diff --git a/src/MaxText/configs/models/qwen3-next-80b-a3b.yml b/src/MaxText/configs/models/qwen3-next-80b-a3b.yml
index f48d7da34d..6f362ba4f5 100644
--- a/src/MaxText/configs/models/qwen3-next-80b-a3b.yml
+++ b/src/MaxText/configs/models/qwen3-next-80b-a3b.yml
@@ -31,6 +31,7 @@ normalization_layer_epsilon: 1.0e-6
 base_mlp_dim: 512
 base_moe_mlp_dim: 512
 num_experts: 512
+shared_experts: 1
 num_experts_per_tok: 10
 norm_topk_prob: True
 
diff --git a/src/maxtext/utils/maxtext_utils.py b/src/maxtext/utils/maxtext_utils.py
index 89a93f6098..b9df72c1d9 100644
--- a/src/maxtext/utils/maxtext_utils.py
+++ b/src/maxtext/utils/maxtext_utils.py
@@ -488,12 +488,82 @@ def get_dense_moe_layers(config):
   elif config.decoder_block == DecoderBlockType.LLAMA4:
     num_moe_layers = config.num_decoder_layers // config.interleave_moe_layer_step
     num_dense_layers = config.num_decoder_layers - num_moe_layers
+  elif config.decoder_block == DecoderBlockType.QWEN3_NEXT:
+    num_moe_layers = config.num_decoder_layers
+    num_dense_layers = 0
   else:
-    raise ValueError("Currently we only support DeepSeek and Llama4 calculation.")
+    raise ValueError("Currently we only support DeepSeek, Llama4, and Qwen3-Next calculation.")
 
   return num_dense_layers, num_moe_layers
 
 
+def calculate_gated_delta_net_flops_per_device(config):
+  """Calculates the FLOPs for a single Gated Delta Net (Linear Attention) layer."""
+  B = config.per_device_batch_size
+  S = config.max_target_length
+  E = config.emb_dim
+
+  H_k = config.gdn_num_key_heads
+  H_v = config.gdn_num_value_heads
+  D_k = config.gdn_key_head_dim
+  D_v = config.gdn_value_head_dim
+  C = config.gdn_chunk_size
+  K_conv = config.gdn_conv_kernel_dim
+
+  K_dim = H_k * D_k
+  V_dim = H_v * D_v
+
+  # 1. Projections (Learnable Weights)
+  # in_proj_qkvz: E -> 2*K_dim + 2*V_dim
+  flops_qkvz = 2 * B * S * E * (2 * K_dim + 2 * V_dim)
+  # in_proj_ba: E -> 2*H_v
+  flops_ba = 2 * B * S * E * (2 * H_v)
+  # out_proj: V_dim -> E
+  flops_out = 2 * B * S * V_dim * E
+
+  flops_projections = flops_qkvz + flops_ba + flops_out
+
+  # 2. Convolution (Learnable Weights)
+  # Depthwise conv on dim (2*K_dim + V_dim)
+  # 2 * B * S * Channels * Kernel
+  flops_conv = 2 * B * S * (2 * K_dim + V_dim) * K_conv
+
+  # 3. Core Gated Delta Net (Attention-like operations)
+  # Assumptions:
+  # H = H_v (broadcasting K to V heads if H_v > H_k)
+  # N = num_chunks & N * C ~ S
+  #
+  # Query (Q): [B, S, H_v, D_k]
+  # Keys (K): [B, S, H_v, D_k]
+  # Values (V): [B, S, H_v, D_v]
+  # Intra-Chunk Attention (A): [B, N, H_v, C, C]
+  # Recurrent State (S): [B, N, H_v, D_k, D_v]
+
+  # - Intra-chunk terms (per chunk C):
+  #   - attn (K*K): 2 * B * S * H_v * C * D_k
+  #   - val_intra (A*V): 2 * B * S * H_v * C * D_v
+  #   - k_cum (A*K): 2 * B * S * H_v * C * D_k
+  #   - inner_attn_body loop (iterative refinement): ≈ (C - 1) * B * H * N * C^2 ≈ B * H * S * C^2
+  flops_intra = 2 * B * S * H_v * C * (2 * D_k + D_v) + (B * H_v * S * C**2)
