diff --git a/ACKNOWLEDGMENTS.md b/ACKNOWLEDGMENTS.md
index b452d5b71..1f581106e 100644
--- a/ACKNOWLEDGMENTS.md
+++ b/ACKNOWLEDGMENTS.md
@@ -14,7 +14,7 @@ OpenBMB's `MiniCPM` and `MiniCPM3`, Kyutai's `Helium`, State-Space's `Mamba v1`
 inclusionAI's `Bailing MoE e.g. Ling-family`, `Bailing MoE Linear e.g. Ling-Linear-family`, 
 Klear team - Kuaishou Technology's `Klear`, AI21 Lab's `Jamba` IBM's `Granite MoE`, 
 Meituan's `LongCat`, Nvidia's `Nemotron H`, Swiss-AI's `Apertus`, Nikity's `Lille130m`, 
-Alibaba Qwen's `Qwen3Next`, Tele-AI's `TeleChat3`, and Allenai's `OLMoE` and `Olmo 3`; 
+Alibaba Qwen's `Qwen3Next` and `Qwen3.5 MoE`, Tele-AI's `TeleChat3`, and Allenai's `OLMoE` and `Olmo 3`; 
 Helped add support for the following model architectures: 
 Alibaba Qwen's `Qwen3 & Qwen3MoE)`; Added support for the following training algorithms: 
 `Full Weight Fine-Tuning`, and the `Muon` optimizer; 
@@ -26,4 +26,4 @@ Added support for the following other features:
   MoonshotAI's `Kimi-Linear`, LiquidAI's `LFM2` and `LFM2 MoE`, 
   Google DeepMind's `Gemma 3`, TII's `Falcon H1` and InterLM's `InternLM 2.5`.
 - Ivan Fioravanti: Added support for the following architectures:
- ServiceNow-AI's `Apriel 1.5`, Tencent's `Hunyuan Dense V1` and `Hunyuan MoE V1`.
\ No newline at end of file
+ ServiceNow-AI's `Apriel 1.5`, Tencent's `Hunyuan Dense V1` and `Hunyuan MoE V1`.
diff --git a/mlx_lm/models/qwen3_5_moe.py b/mlx_lm/models/qwen3_5_moe.py
new file mode 100644
index 000000000..8da242d1a
--- /dev/null
+++ b/mlx_lm/models/qwen3_5_moe.py
@@ -0,0 +1,62 @@
+# Copyright © 2025 Apple Inc.
+
+from dataclasses import dataclass
+from typing import Optional
+
+import mlx.core as mx
+import mlx.nn as nn
+from mlx.utils import tree_flatten, tree_unflatten
+
+from . import qwen3_5_moe_text
+from .base import BaseModelArgs
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str
+    text_config: dict
+
+    @classmethod
+    def from_dict(cls, params):
+        if "text_config" not in params:
+            return cls(model_type=params["model_type"], text_config=params)
+        return super().from_dict(params)
+
+
+class Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.model_type = args.model_type
+        self.language_model = qwen3_5_moe_text.Model(
+            qwen3_5_moe_text.ModelArgs.from_dict(args.text_config)
+        )
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+        input_embeddings: Optional[mx.array] = None,
+    ) -> mx.array:
+        return self.language_model(
+            inputs, cache=cache, input_embeddings=input_embeddings
+        )
+
+    def sanitize(self, weights):
+        weights = tree_unflatten(list(weights.items()))
+        weights.pop("vision_tower", None)
+        weights = dict(tree_flatten(weights))
+
+        sanitized = {}
+        for key, value in weights.items():
+            if not key.startswith("language_model."):
+                key = "language_model." + key
+            sanitized[key] = value
+        return sanitized
+
+    @property
+    def layers(self):
+        return self.language_model.model.layers
+
+    def make_cache(self):
+        return self.language_model.make_cache()
diff --git a/mlx_lm/models/qwen3_5_moe_text.py b/mlx_lm/models/qwen3_5_moe_text.py
new file mode 100644
index 000000000..e4793ed3e
--- /dev/null
+++ b/mlx_lm/models/qwen3_5_moe_text.py
@@ -0,0 +1,343 @@
+# Copyright © 2026 Apple Inc.
