diff --git a/libs/ibm/langchain_ibm/chat_models.py b/libs/ibm/langchain_ibm/chat_models.py
index 02b20f3..aba2cf9 100644
--- a/libs/ibm/langchain_ibm/chat_models.py
+++ b/libs/ibm/langchain_ibm/chat_models.py
@@ -24,6 +24,7 @@
 from ibm_watsonx_ai import APIClient, Credentials  # type: ignore
 from ibm_watsonx_ai.foundation_models import ModelInference  # type: ignore
 from ibm_watsonx_ai.foundation_models.schema import (  # type: ignore
+    BaseSchema,
     TextChatParameters,
 )
 from langchain_core.callbacks import CallbackManagerForLLMRun
@@ -64,6 +65,7 @@
 from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
 from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
 from langchain_core.tools import BaseTool
+from langchain_core.utils._merge import merge_dicts
 from langchain_core.utils.function_calling import (
     convert_to_openai_function,
     convert_to_openai_tool,
@@ -73,7 +75,11 @@
 from pydantic import BaseModel, ConfigDict, Field, SecretStr, model_validator
 from typing_extensions import Self
 
-from langchain_ibm.utils import check_for_attribute, extract_params
+from langchain_ibm.utils import (
+    check_duplicate_chat_params,
+    check_for_attribute,
+    extract_chat_params,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -460,7 +466,54 @@ class ChatWatsonx(BaseChatModel):
     """Version of the CPD instance."""
 
     params: Optional[Union[dict, TextChatParameters]] = None
-    """Model parameters to use during request generation."""
+    """Model parameters to use during request generation.
+
+    Note:
+        `ValueError` is raised if the same Chat generation parameter is provided 
+        within the params attribute and as keyword argument."""
+
+    frequency_penalty: Optional[float] = None
+    """Positive values penalize new tokens based on their existing frequency in the 
+    text so far, decreasing the model's likelihood to repeat the same line verbatim."""
+
+    logprobs: Optional[bool] = None
+    """Whether to return log probabilities of the output tokens or not. 
+    If true, returns the log probabilities of each output token returned 
+    in the content of message."""
+
+    top_logprobs: Optional[int] = None
+    """An integer specifying the number of most likely tokens to return at each 
+    token position, each with an associated log probability. The option logprobs 
+    must be set to true if this parameter is used."""
+
+    max_tokens: Optional[int] = None
+    """The maximum number of tokens that can be generated in the chat completion. 
+    The total length of input tokens and generated tokens is limited by the 
+    model's context length."""
+
+    n: Optional[int] = None
+    """How many chat completion choices to generate for each input message. 
+    Note that you will be charged based on the number of generated tokens across 
+    all of the choices. Keep n as 1 to minimize costs."""
+
+    presence_penalty: Optional[float] = None
+    """Positive values penalize new tokens based on whether they appear in the 
+    text so far, increasing the model's likelihood to talk about new topics."""
+
+    temperature: Optional[float] = None
+    """What sampling temperature to use. Higher values like 0.8 will make the 
+    output more random, while lower values like 0.2 will make it more focused 
+    and deterministic.
+    
+    We generally recommend altering this or top_p but not both."""
+
+    top_p: Optional[float] = None
+    """An alternative to sampling with temperature, called nucleus sampling, 
+    where the model considers the results of the tokens with top_p probability 
+    mass. So 0.1 means only the tokens comprising the top 10% probability mass 
+    are considered.
+
+    We generally recommend altering this or temperature but not both."""
 
     verify: Union[str, bool, None] = None
     """You can pass one of following as verify:
@@ -473,7 +526,7 @@ class ChatWatsonx(BaseChatModel):
     """Model ID validation."""
 
     streaming: bool = False
-    """ Whether to stream the results or not. """
+    """Whether to stream the results or not."""
 
     watsonx_model: ModelInference = Field(default=None, exclude=True)  #: :meta private:
 
