RL) training allows it to generalize across domains like gameplay.

Exciting news: Beam-1.0-501B-A23B by @Reflection_AI just landed #12 among open weight models in Agent Arena with -3.67% Net Improvement, and #22 among open in Code Arena: WebDev with 1438 pts. It’s also fully open under Apache 2.0. On the Agent Arena leaderboard, Beam 1.0 is the top-ranked US model among open models, landing at #12. That puts it ahead of Hy3, MiMo V2.5 Pro and MiniMax M3. Its margin of error is wide (±3.84% on Net Improvement), so it’s statistically closer to the two Qwen3.8 models just above it than its rank suggests. On Code Arena: WebDev, 24 of the top 25 open models come from Chinese labs. The one exception is Reflection’s Beam 1.0 (Apache 2.0). It ranks #22 among open models, which makes it the top US open model on the board, just ahead of Kimi K2.5 Thinking and GLM-5. Congrats to the @Reflection_AI team on this solid open weights release!

Reflection @reflection_ai

2 weeks ago

Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active. - Frontier reasoning efficiency - Advances the Western open frontier on coding & agentic tasks - Trained end-to-end from scratch Full weights release this month. Learn more about

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A 501B open model just landed on the Hub, and you can own it! Beam by @reflection_ai: 501B MoE, 23B active 1M token context Built for coding + agents Token Efficient Apache 2.0 Day-0 in transformers, vLLM, SGLang. We also kicked the tyres hard on this, with shipped SFT + RL recipes 🧵

Beam uses ~26k output tokens per Intelligence Index task: ~19k for reasoning and ~7k for answers. Only 26% of its output goes to final answers, the 28th-lowest share of 219 models tested on Intelligence Index v4.3