MoE Routing Strategies Explained: Top-K, Expert-Choice, Sigmoid Gating and Dynamic Routing (2026 Guide)

Every Mixture-of-Experts (MoE) model repeats one decision for every token: which experts should process it. That decision belongs to the router. MoE routing strategies shape quality, load balance and serving cost in every major open model. This guide explains the 7 routing strategies used in production, and the models that use each one. TL;DR Size: Routers choose from 8 experts (Mixtral) to 384 (DeepSeek-V4-Pro), activating 1 to 8 per token. Runs on: Routing cost shows up in multi-GPU expert parallelism, where each choice becomes all-to-all network traffic. Performance: Sigmoid scoring with aux-loss-free bias balancing is the dominant 2026 recipe for frontier open MoE models. Best: Loss-free balancing cut MaxVio from 0.72 to 0.04 at 1B, with lower perplexity. Worst: Expert choice leaks future tokens in causal models; one test showed a roughly 10% abnormal loss drop. Bottom line: Best thing: bias-based balancing adds no interfering gradients. Worst thing: no router removes the need for serving-time load fixes. What is MoE routing? MoE routing is the mechanism that sends each token to a small subset of expert networks. A router scores every expert, keeps the top candidates and weights their outputs. Only the selected experts run. Mixtral 8x7B , for example, gives each token access to 47B parameters but uses 13B active parameters. The 7 MoE routing strategies used in production 1. Softmax top-k token choice Each token scores all experts with a linear layer and keeps the k highest. A softmax over those k scores sets the mixing weights. Mixtral picks 2 of 8 experts. gpt-oss-120b picks 4 of 128. Qwen3-235B-A22B picks 8 of 128. An auxiliary loss usually keeps expert load even. Used by: Mixtral, gpt-oss, Qwen3. 2. Top-1 switch routing with a capacity factor Switch Transformer showed that routing each token to 1 expert preserves quality and cuts router compute. Each expert gets a fixed buffer: tokens per batch divided by experts, times a capacity factor. Overflow tokens skip the layer through the residual connection. Llama 4 Maverick also sends each token to 1 routed expert, plus a shared expert. Used by: Switch Transformer, Llama 4 Maverick (with a shared expert). 3. Shared plus routed experts DeepSeekMoE splits each layer into always-on shared experts and sparsely routed experts. The shared expert captures common knowledge, so routed experts can specialize. DeepSeek-V3 uses 1 shared and 256 routed experts, activating 8. Kimi K2 , GLM-4.5 and Qwen3.6-35B-A3B follow the same pattern. Qwen3 went the other way and removed shared experts . Used by: DeepSeek-V3 and V4, Kimi K2, GLM-4.5, Qwen3.6, Llama 4. 4. Sigmoid gating with aux-loss-free bias DeepSeek-V3 scores experts with a sigmoid instead of a softmax. It adds a per-expert bias only when choosing the top-k. After each step, the bias drops for overloaded experts and rises for idle ones. Mixing weights still come from the raw scores. GLM-4.5 uses loss-free balance routing with sigmoid gates . DeepSeek-V4 switches the score to sqrtsoftplus but keeps bias balancing. Used by: DeepSeek-V3, DeepSeek-V4, GLM-4.5, Kimi K2. 5. Group-limited and node-limited routing Sending one token to experts on many nodes is expensive. DeepSeek-V3 caps each token at 4 nodes, chosen by their highest affinity scores. Its config splits experts into 8 groups and keeps 4 before the final top-8 pick. DeepSeek says this cap lets training nearly fully overlap computation and communication. Kimi K2 drops grouping entirely, with n_group set to 1. Used by: DeepSeek-V2 and V3. 6. Expert choice routing Google’s expert choice routing flips the direction. Each expert picks its top-k tokens, so every expert gets a fixed bucket size. Tokens can receive a variable number of experts. The paper reports more than 2x faster training convergence. The catch is causality: a token’s expert can depend on later tokens. DeepSeek researchers measured an abnormal loss drop of about 10% from this leakage. Used by: Research and non-causal settings. None of the 10 models in our companion table uses it. 7. Hash routing and dynamic top-k Hash routing assigns experts from the token ID, with no learned router. DeepSeek-V4 uses hash routing in its first 3 MoE layers . Dynamic top-k lets the number of active experts vary per token. SMIDT built it into SGLang and improved P99 time-to-first-token by 1.21 to 1.26x. Used by: DeepSeek-V4 (hash routing), research serving systems (dynamic top-k). MoE routing strategies compared Which MoE routing strategy should you use? Most teams do not pick a router. They inherit one when they choose a checkpoint. Before serving, read 4 fields in config.json: expert count, experts per token, shared experts and topk_method. Changing top-k at inference changes the model itself, so treat it as a quality experiment. For new pretraining runs, sigmoid scoring with bias balancing is the current open default. Key Takeaways Top-k token choice is the baseline; k ranges from 1 (Llama 4) to 8 (DeepSeek-V3, Qwen3). Shared experts are standard in DeepSeek, Kimi, GLM and Qwen3.6; Qwen3 dropped them. Sigmoid plus bias balancing is the 2026 default for frontier open MoE models. Expert choice balances perfectly but leaks future tokens in causal LLMs. DeepSeek-V4 adds hash routing to its first 3 MoE layers. Check out the primary sources: Switch Transformer , Expert Choice , Loss-Free Balancing , DeepSeek-V3 report and the DeepSeek-V4 model card . Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter . Wait! are you on telegram? now you can join us on telegram as well. 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