@@ -526,6 +579,30 @@ def lc_secrets(self) -> Dict[str, str]:
     @model_validator(mode="after")
     def validate_environment(self) -> Self:
         """Validate that credentials and python package exists in environment."""
+        self.params = self.params or {}
+
+        if isinstance(self.params, BaseSchema):
+            self.params = self.params.to_dict()
+
+        check_duplicate_chat_params(self.params, self.__dict__)
+
+        self.params.update(
+            {
+                k: v
+                for k, v in {
+                    "frequency_penalty": self.frequency_penalty,
+                    "logprobs": self.logprobs,
+                    "top_logprobs": self.top_logprobs,
+                    "max_tokens": self.max_tokens,
+                    "n": self.n,
+                    "presence_penalty": self.presence_penalty,
+                    "temperature": self.temperature,
+                    "top_p": self.top_p,
+                }.items()
+                if v is not None
+            }
+        )
+
         if isinstance(self.watsonx_client, APIClient):
             watsonx_model = ModelInference(
                 model_id=self.model_id,
@@ -608,9 +685,10 @@ def _generate(
             return generate_from_stream(stream_iter)
 
         message_dicts, params = self._create_message_dicts(messages, stop, **kwargs)
+        updated_params = self._merge_params(params, kwargs)
 
         response = self.watsonx_model.chat(
-            messages=message_dicts, **(kwargs | {"params": params})
+            messages=message_dicts, **(kwargs | {"params": updated_params})
         )
         return self._create_chat_result(response)
 
@@ -622,6 +700,7 @@ def _stream(
         **kwargs: Any,
     ) -> Iterator[ChatGenerationChunk]:
         message_dicts, params = self._create_message_dicts(messages, stop, **kwargs)
+        updated_params = self._merge_params(params, kwargs)
 
         default_chunk_class: Type[BaseMessageChunk] = AIMessageChunk
         base_generation_info: dict = {}
@@ -630,7 +709,7 @@ def _stream(
         is_first_tool_chunk = True
 
         for chunk in self.watsonx_model.chat_stream(
-            messages=message_dicts, **(kwargs | {"params": params})
+            messages=message_dicts, **(kwargs | {"params": updated_params})
         ):
             if not isinstance(chunk, dict):
                 chunk = chunk.model_dump()
@@ -664,10 +743,33 @@ def _stream(
 
             yield generation_chunk
 
+    @staticmethod
+    def _merge_params(params: dict, kwargs: dict) -> dict:
+        param_updates = {}
+        for k in [
+            "frequency_penalty",
+            "logprobs",
+            "top_logprobs",
+            "max_tokens",
+            "n",
+            "presence_penalty",
+            "temperature",
+            "top_p",
+        ]:
+            if kwargs.get(k) is not None:
+                param_updates[k] = kwargs.pop(k)
+
+        if kwargs.get("params"):
+            merged_params = merge_dicts(params, param_updates)
+        else:
+            merged_params = params | param_updates
+
+        return merged_params
+
     def _create_message_dicts(
         self, messages: List[BaseMessage], stop: Optional[List[str]], **kwargs: Any
     ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
-        params = extract_params(kwargs, self.params)
+        params = extract_chat_params(kwargs, self.params)
 
         if stop is not None:
             if params and "stop_sequences" in params:
diff --git a/libs/ibm/langchain_ibm/utils.py b/libs/ibm/langchain_ibm/utils.py
index 49a764a..0e14051 100644
--- a/libs/ibm/langchain_ibm/utils.py
+++ b/libs/ibm/langchain_ibm/utils.py
@@ -1,3 +1,4 @@
+from copy import deepcopy
 from typing import Any, Dict, Optional, Union
 
 from ibm_watsonx_ai.foundation_models.schema import BaseSchema  # type: ignore
@@ -28,3 +29,31 @@ def extract_params(
         params = params.to_dict()
 