+
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Union
+
+import mlx.core as mx
+import mlx.nn as nn
+from qwen3_next import (
+    Qwen3NextAttention,
+    Qwen3NextMLP,
+    Qwen3NextRMSNormGated,
+)
+
+from .base import (
+    BaseModelArgs,
+    create_attention_mask,
+    create_ssm_mask,
+)
+from .cache import ArraysCache, KVCache
+from .gated_delta import gated_delta_update
+from .switch_layers import SwitchGLU
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str
+    hidden_size: int
+    num_hidden_layers: int
+    intermediate_size: int
+    num_attention_heads: int
+    linear_num_value_heads: int
+    linear_num_key_heads: int
+    linear_key_head_dim: int
+    linear_value_head_dim: int
+    linear_conv_kernel_dim: int
+    num_experts: int
+    num_experts_per_tok: int
+    decoder_sparse_step: int
+    shared_expert_intermediate_size: int
+    mlp_only_layers: List[int]
+    moe_intermediate_size: int
+    rms_norm_eps: float
+    vocab_size: int
+    num_key_value_heads: int
+    rope_theta: float
+    partial_rotary_factor: float
+    max_position_embeddings: int
+    head_dim: Optional[int] = None
+    norm_topk_prob: bool = False
+    tie_word_embeddings: bool = False
+    attention_bias: bool = False
+    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
+    layer_types: Optional[List[str]] = None
+    full_attention_interval: int = 4
+
+    def __post_init__(self):
+        if self.layer_types is None:
+            self.layer_types = [
+                (
+                    "linear_attention"
+                    if (i + 1) % self.full_attention_interval != 0
+                    else "full_attention"
+                )
+                for i in range(self.num_hidden_layers)
+            ]
+
+
+class Qwen3_5MoEGatedDeltaNet(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.num_v_heads = config.linear_num_value_heads
+        self.num_k_heads = config.linear_num_key_heads
+        self.head_k_dim = config.linear_key_head_dim
+        self.head_v_dim = config.linear_value_head_dim
+        self.key_dim = self.head_k_dim * self.num_k_heads
+        self.value_dim = self.head_v_dim * self.num_v_heads
+        if self.num_v_heads % self.num_k_heads != 0:
+            raise ValueError(
+                f"num_v_heads ({self.num_v_heads}) must be divisible by num_k_heads ({self.num_k_heads})"
+            )
+
+        self.conv_kernel_size = config.linear_conv_kernel_dim
+        self.layer_norm_epsilon = config.rms_norm_eps
+
+        self.conv_dim = self.key_dim * 2 + self.value_dim
+        self.conv1d = nn.Conv1d(
+            in_channels=self.conv_dim,
+            out_channels=self.conv_dim,
+            bias=False,
+            kernel_size=self.conv_kernel_size,
+            groups=self.conv_dim,
+            padding=0,
+        )
+
+        self.in_proj_qkv = nn.Linear(
+            self.hidden_size, self.key_dim * 2 + self.value_dim, bias=False
+        )
+        self.in_proj_z = nn.Linear(self.hidden_size, self.value_dim, bias=False)
+        self.in_proj_b = nn.Linear(self.hidden_size, self.num_v_heads, bias=False)
+        self.in_proj_a = nn.Linear(self.hidden_size, self.num_v_heads, bias=False)
+
+        self.dt_bias = mx.ones(self.num_v_heads)
+
+        A = mx.random.uniform(low=0, high=16, shape=(self.num_v_heads,))
+        self.A_log = mx.log(A)
+
+        self.norm = Qwen3NextRMSNormGated(self.head_v_dim, eps=self.layer_norm_epsilon)
+
+        self.out_proj = nn.Linear(self.value_dim, self.hidden_size, bias=False)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        B, S, _ = inputs.shape
+
+        qkv = self.in_proj_qkv(inputs)
+        z = self.in_proj_z(inputs).reshape(B, S, self.num_v_heads, self.head_v_dim)
+        b = self.in_proj_b(inputs)
+        a = self.in_proj_a(inputs)
+
+        if cache is not None and cache[0] is not None:
+            conv_state = cache[0]
+        else:
