# Beam

Reflection released Beam, its first open-weight model, together with the safety training methods and evaluation tools behind it. Public safety evaluations had not covered enough ground to test whether safety boundaries hold in adversarial and tool-use settings. Beam combines pretraining data filtering with dedicated safety alignment training, merging a capability teacher and a safety teacher through multi-teacher on-policy distillation. On 1,500 human-written conversations in the Iris safety evaluations, Beam declined 89% of harmful requests and complied with 82% of safety-related harmless requests.

Reflection released Beam, its first open-weight model, together with the safety training methods and evaluation tools behind it. Public safety evaluations had not covered enough ground to test whether safety boundaries hold in adversarial and tool-use settings. Beam combines pretraining data filtering with dedicated safety alignment training, merging a capability teacher and a safety teacher through multi-teacher on-policy distillation. On 1,500 human-written conversations in the Iris safety evaluations, Beam declined 89% of harmful requests and complied with 82% of safety-related harmless requests.

Beam, Reflection's 501B-parameter mixture-of-experts model with 23B active parameters per token, targets software engineering and long-horizon agentic tasks and is planned for Apache 2.0 release. SGLang now supports Beam natively and powers its production API, turning inference efficiency into serving performance through BF16, NVFP4 and dynamic FP8 checkpoints, DFlash2 speculative decoding and prefix caching. On an OpenHands multi-turn agentic coding replay with the NVFP4 checkpoint on four NVIDIA GB300 GPUs, SGLang reaches 127.6K total tokens/sec per GPU at 185.87 tokens/sec per user (p50) with concurrency 32.

Beam, Reflection's 501B-parameter mixture-of-experts model with 23B active parameters per token, targets software engineering and long-horizon agentic tasks and is planned for Apache 2.0 release. SGLang now supports Beam natively and powers its production API, turning inference efficiency into serving performance through BF16, NVFP4 and dynamic FP8 checkpoints, DFlash2 speculative decoding and prefix caching. On an OpenHands multi-turn agentic coding replay with the NVFP4 checkpoint on four NVIDIA GB300 GPUs, SGLang reaches 127.6K total tokens/sec per GPU at 185.87 tokens/sec per user (p50) with concurrency 32.

Reflection AI's Beam reasoning model has left stealth testing under the codename stealth/cat-jam and is now available on AI Gateway under its own name, free for a limited launch period when served by Reflection AI. It targets coding and agentic tasks, ships weights under Apache 2.0, supports tool calling and structured outputs, and carries a 1M-token context window. The model has 501B total parameters but activates only 23B per token and skips tokens a task does not need, so it tends to answer sooner than larger open models.

Reflection AI's Beam reasoning model has left stealth testing under the codename stealth/cat-jam and is now available on AI Gateway under its own name, free for a limited launch period when served by Reflection AI. It targets coding and agentic tasks, ships weights under Apache 2.0, supports tool calling and structured outputs, and carries a 1M-token context window. The model has 501B total parameters but activates only 23B per token and skips tokens a task does not need, so it tends to answer sooner than larger open models.

Reflection, a new US lab, released Beam, its first open model: 501B sparse mixture-of-experts weights under Apache 2.0, with about 23B parameters active per token, a 1M-token context, and a focus on coding and agentic work. Hybrid attention keeps only 9 of 52 layers global while 43 hold a fixed 512-token window, so the KV cache grows about 36 KiB per token, roughly one-sixth of the all-global rate. BF16, FP8 and NVFP4 checkpoints ship alongside day-0 transformers, SGLang and vLLM support and a Rust-based harness.

Reflection, a new US lab, released Beam, its first open model: 501B sparse mixture-of-experts weights under Apache 2.0, with about 23B parameters active per token, a 1M-token context, and a focus on coding and agentic work. Hybrid attention keeps only 9 of 52 layers global while 43 hold a fixed 512-token window, so the KV cache grows about 36 KiB per token, roughly one-sixth of the all-global rate. BF16, FP8 and NVFP4 checkpoints ship alongside day-0 transformers, SGLang and vLLM support and a Rust-based harness.

Reflection's Beam is a 501B-parameter mixture-of-experts model with 23B active parameters per token, and vLLM supports it from day one with reasoning, tool calling and structured outputs working out of the box. Its first implementation was almost pure PyTorch; coding agents profiled the model, wrote kernels and benchmarked each change, cutting batch-1 decode on TP8 from 8.95 to 3.30 milliseconds per step and an 8×2K prefill from 328 to 176 milliseconds. With its DFlash2 draft head, a single request reaches 708 tokens per second on TP8 at an acceptance length of 3.7.

Reflection's Beam is a 501B-parameter mixture-of-experts model with 23B active parameters per token, and vLLM supports it from day one with reasoning, tool calling and structured outputs working out of the box. Its first implementation was almost pure PyTorch; coding agents profiled the model, wrote kernels and benchmarked each change, cutting batch-1 decode on TP8 from 8.95 to 3.30 milliseconds per step and an 8×2K prefill from 328 to 176 milliseconds. With its DFlash2 draft head, a single request reaches 708 tokens per second on TP8 at an acceptance length of 3.7.

Reflection released Beam, its first open-weight model, with day-0 support on the Baseten platform. Beam is an autoregressive transformer with a mixture-of-experts architecture: 501B total parameters, 23B active per token, and a context window up to 1 million tokens, aimed at coding and agentic workloads. Training leaned on reinforcement learning, using longer multi-step rollouts so the model can practice tool use and adjust from feedback, plus a length penalty against unnecessary tokens, with the goal of cutting the cost of these workloads.

Reflection released Beam, its first open-weight model, with day-0 support on the Baseten platform. Beam is an autoregressive transformer with a mixture-of-experts architecture: 501B total parameters, 23B active per token, and a context window up to 1 million tokens, aimed at coding and agentic workloads. Training leaned on reinforcement learning, using longer multi-step rollouts so the model can practice tool use and adjust from feedback, plus a length penalty against unnecessary tokens, with the goal of cutting the cost of these workloads.

Reflection released Beam, its first model, an open weights system with 501B total and 23B active parameters that scores 30 on the Artificial Analysis Intelligence Index, second among US open weights models behind Meta's Muse Spark Open (39) and ahead of NVIDIA's Nemotron 3.5 Super VL (26). It uses about 26k output tokens per Intelligence Index task, roughly half of K2 Horizon (31), while proprietary models still set the frontier, with GPT-6 Astra (max) scoring 53 on the same budget.

Reflection released Beam, its first model, an open weights system with 501B total and 23B active parameters that scores 30 on the Artificial Analysis Intelligence Index, second among US open weights models behind Meta's Muse Spark Open (39) and ahead of NVIDIA's Nemotron 3.5 Super VL (26). It uses about 26k output tokens per Intelligence Index task, roughly half of K2 Horizon (31), while proprietary models still set the frontier, with GPT-6 Astra (max) scoring 53 on the same budget.