diff --git a/src/models/qwen35.cpp b/src/models/qwen35.cpp
index 802f2e2f13e9..8a4e434117fa 100644
--- a/src/models/qwen35.cpp
+++ b/src/models/qwen35.cpp
@@ -465,17 +465,14 @@ ggml_tensor * llama_model_qwen35::graph::build_layer_attn_linear(
     // Apply gated normalization: self.norm(core_attn_out, z)
     ggml_tensor * attn_out_norm = build_norm_gated(output, model.layers[il].ssm_norm, z_2d, il);
 
-    // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim]
-    ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
+    // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * head_dim , n_tokens * n_seqs ]
+    ggml_tensor * final_output = ggml_reshape_2d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens * n_seqs);
     cb(final_output, "final_output", il);
 
     // Output projection
     cur = build_lora_mm(model.layers[il].ssm_out, final_output, model.layers[il].ssm_out_s);
     cb(cur, "linear_attn_out", il);
 
-    // Reshape back to original dimensions
-    cur = ggml_reshape_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
-
     return cur;
 }
 
diff --git a/src/models/qwen35moe.cpp b/src/models/qwen35moe.cpp
index fdbb2c63d441..939f10f7f061 100644
--- a/src/models/qwen35moe.cpp
+++ b/src/models/qwen35moe.cpp
@@ -475,17 +475,13 @@ ggml_tensor * llama_model_qwen35moe::graph::build_layer_attn_linear(
     // Apply gated normalization: self.norm(core_attn_out, z)
     ggml_tensor * attn_out_norm = build_norm_gated(output, model.layers[il].ssm_norm, z_2d, il);
 
-    // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim]
-    ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
-    cb(final_output, "final_output", il);
+    // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * head_dim , n_tokens * n_seqs ]
+    ggml_tensor * final_output = ggml_reshape_2d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens * n_seqs);
 
     // Output projection
     cur = build_lora_mm(model.layers[il].ssm_out, final_output, model.layers[il].ssm_out_s);
     cb(cur, "linear_attn_out", il);
 
-    // Reshape back to original dimensions
-    cur = ggml_reshape_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
-
     return cur;
 }
 
diff --git a/src/models/qwen3next.cpp b/src/models/qwen3next.cpp
index 13aa68ee8028..98fafd917b44 100644
--- a/src/models/qwen3next.cpp
+++ b/src/models/qwen3next.cpp
@@ -542,17 +542,14 @@ ggml_tensor * llama_model_qwen3next::graph::build_layer_attn_linear(
     // Apply gated normalization: self.norm(core_attn_out, z)
     ggml_tensor * attn_out_norm = build_norm_gated(output, model.layers[il].ssm_norm, z_2d, il);
 
-    // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_tokens, n_seqs, n_heads * head_dim]
-    ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
+    // Final reshape: [head_dim, n_heads, n_tokens, n_seqs] -> [n_heads * head_dim , n_tokens * n_seqs ]
+    ggml_tensor * final_output = ggml_reshape_2d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens * n_seqs);
     cb(final_output, "final_output", il);
 
     // Output projection
     cur = build_lora_mm(model.layers[il].ssm_out, final_output);
     cb(cur, "linear_attn_out", il);
 
-    // Reshape back to original dimensions
-    cur = ggml_reshape_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
-
     return cur;
 }
 
diff --git a/src/models/qwen4exp.cpp b/src/models/qwen4exp.cpp
index 77430e7afdc4..22e7159840b9 100644
--- a/src/models/qwen4exp.cpp
+++ b/src/models/qwen4exp.cpp
@@ -1131,14 +1131,12 @@ ggml_tensor * llama_model_qwen4exp::graph::build_layer_attn_linear(
     // gated normalization, as self.norm(core_attn_out, z) in the reference
     ggml_tensor * attn_out_norm = build_norm_gated(output, model.layers[il].ssm_norm, z_2d, il);
 
-    ggml_tensor * final_output = ggml_reshape_3d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens, n_seqs);
+    ggml_tensor * final_output = ggml_reshape_2d(ctx0, attn_out_norm, head_v_dim * num_v_heads, n_seq_tokens * n_seqs);
     cb(final_output, "final_output", il);
 
     cur = build_lora_mm(model.layers[il].ssm_out, final_output, model.layers[il].ssm_out_s);
     cb(cur, "linear_attn_out", il);
 
-    cur = ggml_reshape_2d(ctx0, cur, n_embd, n_seq_tokens * n_seqs);
-
     return cur;
 }