+
+  # - Inter-chunk terms (Recurrent State D_k * D_v):
+  #   - attn_i (Q*K): 2 * B * S * H_v * C * D_k
+  #   - v_prime (K*S): 2 * B * S * H_v * D_k * D_v
+  #   - attn_inter (Q*S): 2 * B * S * H_v * D_k * D_v
+  #   - core_out (A*V): 2 * B * S * H_v * C * D_v
+  #   - update (K*V): 2 * B * S * H_v * D_k * D_v
+  flops_inter = (2 * B * S * H_v * C * (D_k + D_v)) + (6 * B * S * H_v * D_k * D_v)
+
+  flops_core = flops_intra + flops_inter
+
+  # Weights part: Projections + Conv
+  gdn_weight_flops = flops_projections + flops_conv
+  # Attention part: Core
+  gdn_attn_flops = flops_core
+
+  return gdn_weight_flops, gdn_attn_flops
+
+
 def calculate_gemma3_vision_layers_tflops_per_device(config):
   """
   Estimate TFLOPs for Gemma3 vision encoder (ViT-style).
@@ -634,7 +704,7 @@ def calculate_tflops_training_per_device(config, log=True):
   # MLP flops
   if config.num_experts > 1:
     # calculation based on dropless implementation
-    if config.decoder_block in (DecoderBlockType.DEEPSEEK, DecoderBlockType.LLAMA4):
+    if config.decoder_block in (DecoderBlockType.DEEPSEEK, DecoderBlockType.LLAMA4, DecoderBlockType.QWEN3_NEXT):
       total_ffn_flops = calculate_routed_and_shared_ffn_tflops_per_device(config)
     else:
       gate_flops = 2 * config.per_device_batch_size * config.max_target_length * config.emb_dim * config.num_experts
@@ -702,6 +772,24 @@ def calculate_tflops_training_per_device(config, log=True):
         (total_ffn_flops + (qkv_flops + projection_flops) * config.num_decoder_layers + embedding_flops) * 3 / 10**12
     )
     attention_tflops = causal_attention_flops * config.num_decoder_layers * 3 / 10**12
+  elif config.decoder_block == DecoderBlockType.QWEN3_NEXT:
+    gdn_weight_flops_per_layer, gdn_attn_flops_per_layer = calculate_gated_delta_net_flops_per_device(config)
+    cycle_interval = config.inhomogeneous_layer_cycle_interval
+    num_full_attn_layers = config.num_decoder_layers // cycle_interval
+    num_linear_attn_layers = config.num_decoder_layers - num_full_attn_layers
+
+    # Weights TFLOPs:
+    total_weights = (
+        total_ffn_flops
+        + embedding_flops
+        + (qkv_flops + projection_flops) * num_full_attn_layers
+        + gdn_weight_flops_per_layer * num_linear_attn_layers
+    )
+    learnable_weight_tflops = total_weights * 3 / 10**12
+
+    # Attention TFLOPs:
+    total_attn = (causal_attention_flops * num_full_attn_layers) + (gdn_attn_flops_per_layer * num_linear_attn_layers)
+    attention_tflops = total_attn * 3 / 10**12
   else:
     # multiply by 3 for both feed forward and back propagation flops
     learnable_weight_tflops = (
diff --git a/tests/unit/flop_calculation_test.py b/tests/unit/flop_calculation_test.py
index d147f71ff1..7b0de4317f 100644
--- a/tests/unit/flop_calculation_test.py
+++ b/tests/unit/flop_calculation_test.py
@@ -98,6 +98,150 @@ def compute_gpt_attention_flops_per_device(self, kwargs: dict) -> float:
 