     return params or {}
+
+
+def extract_chat_params(
+    kwargs: Dict[str, Any],
+    default_params: Optional[Union[BaseSchema, Dict[str, Any]]] = None,
+) -> Dict[str, Any]:
+    if kwargs.get("params") is not None:
+        params = kwargs.pop("params")
+        check_duplicate_chat_params(params, kwargs)
+    elif default_params is not None:
+        params = deepcopy(default_params)
+    else:
+        params = None
+
+    if isinstance(params, BaseSchema):
+        params = params.to_dict()
+
+    return params or {}
+
+
+def check_duplicate_chat_params(params: dict, kwargs: dict) -> None:
+    duplicate_keys = {k for k, v in kwargs.items() if v is not None and k in params}
+
+    if duplicate_keys:
+        raise ValueError(
+            f"Duplicate parameters found in params and keyword arguments: "
+            f"{list(duplicate_keys)}"
+        )
diff --git a/libs/ibm/poetry.lock b/libs/ibm/poetry.lock
index 4162223..b9e6b0f 100644
--- a/libs/ibm/poetry.lock
+++ b/libs/ibm/poetry.lock
@@ -350,47 +350,47 @@ zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "ibm-cos-sdk"
-version = "2.13.6"
+version = "2.13.5"
 description = "IBM SDK for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ibm-cos-sdk-2.13.6.tar.gz", hash = "sha256:171cf2ae4ab662a4b8ab58dcf4ac994b0577d6c92d78490295fd7704a83978f6"},
+    {file = "ibm-cos-sdk-2.13.5.tar.gz", hash = "sha256:1aff7f9863ac9072a3db2f0053bec99478b26f3fb5fa797ce96a15bbb13cd40e"},
 ]
 
 [package.dependencies]
-ibm-cos-sdk-core = "2.13.6"
-ibm-cos-sdk-s3transfer = "2.13.6"
+ibm-cos-sdk-core = "2.13.5"
+ibm-cos-sdk-s3transfer = "2.13.5"
 jmespath = ">=0.10.0,<=1.0.1"
 
 [[package]]
 name = "ibm-cos-sdk-core"
-version = "2.13.6"
+version = "2.13.5"
 description = "Low-level, data-driven core of IBM SDK for Python"
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "ibm-cos-sdk-core-2.13.6.tar.gz", hash = "sha256:dd41fb789eeb65546501afabcd50e78846ab4513b6ad4042e410b6a14ff88413"},
+    {file = "ibm-cos-sdk-core-2.13.5.tar.gz", hash = "sha256:d3a99d8b06b3f8c00b1a9501f85538d592463e63ddf8cec32672ab5a0b107b83"},
 ]
 
 [package.dependencies]
 jmespath = ">=0.10.0,<=1.0.1"
 python-dateutil = ">=2.9.0,<3.0.0"
-requests = ">=2.32.0,<2.32.3"
-urllib3 = ">=1.26.18,<3"
+requests = ">=2.32.3,<3.0"
+urllib3 = {version = ">=1.26.18,<2.2", markers = "python_version >= \"3.10\""}
 
 [[package]]
 name = "ibm-cos-sdk-s3transfer"
-version = "2.13.6"
+version = "2.13.5"
 description = "IBM S3 Transfer Manager"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "ibm-cos-sdk-s3transfer-2.13.6.tar.gz", hash = "sha256:e0acce6f380c47d11e07c6765b684b4ababbf5c66cc0503bc246469a1e2b9790"},
+    {file = "ibm-cos-sdk-s3transfer-2.13.5.tar.gz", hash = "sha256:9649b1f2201c6de96ff5a6b5a3686de3a809e6ef3b8b12c7c4f2f7ce72da7749"},
 ]
 
 [package.dependencies]
-ibm-cos-sdk-core = "2.13.6"
+ibm-cos-sdk-core = "2.13.5"
 
 [[package]]
 name = "ibm-watsonx-ai"
@@ -508,7 +508,7 @@ files = [
 
 [[package]]
 name = "langchain-core"
-version = "0.3.13"
+version = "0.3.15"
 description = "Building applications with LLMs through composability"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -531,12 +531,12 @@ typing-extensions = ">=4.7"
 type = "git"
 url = "https://github.com/langchain-ai/langchain.git"
 reference = "HEAD"
-resolved_reference = "94e5765416037b9b90a27603cfda36cbe86bce71"
+resolved_reference = "b509747c7f13c5cad2226e62a3c9d948664fa193"
 subdirectory = "libs/core"
 
 [[package]]
 name = "langchain-standard-tests"
-version = "0.1.1"
+version = "0.3.0"
 description = "Standard tests for LangChain implementations"
 optional = false
 python-versions = ">=3.9,<4.0"
@@ -545,7 +545,7 @@ develop = false
 
