diff --git a/litellm/litellm_core_utils/health_check_helpers.py b/litellm/litellm_core_utils/health_check_helpers.py
index 47a27c8ef5b..9fb0c4126b3 100644
--- a/litellm/litellm_core_utils/health_check_helpers.py
+++ b/litellm/litellm_core_utils/health_check_helpers.py
@@ -44,7 +44,9 @@ class HealthCheckHelpers:
         model_params["model"] = cheapest_models[0]
         model_params["litellm_logging_obj"] = litellm_logging_obj
         model_params["fallbacks"] = fallback_models
-        model_params["max_tokens"] = 10  # gpt-5-nano throws errors for max_tokens=1
+        model_params["max_tokens"] = model_params.get(
+            "max_tokens", 10
+        )  # gpt-5-nano throws errors for max_tokens=1
         await acompletion(**model_params)
         return {}
 
diff --git a/litellm/proxy/health_check.py b/litellm/proxy/health_check.py
index d228bdb2129..341ea4bd9e2 100644
--- a/litellm/proxy/health_check.py
+++ b/litellm/proxy/health_check.py
@@ -234,6 +234,14 @@ def _update_litellm_params_for_health_check(
     - for Bedrock models with region routing (bedrock/region/model), strips the litellm routing prefix but preserves the model ID
     """
     litellm_params["messages"] = _get_random_llm_message()
+    _health_check_max_tokens = model_info.get("health_check_max_tokens", None)
+    if _health_check_max_tokens is not None:
+        litellm_params["max_tokens"] = _health_check_max_tokens
+    elif "*" not in (
+        model_info.get("health_check_model") or litellm_params.get("model") or ""
+    ):
+        litellm_params["max_tokens"] = 1
+
     _health_check_model = model_info.get("health_check_model", None)
     if _health_check_model is not None:
         litellm_params["model"] = _health_check_model
diff --git a/tests/test_litellm/proxy/test_health_check_max_tokens.py b/tests/test_litellm/proxy/test_health_check_max_tokens.py
new file mode 100644
index 00000000000..da2bb21abea
--- /dev/null
+++ b/tests/test_litellm/proxy/test_health_check_max_tokens.py
@@ -0,0 +1,71 @@
+
+import pytest
+from litellm.proxy.health_check import _update_litellm_params_for_health_check
+from litellm.litellm_core_utils.health_check_helpers import HealthCheckHelpers
+from unittest.mock import AsyncMock, patch, MagicMock
+
+@pytest.mark.asyncio
+async def test_update_litellm_params_max_tokens_default():
+    """
+    Test that max_tokens defaults to 1 for non-wildcard models.
+    """
+    model_info = {}
+    litellm_params = {"model": "gpt-4"}
+    
+    updated_params = _update_litellm_params_for_health_check(model_info, litellm_params)
+    
+    assert updated_params["max_tokens"] == 1
+
+@pytest.mark.asyncio
+async def test_update_litellm_params_max_tokens_custom():
+    """
+    Test that max_tokens respects health_check_max_tokens from model_info.
+    """
+    model_info = {"health_check_max_tokens": 5}
+    litellm_params = {"model": "gpt-4"}
+    
+    updated_params = _update_litellm_params_for_health_check(model_info, litellm_params)
+    
+    assert updated_params["max_tokens"] == 5
+
+@pytest.mark.asyncio
+async def test_update_litellm_params_max_tokens_wildcard():
+    """
+    Test that max_tokens does NOT default to 1 for wildcard models.
+    """
+    model_info = {}
+    litellm_params = {"model": "openai/*"}
+    
+    updated_params = _update_litellm_params_for_health_check(model_info, litellm_params)
+    
+    # Should not be set to 1
+    assert "max_tokens" not in updated_params or updated_params["max_tokens"] != 1
+
+@pytest.mark.asyncio
+async def test_ahealth_check_wildcard_models_respects_max_tokens():
+    """
+    Test that ahealth_check_wildcard_models respects max_tokens if passed,
+    otherwise defaults to 10.
+    """
+    with patch("litellm.litellm_core_utils.llm_request_utils.pick_cheapest_chat_models_from_llm_provider", return_value=["gpt-4o-mini"]), \
+         patch("litellm.acompletion", new_callable=AsyncMock) as mock_acompletion:
+        
+        # Test Case 1: No max_tokens passed, should default to 10
+        model_params = {}
+        await HealthCheckHelpers.ahealth_check_wildcard_models(
+            model="openai/*",
+            custom_llm_provider="openai",
+            model_params=model_params,
+            litellm_logging_obj=MagicMock()
+        )
+        assert model_params["max_tokens"] == 10
+        
+        # Test Case 2: Custom health_check_max_tokens passed via model_params, should be respected
+        model_params = {"max_tokens": 3}
+        await HealthCheckHelpers.ahealth_check_wildcard_models(
+            model="openai/*",
+            custom_llm_provider="openai",
+            model_params=model_params,
+            litellm_logging_obj=MagicMock()
+        )
+        assert model_params["max_tokens"] == 3