diff --git a/test/srt/models/test_embedding_models.py b/test/srt/models/test_embedding_models.py
index aefe4f3e7..f3ed4cdd7 100644
--- a/test/srt/models/test_embedding_models.py
+++ b/test/srt/models/test_embedding_models.py
@@ -17,6 +17,7 @@ import multiprocessing as mp
 import unittest
 
 import torch
+from transformers import AutoConfig, AutoTokenizer
 
 from sglang.test.runners import DEFAULT_PROMPTS, HFRunner, SRTRunner
 from sglang.test.test_utils import get_similarities
@@ -34,6 +35,24 @@ class TestEmbeddingModels(unittest.TestCase):
     def setUpClass(cls):
         mp.set_start_method("spawn", force=True)
 
+    def _truncate_prompts(self, prompts, model_path):
+        config = AutoConfig.from_pretrained(model_path)
+        max_length = getattr(config, "max_position_embeddings", 2048)
+
+        tokenizer = AutoTokenizer.from_pretrained(model_path)
+
+        truncated_prompts = []
+        for prompt in prompts:
+            tokens = tokenizer(prompt, return_tensors="pt", truncation=False)
+            if len(tokens.input_ids[0]) > max_length:
+                truncated_text = tokenizer.decode(
+                    tokens.input_ids[0][: max_length - 1], skip_special_tokens=True
+                )
+                truncated_prompts.append(truncated_text)
+            else:
+                truncated_prompts.append(prompt)
+        return truncated_prompts
+
     def assert_close_prefill_logits(
         self,
         prompts,
@@ -42,12 +61,14 @@ class TestEmbeddingModels(unittest.TestCase):
         torch_dtype,
         prefill_tolerance,
     ) -> None:
+        truncated_prompts = self._truncate_prompts(prompts, model_path)
+
         with HFRunner(
             model_path,
             torch_dtype=torch_dtype,
             model_type="embedding",
         ) as hf_runner:
-            hf_outputs = hf_runner.forward(prompts)
+            hf_outputs = hf_runner.forward(truncated_prompts)
 
         with SRTRunner(
             model_path,
@@ -55,7 +76,7 @@ class TestEmbeddingModels(unittest.TestCase):
             torch_dtype=torch_dtype,
             model_type="embedding",
         ) as srt_runner:
-            srt_outputs = srt_runner.forward(prompts)
+            srt_outputs = srt_runner.forward(truncated_prompts)
 
         for i in range(len(prompts)):
             hf_logits = torch.Tensor(hf_outputs.embed_logits[i])