 [package.dependencies]
 httpx = "^0.27.0"
-langchain-core = "^0.3.0"
+langchain-core = "^0.3.15"
 pytest = ">=7,<9"
 syrupy = "^4"
 
@@ -553,18 +553,18 @@ syrupy = "^4"
 type = "git"
 url = "https://github.com/langchain-ai/langchain.git"
 reference = "HEAD"
-resolved_reference = "94e5765416037b9b90a27603cfda36cbe86bce71"
+resolved_reference = "b509747c7f13c5cad2226e62a3c9d948664fa193"
 subdirectory = "libs/standard-tests"
 
 [[package]]
 name = "langsmith"
-version = "0.1.137"
+version = "0.1.141"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
-    {file = "langsmith-0.1.137-py3-none-any.whl", hash = "sha256:4256d5c61133749890f7b5c88321dbb133ce0f440c621ea28e76513285859b81"},
-    {file = "langsmith-0.1.137.tar.gz", hash = "sha256:56cdfcc6c74cb20a3f437d5bd144feb5bf93f54c5a2918d1e568cbd084a372d4"},
+    {file = "langsmith-0.1.141-py3-none-any.whl", hash = "sha256:e133c6bed9c6d274f19735696a169dea890d35d444ae61c25fb47082aa2d0f16"},
+    {file = "langsmith-0.1.141.tar.gz", hash = "sha256:f3f17d2abc7b8a3857ad8d492b535970109b5ea40e712df91a782522ed581516"},
 ]
 
 [package.dependencies]
@@ -702,80 +702,80 @@ files = [
 
 [[package]]
 name = "orjson"
-version = "3.10.10"
+version = "3.10.11"
 description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "urllib3-2.1.0-py3-none-any.whl", hash = "sha256:55901e917a5896a349ff771be919f8bd99aff50b79fe58fec595eb37bbc56bb3"},
+    {file = "urllib3-2.1.0.tar.gz", hash = "sha256:df7aa8afb0148fa78488e7899b2c59b5f4ffcfa82e6c54ccb9dd37c1d7b52d54"},
 ]
 
 [package.extras]
 brotli = ["brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
-h2 = ["h2 (>=4,<5)"]
 socks = ["pysocks (>=1.5.6,!=1.5.7,<2.0)"]
 zstd = ["zstandard (>=0.18.0)"]
 
 [[package]]
 name = "watchdog"
-version = "5.0.3"
+version = "6.0.0"
 description = "Filesystem events monitoring"
 optional = false
 python-versions = ">=3.9"
 files = [
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-    {file = "watchdog-5.0.3.tar.gz", hash = "sha256:108f42a7f0345042a854d4d0ad0834b741d421330d5f575b81cb27b883500176"},
+    {file = "watchdog-6.0.0-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:d1cdb490583ebd691c012b3d6dae011000fe42edb7a82ece80965b42abd61f26"},
+    {file = "watchdog-6.0.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bc64ab3bdb6a04d69d4023b29422170b74681784ffb9463ed4870cf2f3e66112"},
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+    {file = "watchdog-6.0.0-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:e6439e374fc012255b4ec786ae3c4bc838cd7309a540e5fe0952d03687d8804e"},
+    {file = "watchdog-6.0.0-py3-none-manylinux2014_aarch64.whl", hash = "sha256:7607498efa04a3542ae3e05e64da8202e58159aa1fa4acddf7678d34a35d4f13"},
+    {file = "watchdog-6.0.0-py3-none-manylinux2014_armv7l.whl", hash = "sha256:9041567ee8953024c83343288ccc458fd0a2d811d6a0fd68c4c22609e3490379"},
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+    {file = "watchdog-6.0.0-py3-none-manylinux2014_ppc64.whl", hash = "sha256:212ac9b8bf1161dc91bd09c048048a95ca3a4c4f5e5d4a7d1b1a7d5752a7f96f"},
+    {file = "watchdog-6.0.0-py3-none-manylinux2014_ppc64le.whl", hash = "sha256:e3df4cbb9a450c6d49318f6d14f4bbc80d763fa587ba46ec86f99f9e6876bb26"},
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+    {file = "watchdog-6.0.0-py3-none-manylinux2014_x86_64.whl", hash = "sha256:20ffe5b202af80ab4266dcd3e91aae72bf2da48c0d33bdb15c66658e685e94e2"},
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+    {file = "watchdog-6.0.0-py3-none-win_amd64.whl", hash = "sha256:cbafb470cf848d93b5d013e2ecb245d4aa1c8fd0504e863ccefa32445359d680"},
+    {file = "watchdog-6.0.0-py3-none-win_ia64.whl", hash = "sha256:a1914259fa9e1454315171103c6a30961236f508b9b623eae470268bbcc6a22f"},
+    {file = "watchdog-6.0.0.tar.gz", hash = "sha256:9ddf7c82fda3ae8e24decda1338ede66e1c99883db93711d8fb941eaa2d8c282"},
 ]
 
