@reflection_ai: We just served 1 trillion tokens of Beam in 24 hours. To celebrate, we've reset everyone's limits. Go build something b…
Summary
Reflection AI announced that its Beam inference service served 1 trillion tokens in 24 hours and reset usage limits for all users to celebrate the milestone.
View Cached Full Text
Cached at: 10/18/26, 07:09 AM
We just served 1 trillion tokens of Beam in 24 hours. To celebrate, we’ve reset everyone’s limits.
Go build something big 👉 https://t.co/ry8O6JPlz6 https://t.co/a9eDj2vVjc
Similar Articles
@maximelabonne: Trust me, that's A LOT of tokens
Liquid AI celebrates processing 1 billion requests on Shopify’s platform, highlighting a milestone in their multi-year partnership.
Beam: Reflection's 501B open-weight model
Reflection AI introduced Beam, a 501B-parameter sparse MoE open-weight model (23B active) built for coding, reasoning, and agentic workloads, pretrained on 23.8T tokens and scaled with a high-compute RL run on 10.5K GB300 GPUs. Beam is positioned as advancing the Western open-weight frontier, approaching GLM 5.2 and Qwen 3.8-Max on coding/agentic tasks while using 3–4× less inference compute, with weights and a technical report due later this month.
@woosuk_k: Congrats to @reflection_ai on the release! This time, we let AI agents drive model optimization autonomously, with perf…
Woosuk Kim congratulates Reflection AI on releasing Reflection-Beam-1.0, a 501B-total-parameter (23B active) MoE model with 1M-token context, hybrid attention, and Harmony-format reasoning, highlighting AI agents autonomously driving model optimization with tracked performance gains and Day-0 vLLM/SkyRL support.
@heyshrutimishra: Just Insane Six and a half years ago, the world token leader was an OpenAI employee using 100,000 tokens a month. The w…
Shruti Mishra highlights the explosive growth in AI token usage—from an OpenAI employee using 100k tokens/month six years ago to a worldwide average of 100k/month today—and argues that demand for cheap, high-quality AI intelligence is effectively uncapped.
@huggingface: LIVE STREAM in minutes! Hear from the Reflection team about how they built Beam. Stream: https://youtube.com/watch?v=dG…
Hugging Face hosts a live stream with the Reflection team about how they built Beam, which is being released with open weights under Apache 2.0 in collaboration with 40+ partners, including weights, tokenizer, and reference implementation in BF16, FP8, and NVFP4 formats.