Reflection Releases Its First Model Beam, Scoring 30 on the Artificial Analysis Intelligence Index
Reflection has released its first model, Beam, an open-weight model with 501B total parameters and 23B activated parameters. It scores 30 on the Artificial Analysis Intelligence Index, ranking second among US open-weight models, behind only Meta's Muse Spark Open (39) and ahead of NVIDIA's Nemotron 3.5 Super VL (26). Beam consumes about 26k output tokens per Intelligence Index task, of which about 19k are used for reasoning and 7k for answering; it scores 48.5% on AutomationBench-AA, but only 3.0% on Terminal-Bench 4.0. On AA-Omniscience, it answers 33% of questions correctly, with a hallucination rate of 47% and a net index score of 1.5.
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SelectedAI score 60
Third-party evaluations place Beam among open-source models in terms of ranking, token efficiency, and hallucination rate, which can be used to judge the boundaries of its capabilities.
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October 16, 2026Reflection’s first model, Beam, scores 30 on the Artificial Analysis Intelligence Index, placing it #2 among US open weights modelsSee model page See model page
Beam is an open weights model with 501B total and 23B active parameters
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)
Compared to open weights peers
Beam scores highly against US open weights models, trailing Meta's Muse Spark Open (39) and ahead of NVIDIA's Nemotron 3.5 Super VL (26), all released this week. Among all open weights models it ranks tenth, one point behind K2 Horizon (31)
Cyber capabilities
Beam completes 34% of tasks on the Artificial Analysis Cyber Index, making it the leading US open weights model that we’ve tested
Driven by its token efficiency, it also sits on the Cyber Index vs. Output Tokens per task frontier, alongside GPT-6 Sol (Daybreak Blue)
Highly token efficient
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
Coding skills
Running in Mirror, Reflection's open-source CLI harness, Beam scores 28 on the Artificial Analysis Coding Agent Index. It sits 15 points behind the next setup, Claude Code with Qwen3.8 Max (43), and 40 behind the leaders (68). It is, however, frugal: only Codex with GPT-6 Astra and Codex with GPT-6.1 Sol use fewer tokens
Intelligence Index Components
Across the ten evaluations in the Intelligence Index, Beam stands out on AutomationBench-AA (48.5%) and CritPt (13.1%), where it more than doubles K2 Horizon (4.6%) and Qwen3.8 27B (5.4%). Its weakest results are on Terminal-Bench 4.0 (3.0%) and AA-Briefcase (976 Elo)
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