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TL;DR
In 2026, all leading AI models are now mixture-of-experts, enabling trillion-parameter models to operate efficiently. This shift separates total parameters from active compute, reducing costs and increasing scalability.
All leading frontier AI models in 2026 now utilize a mixture-of-experts (MoE) architecture, a development that allows models with trillions of parameters to operate efficiently. This shift is crucial for scaling AI capabilities without incurring prohibitive costs, making large-scale models more accessible and practical.
Traditional dense models use every parameter for each token processed, leading to costs that grow linearly with model size. For example, a 70-billion-parameter dense model requires roughly 70 billion computations per token. As models grow larger, this becomes economically unsustainable, prompting the industry to adopt mixture-of-experts (MoE) architectures.
MoE models split their capacity into many sub-networks called experts. During inference, a small, fast routing component selects only a handful of these experts to activate per token, drastically reducing per-token computation. For instance, Kimi K3’s 2.8 trillion parameters are all stored in memory, but only about 104 billion are active during processing, enabling large models to operate at speeds comparable to much smaller dense models.
This architecture decouples total model size from active compute cost, allowing models to expand knowledge without proportional increases in operational expenses. The router learns during training which experts to activate, creating emergent specialization that enhances performance across diverse tasks. This highlights the importance of robust control mechanisms in advanced AI models.
Every serious open model this year quotes two parameter counts instead of one — a huge total and a much smaller active. That split is the single highest-leverage concept for reasoning about how these models run, what they cost, and why they behave as they do.
Instead of one monolith where everything activates for every token, an MoE splits its capacity into many parallel experts. For each token, a small fast router selects only a handful to run. The rest stay dormant.
A dense model welds capability and running-cost together — every added parameter is paid for on every token, forever. MoE breaks the lockstep.
- All 70B run to answer “capital of France?”
- Capability and cost rise in lockstep
- Past a few hundred billion, the per-token bill becomes absurd
- Breadth of a giant model at the per-token compute of a small one
- Grow capability by adding experts, not per-token cost
- The only way trillion-parameter open models are serveable at all
This is the source of the most common expensive mistake in local inference. The two counts are paid to two different pieces of hardware.
The same model, seen through the two-number lens: what fits, and how fast.
Read those two numbers as two questions and you can predict a model before downloading it.
Implications of Mixture-of-Experts for Large-Scale AI
This development transforms how large AI models are built and deployed. By separating total parameter count from active computation, MoE models enable the deployment of trillion-parameter models that are both cost-effective and fast enough for practical use. This shift makes advanced AI capabilities more accessible, fostering broader adoption and innovation. However, it also introduces new complexities in hardware provisioning and model management, as organizations must now consider both total memory requirements and active compute capacity.
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Evolution from Dense Models to Mixture-of-Experts
Prior to 2026, AI models primarily relied on dense architectures, where all parameters were active for each token, leading to rapidly escalating costs at larger scales. As models surpassed a few hundred billion parameters, maintaining performance became prohibitively expensive. The industry responded by developing mixture-of-experts architectures, which distribute parameters across many sub-networks and activate only a subset during inference. This approach gained prominence as it allowed models to scale to trillions of parameters without proportional increases in compute and memory costs.
The adoption of MoE was driven by the need to balance knowledge breadth with operational efficiency, enabling models like Kimi K3 and DeepSeek to deliver advanced capabilities at manageable costs. This transition marks a fundamental shift in AI model design, emphasizing efficiency and scalability.
"The reason every serious open model in 2026 quotes two parameter counts instead of one is that they are all now mixtures of experts, and understanding what that means is the key to reasoning about how these models run and why they behave the way they do."
— Thorsten Meyer
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Unresolved Questions About MoE Scalability and Management
While the benefits of MoE architectures are clear, several questions remain. It is not yet fully understood how to best manage expert specialization to prevent issues like expert collapse or imbalance. Additionally, the impact of MoE on model interpretability and robustness continues to be studied, and the long-term hardware implications are still evolving.
Further research is needed to optimize expert routing and training stability, as well as to develop standards for hardware provisioning that can handle the unique demands of MoE models at scale.
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Future Developments in Mixture-of-Experts AI Models
In the coming months, expect continued refinement of MoE architectures, including improved routing algorithms and training techniques. Hardware providers are also likely to develop specialized accelerators optimized for MoE models, reducing costs further. Researchers will focus on addressing current limitations around expert balance and interpretability, aiming to make these models more reliable and easier to manage at scale.
Additionally, more open models adopting MoE are expected to emerge, expanding access to large-scale AI capabilities for a broader range of organizations and applications.
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Key Questions
Why are all top AI models now based on mixture-of-experts?
Because MoE architectures allow models to have trillions of parameters while keeping per-token compute costs manageable, enabling large-scale knowledge without prohibitive expenses.
How does the mixture-of-experts approach reduce costs?
It activates only a small subset of the total experts per token, meaning most parameters remain dormant during inference, significantly lowering compute and memory requirements per operation.
What are the main challenges of MoE models?
Managing expert balance, preventing collapse, improving interpretability, and optimizing hardware compatibility are ongoing challenges for MoE deployment.
Will MoE replace dense models entirely?
While MoE offers significant advantages at scale, dense models remain useful for smaller tasks and simpler applications; the two approaches are likely to coexist for some time.
Source: ThorstenMeyerAI.com
