diff --git a/tests/entrypoints/openai/test_default_mm_loras.py b/tests/entrypoints/openai/test_default_mm_loras.py
index 336bda81a9ef..818ee2644b54 100644
--- a/tests/entrypoints/openai/test_default_mm_loras.py
+++ b/tests/entrypoints/openai/test_default_mm_loras.py
@@ -29,7 +29,7 @@ def multimodal_server():  # noqa: F811
         "--dtype",
         "half",
         "--max-model-len",
-        "12800",
+        "4096",
         "--enforce-eager",
         # lora config below
         "--enable-lora",
diff --git a/vllm/config/model.py b/vllm/config/model.py
index c335c5c25e9e..e3d158c1c60f 100644
--- a/vllm/config/model.py
+++ b/vllm/config/model.py
@@ -2142,8 +2142,18 @@ def _get_and_verify_max_len(
     # If the user didn't specify `max_model_len`, then use that derived from
     # the model config as a default value.
     if max_model_len is None:
-        max_model_len = int(derived_max_model_len)
+        # For LongRoPE, default to original_max_position_embeddings to avoid
+        # performance degradation for shorter sequences
+        if rope_scaling is not None and rope_scaling["rope_type"] == "longrope":
+            max_model_len = int(
+                getattr(
+                    hf_config, "original_max_position_embeddings", derived_max_model_len
+                )
+            )
+        else:
+            max_model_len = int(derived_max_model_len)
         max_model_len = current_platform.check_max_model_len(max_model_len)
+
     # If the user specified a max length, make sure it is smaller than the
     # derived length from the HF model config.
     elif max_model_len > derived_max_model_len:
diff --git a/vllm/model_executor/layers/rotary_embedding/phi3_long_rope_scaled_rope.py b/vllm/model_executor/layers/rotary_embedding/phi3_long_rope_scaled_rope.py
index 2a42e3bd00ec..e58c9783479b 100644
--- a/vllm/model_executor/layers/rotary_embedding/phi3_long_rope_scaled_rope.py
+++ b/vllm/model_executor/layers/rotary_embedding/phi3_long_rope_scaled_rope.py
@@ -5,8 +5,13 @@
 import torch
 import torch.nn as nn
 
+from vllm.config import get_current_vllm_config
+from vllm.logger import init_logger
+
 from .common import rotate_neox
 
+logger = init_logger(__name__)
+
 
 class Phi3LongRoPEScaledRotaryEmbedding(nn.Module):
     """Phi3 family of models scaled rotary embedding.
@@ -43,6 +48,22 @@ def __init__(
         self.short_factor = short_factor
         self.long_factor = long_factor
 
+        # Force long factors if max_model_len (runtime max length) exceeds
+        # original_max_position_embeddings to prevent KV cache invalidation when
+        # sequences cross this threshold during generation
+        max_model_len = get_current_vllm_config().model_config.max_model_len
+        self.use_long_rope = max_model_len > original_max_position_embeddings
+        if self.use_long_rope:
+            logger.warning_once(
+                "Using LongRoPE scaling factors. This enables longer "
+                "contexts (%d tokens vs original %d tokens) at the cost of "
+                "some performance degradation for shorter sequences. If "
+                "this is not desired, set `max_model_len` to be at most %d.",
+                max_position_embeddings,
+                original_max_position_embeddings,
+                original_max_position_embeddings,
+            )
+
         scale = self.max_position_embeddings / self.original_max_position_embeddings
         if scale <= 1.0:
             scaling_factor = 1.0
@@ -112,15 +133,12 @@ def forward(
         query = query.view(*query.shape[:-1], -1, self.head_size)
         key = key.view(*key.shape[:-1], -1, self.head_size)
 
-        k = self.original_max_position_embeddings
-        long_prompt_offset = (
-            torch.any(positions > k).float() * torch.full_like(positions, k)
-        ).long()
-        idx = (
-            torch.add(positions, long_prompt_offset)
-            if long_prompt_offset is not None
-            else positions
-        )
+        if self.use_long_rope:
+            k = self.original_max_position_embeddings
+            long_prompt_offset = torch.full_like(positions, k).long()
+            idx = torch.add(positions, long_prompt_offset)
+        else:
+            idx = positions
         idx = torch.add(idx, offsets) if offsets is not None else idx
         cos_sin = torch.index_select(self.long_short_cos_sin_cache, 0, idx)