diff --git a/tests/model_alignment/qwen_align_test.py b/tests/model_alignment/qwen_align_test.py
index bcb96813..4a06ab72 100644
--- a/tests/model_alignment/qwen_align_test.py
+++ b/tests/model_alignment/qwen_align_test.py
@@ -16,6 +16,8 @@
 import transformers
 from tunix.models.qwen2 import model as qwen2_model
 from tunix.models.qwen2 import params as qwen2_params
+from tunix.models.qwen3 import model as qwen3_model
+from tunix.models.qwen3 import params as qwen3_params
 from tunix.sft import utils
 from tunix.tests import test_common as tc
 
@@ -74,20 +76,35 @@ def get_per_layer_hf_output(model, seq_len: int, num_layer_to_run: int = 1):
   return logits[0].detach().numpy()
 
 
-def get_per_layer_jax_output(model, seq_len: int, num_layer_to_run: int = 1):
+def get_per_layer_jax_output(
+    model_name: str, model, seq_len: int, num_layer_to_run: int = 1
+):
   """Get the first decoder layer output from the Tunix model."""
   x = (jnp.arange(seq_len) + 1).reshape(1, -1)
   positions = jnp.arange(seq_len).reshape(1, -1)
   attn_mask = utils.make_causal_attn_mask(jnp.ones((1, seq_len)))
-  sin, cos = qwen2_model._generate_pos_embeddings(  # pylint: disable=protected-access
-      positions, model.config.head_dim, model.config.rope_theta
-  )
 
-  logits = model.embedder.encode(x)
-  for i in range(num_layer_to_run):
-    _, logits = model.layers[i](logits, None, attn_mask, sin, cos)
+  if model_name in [
+      "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
+      "Qwen/Qwen2.5-1.5B-Instruct",
+  ]:
+    sin, cos = qwen2_model._generate_pos_embeddings(  # pylint: disable=protected-access
+        positions, model.config.head_dim, model.config.rope_theta
+    )
+
+    logits = model.embedder.encode(x)
+    for i in range(num_layer_to_run):
+      _, logits = model.layers[i](logits, None, attn_mask, sin, cos)
+
+    return logits
+  elif model_name == "Qwen/Qwen3-0.6B":
+    logits = model.embedder.encode(x)
+    for i in range(num_layer_to_run):
+      _, logits = model.layers[i](logits, positions, None, attn_mask)
 
-  return logits
+    return logits
+  else:
+    raise ValueError(f"Unsupported model: {model_name}")
 
 
 class QwenAlignTest(parameterized.TestCase):
@@ -97,17 +114,27 @@ class QwenAlignTest(parameterized.TestCase):
           testcase_name="deepseek_r1_distill_qwen_1_5b",
           model_name="deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
           model_config=qwen2_model.ModelConfig.deepseek_r1_distill_qwen_1_5b,
+          model_params=qwen2_params,
           tolerance=2e-3,
       ),
       dict(
           testcase_name="qwen2_5_1_5b_instruct",
           model_name="Qwen/Qwen2.5-1.5B-Instruct",
           model_config=qwen2_model.ModelConfig.qwen2_5_1_5b,
+          model_params=qwen2_params,
+          tolerance=1e-3,
+      ),
+      dict(
+          testcase_name="qwen3_0_6b",
+          model_name="Qwen/Qwen3-0.6B",
+          model_config=qwen3_model.ModelConfig.qwen3_0_6b,
+          model_params=qwen3_params,
           tolerance=1e-3,
       ),
-      # Note: Qwen/Qwen2.5-7B-Instruct will OOM on v5e-8.
   )
-  def test_qwen_model_alignment(self, model_name, model_config, tolerance):
+  def test_qwen_model_alignment(
+      self, model_name, model_config, model_params, tolerance
+  ):
     model_path = os.path.join(tempfile.gettempdir(), "models", model_name)
 
     tc.download_from_huggingface(repo_id=model_name, model_path=model_path)
@@ -117,7 +144,7 @@ def test_qwen_model_alignment(self, model_name, model_config, tolerance):
     )
     print("HF model loaded.")
 
-    jax_model = qwen2_params.create_model_from_safe_tensors(
+    jax_model = model_params.create_model_from_safe_tensors(
         model_path,
         model_config(),
         mesh=jax.make_mesh((1, 1), ("fsdp", "tp")),
@@ -148,7 +175,9 @@ def test_qwen_model_alignment(self, model_name, model_config, tolerance):
 
     layer_to_run = model_config().num_layers
     hf_logits = get_per_layer_hf_output(hf_model, seq_len, layer_to_run)
-    jax_logits = get_per_layer_jax_output(jax_model, seq_len, layer_to_run)
+    jax_logits = get_per_layer_jax_output(
+        model_name, jax_model, seq_len, layer_to_run
+    )
     np.testing.assert_allclose(
         hf_logits.squeeze(),
         jax_logits.squeeze(),