+            conv_state = mx.zeros(
+                (B, self.conv_kernel_size - 1, self.conv_dim),
+                dtype=inputs.dtype,
+            )
+
+        if mask is not None:
+            qkv = mx.where(mask[..., None], qkv, 0)
+        conv_input = mx.concatenate([conv_state, qkv], axis=1)
+        if cache is not None:
+            cache[0] = conv_input[:, -(self.conv_kernel_size - 1) :]
+        conv_out = nn.silu(self.conv1d(conv_input))
+
+        q, k, v = mx.split(conv_out, [self.key_dim, 2 * self.key_dim], -1)
+        q = q.reshape(B, S, self.num_k_heads, self.head_k_dim)
+        k = k.reshape(B, S, self.num_k_heads, self.head_k_dim)
+        v = v.reshape(B, S, self.num_v_heads, self.head_v_dim)
+
+        state = cache[1] if cache else None
+        inv_scale = k.shape[-1] ** -0.5
+        q = (inv_scale**2) * mx.fast.rms_norm(q, None, 1e-6)
+        k = inv_scale * mx.fast.rms_norm(k, None, 1e-6)
+
+        out, state = gated_delta_update(
+            q,
+            k,
+            v,
+            a,
+            b,
+            self.A_log,
+            self.dt_bias,
+            state,
+            mask,
+            use_kernel=not self.training,
+        )
+
+        if cache is not None:
+            cache[1] = state
+
+        out = self.norm(out, z)
+        return self.out_proj(out.reshape(B, S, -1))
+
+
+class Qwen3NextSparseMoeBlock(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        dim = args.hidden_size
+        intermediate_size = args.moe_intermediate_size
+        shared_expert_intermediate_size = args.shared_expert_intermediate_size
+
+        self.norm_topk_prob = args.norm_topk_prob
+        self.num_experts = num_experts = args.num_experts
+        self.top_k = args.num_experts_per_tok
+
+        self.gate = nn.Linear(dim, num_experts, bias=False)
+        self.switch_mlp = SwitchGLU(dim, intermediate_size, num_experts)
+
+        self.shared_expert = Qwen3NextMLP(dim, shared_expert_intermediate_size)
+        self.shared_expert_gate = nn.Linear(dim, 1, bias=False)
+
+    def __call__(
+        self,
+        x: mx.array,
+    ) -> mx.array:
+        gates = self.gate(x)
+        gates = mx.softmax(gates, axis=-1, precise=True)
+
+        k = self.top_k
+        inds = mx.argpartition(gates, kth=-k, axis=-1)[..., -k:]
+        scores = mx.take_along_axis(gates, inds, axis=-1)
+        if self.norm_topk_prob:
+            scores = scores / scores.sum(axis=-1, keepdims=True)
+
+        y = self.switch_mlp(x, inds)
+        y = (y * scores[..., None]).sum(axis=-2)
+
+        shared_y = self.shared_expert(x)
+        shared_y = mx.sigmoid(self.shared_expert_gate(x)) * shared_y
+
+        return y + shared_y
+
+
+class Qwen3MoENextDecoderLayer(nn.Module):
+    def __init__(self, args: ModelArgs, layer_idx: int):
+        super().__init__()
+        self.is_linear = (layer_idx + 1) % args.full_attention_interval != 0
+        if self.is_linear:
+            self.linear_attn = Qwen3_5MoEGatedDeltaNet(args)
+        else:
+            self.self_attn = Qwen3NextAttention(args)
+
+        self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+        self.post_attention_layernorm = nn.RMSNorm(
+            args.hidden_size, eps=args.rms_norm_eps
+        )
+        if (layer_idx not in args.mlp_only_layers) and (
+            args.num_experts > 0 and (layer_idx + 1) % args.decoder_sparse_step == 0
+        ):
+            self.mlp = Qwen3NextSparseMoeBlock(args)
+        else:
+            self.mlp = Qwen3NextMLP(args.hidden_size, args.intermediate_size)
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        if self.is_linear:
+            r = self.linear_attn(self.input_layernorm(x), mask, cache)
+        else:
+            r = self.self_attn(self.input_layernorm(x), mask, cache)
+        h = x + r
+        out = h + self.mlp(self.post_attention_layernorm(h))
+        return out
+
+
+class Qwen3NextModel(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
