Reflection's Beam model draws an independent token-efficiency verdict
As seen on the 24/7 Wall St. homepage on October 5, 2026.
An independent benchmarker calling Beam one of the most token-efficient open models it has seen matters because token efficiency is what closed-model pricing rests on, and Reflection is releasing full weights this month.
Artificial Analysis has been given access by Reflection and is independently benchmarking Beam Early indicators suggest Beam will be one of the most token-efficient open models we've seen for its level of intelligence. Congratulations @reflection_ai on the announcement! https://t.co/KmipQztsOZ [Quoted @reflection_ai]: 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 https://t.co/1rMABCywUG
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Artificial Analysis, which benchmarks AI models independently, was given direct access to Beam by Reflection and began testing it ahead of the public release. That kind of third-party access before a launch is uncommon, and the early read carries weight precisely because Artificial Analysis has no stake in the outcome.
Beam is a mixture-of-experts style open model with 501 billion total parameters but only 23 billion active at any one time. That gap between total and active parameters is the architectural lever that drives token efficiency: the model can draw on a large pool of specialized capacity without paying the compute cost of running all of it on every token.
Artificial Analysis described Beam as likely to be one of the most token-efficient open models it has seen for its level of intelligence. Token efficiency determines how much useful output a model delivers per unit of compute, which is the same variable that underlies pricing on closed commercial APIs. A highly token-efficient open model with full weights released publicly puts direct pressure on that pricing dynamic.
Reflection says full weights will be released this month, meaning developers and researchers will be able to run, fine-tune, and evaluate Beam themselves in the near term. The independent benchmark from Artificial Analysis gives the community an early baseline to hold that release against once it arrives.