Unverified50% confidenceFactExact time
Beam reaches frontier level on coding and agent tasks while using about 3-4 times fewer inference tokens than comparable open-source models.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/19/2026
First Seen
Valid until: 1/17/2027
Sources
Related Entities
Related Claims
UnverifiedBeam adopts a Sparse Mixture-of-Experts (Sparse MoE) architecture with 52 layers, activating only 23 billion parameters at a time.64% similarUnverifiedIn practice, 4-bit to 8-bit quantized versions perform nearly identically to the original model on most tasks.61% similarUnverifiedBeam was pretrained on 23.8 trillion high-quality tokens.60% similarUnverifiedBeam's VRAM requirement is close to that of a 501B-scale model, while its compute requirement is close to that of a 23B-scale model.60% similarUnverifiedThe core idea of model routing is that not all tasks require the most powerful model; simple tasks can use lightweight models such as GPT-4o-mini, Claude 3 Haiku, or Llama 3.1 8B, potentially at just 1/50 or even 1/100 the cost of flagship models.59% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1070560API
curl https://kongchang.com/api/v1/knowledge/claims/1070560MCP
get_claim(id=1070560)