+        self.layers = [
+            Qwen3MoENextDecoderLayer(args=args, layer_idx=i)
+            for i in range(args.num_hidden_layers)
+        ]
+        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+        self.ssm_idx = 0
+        self.fa_idx = args.full_attention_interval - 1
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        hidden_states = self.embed_tokens(inputs)
+
+        if cache is None:
+            cache = [None] * len(self.layers)
+
+        fa_mask = create_attention_mask(hidden_states, cache[self.fa_idx])
+        ssm_mask = create_ssm_mask(hidden_states, cache[self.ssm_idx])
+
+        for layer, c in zip(self.layers, cache):
+            mask = ssm_mask if layer.is_linear else fa_mask
+            hidden_states = layer(hidden_states, mask=mask, cache=c)
+
+        return self.norm(hidden_states)
+
+
+class Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.model_type = args.model_type
+        self.model = Qwen3NextModel(args)
+        if not args.tie_word_embeddings:
+            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        out = self.model(inputs, cache)
+        if self.args.tie_word_embeddings:
+            out = self.model.embed_tokens.as_linear(out)
+        else:
+            out = self.lm_head(out)
+        return out
+
+    @property
+    def layers(self):
+        return self.model.layers
+
+    def make_cache(self):
+        return [ArraysCache(size=2) if l.is_linear else KVCache() for l in self.layers]
+
+    def sanitize(self, weights):
+        if "model.layers.0.mlp.experts.0.up_proj.weight" not in weights:
+            return weights
+        weights = {key: value for key, value in weights.items() if "mtp." not in key}
+
+        if self.args.tie_word_embeddings:
+            weights.pop("lm_head.weight", None)
+
+        for l in range(self.args.num_hidden_layers):
+            prefix = f"model.layers.{l}.mlp"
+            for n in ["up_proj", "down_proj", "gate_proj"]:
+                to_join = [
+                    weights.pop(f"{prefix}.experts.{e}.{n}.weight")
+                    for e in range(self.args.num_experts)
+                ]
+                weights[f"{prefix}.switch_mlp.{n}.weight"] = mx.stack(to_join)
+
+        norm_keys = (
+            ".input_layernorm.weight",
+            ".post_attention_layernorm.weight",
+            "model.norm.weight",
+            ".q_norm.weight",
+            ".k_norm.weight",
+        )
+        for k, v in weights.items():
+            if "conv1d.weight" in k and v.shape[-1] != 1:
+                weights[k] = v.moveaxis(2, 1)
+            if any(k.endswith(sfx) for sfx in norm_keys):
+                if v.ndim == 1:
+                    weights[k] = v + 1.0
+        return weights
+
+    @property
+    def quant_predicate(self):
+        def predicate(path, _):
+            if path.endswith("mlp.gate") or path.endswith("shared_expert_gate"):
+                return {"group_size": 64, "bits": 8}
+            return True
+
+        return predicate
diff --git a/mlx_lm/models/qwen3_next.py b/mlx_lm/models/qwen3_next.py
index 47c9971bd..fa23b46b3 100644
--- a/mlx_lm/models/qwen3_next.py
+++ b/mlx_lm/models/qwen3_next.py
@@ -45,7 +45,7 @@ class ModelArgs(BaseModelArgs):
     rope_theta: float
     partial_rotary_factor: float
     max_position_embeddings: int
-    head_dim: int
+    head_dim: Optional[int] = None
     norm_topk_prob: bool = False
     tie_word_embeddings: bool = False
     attention_bias: bool = False
@@ -73,7 +73,7 @@ def __init__(self, args: ModelArgs):
         super().__init__()
         self.num_key_value_heads = args.num_key_value_heads
         self.num_attention_heads = args.num_attention_heads
-        self.head_dim = args.head_dim
+        self.head_dim = args.head_dim or (args.hidden_size // args.num_attention_heads)
         self.scale = self.head_dim**-0.5
 
         self.q_proj = nn.Linear(