     return attention_flops / 1e12  # return tflops
 
+  def compute_qwen3_next_attention_flops_per_device(self, kwargs: dict) -> float:
+    """
+    Computes the total training TFLOPs per device for a Qwen3-Next model.
+    Only counts the attention mechanism operations (non-weights).
+    """
+    B = kwargs["per_device_batch_size"]
+    S = kwargs["max_target_length"]
+    N = kwargs["base_num_decoder_layers"]
+    cycle_interval = kwargs["inhomogeneous_layer_cycle_interval"]
+
+    # Layer counts
+    num_full_layers = N // cycle_interval
+    num_linear_layers = N - num_full_layers
+
+    # 1. Full Attention FLOPs (Causal)
+    D_head = kwargs["head_dim"]
+    H_q = kwargs["base_num_query_heads"]
+    # 2 for QK^T and SV, 3 for fwd+bwd.
+    # Note: maxtext_utils divides by 2 for causal masking.
+    # Formula: 2 * 3 * B * S^2 * H * D
+    full_attn_flops = 2 * 3 * num_full_layers * B * (S**2) * H_q * D_head
+
+    # 2. Linear Attention (Gated Delta Net) FLOPs
+    H_v = kwargs["gdn_num_value_heads"]
+    D_k = kwargs["gdn_key_head_dim"]
+    D_v = kwargs["gdn_value_head_dim"]
+    C = kwargs["gdn_chunk_size"]
+
+    # Formulas from maxtext_utils.calculate_gated_delta_net_flops_per_device
+    flops_intra = 2 * B * S * H_v * C * (2 * D_k + D_v) + (B * H_v * S * C**2)
+    flops_inter = (2 * B * S * H_v * C * (D_k + D_v)) + (6 * B * S * H_v * D_k * D_v)
+
+    # 3 for fwd+bwd
+    linear_attn_flops = 3 * num_linear_layers * (flops_intra + flops_inter)
+
+    return (full_attn_flops + linear_attn_flops) / 1e12
+
+  @pytest.mark.cpu_only
+  def test_qwen3_next_flops(self):
+    """Test Qwen3-Next Flops calculation"""
+    kwargs = {
+        "model_name": "qwen3-next-80b-a3b",
+        "override_model_config": True,
+        "per_device_batch_size": 1,
+        "max_target_length": 4096,
+        "decoder_block": "qwen3_next",
+        "gradient_accumulation_steps": 1,
+        "skip_jax_distributed_system": True,
+        # Core Architectural Parameters
+        "base_emb_dim": 2048,
+        "base_num_decoder_layers": 48,
+        "base_num_query_heads": 16,
+        "base_num_kv_heads": 2,
+        "head_dim": 256,
+        "vocab_size": 151936,
+        # MoE Parameters
+        "base_mlp_dim": 512,  # Note: maxtext_utils uses moe_mlp_dim for calculations
+        "base_moe_mlp_dim": 512,
+        "num_experts": 512,
+        "num_experts_per_tok": 10,
+        "mlp_activations": ["silu", "linear"],
+        # Qwen3-Next Specific Parameters
+        "inhomogeneous_layer_cycle_interval": 4,
+        "gdn_conv_kernel_dim": 4,
+        "gdn_key_head_dim": 128,
+        "gdn_value_head_dim": 128,
+        "gdn_num_key_heads": 16,
+        "gdn_num_value_heads": 32,
+        "gdn_chunk_size": 64,
+    }
+
+    # 1. Calculate Attention TFLOPs
+    attention_tflops = self.compute_qwen3_next_attention_flops_per_device(kwargs)
+
+    # 2. Calculate Learnable Weight Active Params
+    # Config Shortcuts
+    emb_dim = kwargs["base_emb_dim"]
+    vocab = kwargs["vocab_size"]
+    N = kwargs["base_num_decoder_layers"]
+
+    # MoE Active Params (per layer)
+    # FFN uses SwiGLU (3 matrices), Qwen3-Next has 1 shared + N routed experts
+    # Params = Gate + Shared + Routed
+    # Gate: emb_dim * num_experts
+    # Expert: 3 * emb_dim * moe_mlp_dim
+    moe_mlp_dim = kwargs["base_moe_mlp_dim"]
+    num_experts = kwargs["num_experts"]
+    num_routed = kwargs["num_experts_per_tok"]
+
+    params_moe_layer = (
+        (emb_dim * num_experts) + (3 * emb_dim * moe_mlp_dim * 1) + (3 * emb_dim * moe_mlp_dim * num_routed)
+    )
+
+    # Full Attention Params (per full layer)
+    Hq = kwargs["base_num_query_heads"]
+    Hkv = kwargs["base_num_kv_heads"]
+    Hd = kwargs["head_dim"]
+    # Q, K, V, Out projections
+    params_full_attn = (emb_dim * (Hq + 2 * Hkv) * Hd) + (Hq * Hd * emb_dim)
+
+    # GDN Linear Attention Params (per linear layer)
+    Hk_g = kwargs["gdn_num_key_heads"]
+    Hv_g = kwargs["gdn_num_value_heads"]
+    Dk_g = kwargs["gdn_key_head_dim"]
+    Dv_g = kwargs["gdn_value_head_dim"]
+    K_conv = kwargs["gdn_conv_kernel_dim"]
+
+    K_dim = Hk_g * Dk_g
+    V_dim = Hv_g * Dv_g
+
+    # Projections: qkvz (in->2K+2V), ba (in->2Hv), out (V->in)
+    params_gdn_proj = (emb_dim * (2 * K_dim + 2 * V_dim)) + (emb_dim * 2 * Hv_g) + (V_dim * emb_dim)
+    # Conv: depthwise on 2K+V
+    params_gdn_conv = (2 * K_dim + V_dim) * K_conv
+
+    params_gdn_layer = params_gdn_proj + params_gdn_conv
+
+    # Total Active Params
+    # 12 Full Layers, 36 Linear Layers
+    num_full = N // kwargs["inhomogeneous_layer_cycle_interval"]
+    num_linear = N - num_full
+
+    total_active_params = (
+        (vocab * emb_dim)
+        + (num_full * (params_full_attn + params_moe_layer))
+        + (num_linear * (params_gdn_layer + params_moe_layer))
+    )
+
+    # Weight TFLOPs = 6 * B * S * P
+    B = kwargs["per_device_batch_size"]
+    S = kwargs["max_target_length"]
+    weight_tflops = 6 * B * S * total_active_params / 1e12
+
+    golden_tflops = weight_tflops + attention_tflops
+
+    # Run Calculation
+    cfg = pyconfig.initialize(
+        [None, os.path.join(MAXTEXT_PKG_DIR, "configs", "base.yml")],
+        **kwargs,
+    )
+    calculated_tflops, _, _ = calculate_tflops_training_per_device(cfg)
+
+    self.assertFlopsAlmostEqual(calculated_tflops, golden_tflops)
+
   @pytest.mark.cpu_only
   def test_llama2_7b_flops(self):
     """Test Llama2 7b Flops calculation with default parameters"""