Use a tiered approach for model selection based on parameter count. Models under 10B are for simple tasks like RAG. The 10-100B range is the sweet spot for agentic systems. Models over 100B parameters are for complex, multi-lingual, enterprise-wide deployments.

China is gaining an efficiency edge in AI by using "distillation"—training smaller, cheaper models from larger ones. This "train the trainer" approach is much faster and challenges the capital-intensive US strategy, highlighting how inefficient and "bloated" current Western foundational models are.

The model uses a Mixture-of-Experts (MoE) architecture with over 200 billion parameters, but only activates a "sparse" 10 billion for any given task. This design provides the knowledge base of a massive model while keeping inference speed and cost comparable to much smaller models.

Classifying a model as "reasoning" based on a chain-of-thought step is no longer useful. With massive differences in token efficiency, a so-called "reasoning" model can be faster and cheaper than a "non-reasoning" one for a given task. The focus is shifting to a continuous spectrum of capability versus overall cost.

Instead of relying solely on massive, expensive, general-purpose LLMs, the trend is toward creating smaller, focused models trained on specific business data. These "niche" models are more cost-effective to run, less likely to hallucinate, and far more effective at performing specific, defined tasks for the enterprise.

Model architecture decisions directly impact inference performance. AI company Zyphra pre-selects target hardware and then chooses model parameters—such as a hidden dimension with many powers of two—to align with how GPUs split up workloads, maximizing efficiency from day one.

Performance on knowledge-intensive benchmarks correlates strongly with an MoE model's total parameter count, not its active parameter count. With leading models like Kimi K2 reportedly using only ~3% active parameters, this suggests there is significant room to increase sparsity and efficiency without degrading factual recall.

The binary distinction between "reasoning" and "non-reasoning" models is becoming obsolete. The more critical metric is now "token efficiency"—a model's ability to use more tokens only when a task's difficulty requires it. This dynamic token usage is a key differentiator for cost and performance.

Artificial Analysis found its knowledge-based "Amnesian's" accuracy benchmark tracks closely with an LLM's total parameter count. By plotting open-weight models on this curve, they can reasonably estimate the size of closed models, suggesting leading frontier models are in the 5-10 trillion parameter range.