Artificial Analysis: Beam scores 30 on the Intelligence Index, #2 among US open-weight models
Artificial Analysis @ArtificialAnlysX
Why it matters
First independent benchmark of Beam (~52k views): index 30, 26k output tokens per task, 47% hallucination rate.
Summary
Posted Oct 16 13:31 UTC by Artificial Analysis (@ArtificialAnlys); 51,579 views, 513 likes, 47 reposts when read via api.fxtwitter.com on 2026-10-17.
Archived text
Reflection’s first model, Beam, scores 30 on the Artificial Analysis Intelligence Index, placing it #2 among US open weights models
Beam is an open weights model with 501B total and 23B active parameters, released on @huggingface
Congratulations to @reflection_ai on the release!
Key takeaways:
➤ Second among US open weights models: Beam scores 30, behind only Meta’s Muse Spark Open (39) and ahead of yesterday’s NVIDIA Nemotron 3.5 Super VL (26) release. Outside China, only Muse Spark Open and the UAE’s K2 Horizon (31) score higher
➤ Token efficient for its intelligence: Beam uses ~26k output tokens per Intelligence Index task compared to K2 Horizon, one point ahead, which uses twice as many (~52k). Proprietary models still set the frontier, with GPT-6 Astra (max) scoring 53 on the same budget (~27k)
➤ Strong on agentic workflows, weaker in the terminal: Beam scores 48.5% on AutomationBench-AA, 11 percentage points ahead of K2 Horizon (37.2%) and level with Qwen3.8 27B (48.2%). Terminal-Bench 4.0 is a weak point, where Beam scores 3.0%, behind Qwen3.8 27B (5.6%) and Muse Spark Open (8.1%)
➤ Knows more, guesses more: On AA-Omniscience it answers 33% of questions correctly, well ahead of K2 Horizon (18%) and Qwen3.8 27B (16%). But its hallucination rate is 47%, against 26% for K2 Horizon and 30% for Qwen3.8 27B, giving Beam a net index score of 1.5, ahead of both peers (−3.0 and −10.0)
Other model details Context window: 1M tokens Modalities: Text Parameters: 501B / 23B