 [package.extras]
@@ -1417,4 +1416,4 @@ type = ["pytest-mypy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<4.0"
-content-hash = "33a6cea354dc8afa5d40c16c29e339a844adf6649610266d09340f46c696fe8b"
+content-hash = "3bf4122afd1360e129c7ae11941675b2afd83686f4eda9556949a33579ab6486"
diff --git a/libs/ibm/pyproject.toml b/libs/ibm/pyproject.toml
index bb60464..c67c945 100644
--- a/libs/ibm/pyproject.toml
+++ b/libs/ibm/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "langchain-ibm"
-version = "0.3.2"
+version = "0.3.3"
 description = "An integration package connecting IBM watsonx.ai and LangChain"
 authors = ["IBM"]
 readme = "README.md"
@@ -13,7 +13,7 @@ license = "MIT"
 [tool.poetry.dependencies]
 python = ">=3.10,<4.0"
 langchain-core = ">=0.3.0,<0.4"
-ibm-watsonx-ai = "^1.1.14"
+ibm-watsonx-ai = "^1.1.16"
 
 [tool.poetry.group.test]
 optional = true
diff --git a/libs/ibm/tests/integration_tests/test_chat_models.py b/libs/ibm/tests/integration_tests/test_chat_models.py
index 184d33a..18ffc5f 100644
--- a/libs/ibm/tests/integration_tests/test_chat_models.py
+++ b/libs/ibm/tests/integration_tests/test_chat_models.py
@@ -315,7 +315,7 @@ def test_21a_bind_tools_tool_choice_auto() -> None:
     assert "location" in tool_call["args"]
 
 
-@pytest.mark.skip(reason="Not supported yet")
+@pytest.mark.xfail(reason="Not supported yet")
 def test_21b_bind_tools_tool_choice_none() -> None:
     chat = ChatWatsonx(
         model_id=MODEL_ID_TOOL,
@@ -350,7 +350,7 @@ def test_21b_bind_tools_tool_choice_none() -> None:
     assert "location" in tool_call["args"]
 
 
-@pytest.mark.skip(reason="Not supported yet")
+@pytest.mark.xfail(reason="Not supported yet")
 def test_21c_bind_tools_tool_choice_required() -> None:
     chat = ChatWatsonx(
         model_id=MODEL_ID_TOOL,
@@ -578,7 +578,7 @@ async def test_json_mode_async() -> None:
     assert json.loads(full.content) == {"a": 1}
 
 
-@pytest.mark.skip(reason="Not implemented")
+@pytest.mark.xfail(reason="Not implemented")
 def test_streaming_tool_call() -> None:
     chat = ChatWatsonx(
         model_id=MODEL_ID_TOOL,
@@ -652,7 +652,7 @@ def test_structured_output() -> None:
     assert isinstance(result, dict)
 
 
-@pytest.mark.skip(reason="Not implemented")
+@pytest.mark.xfail(reason="Not implemented")
 def test_streaming_structured_output() -> None:
     chat = ChatWatsonx(
         model_id=MODEL_ID_TOOL,
@@ -673,3 +673,285 @@ class Person(BaseModel):
         assert chunk.name == "Erick"
         assert chunk.age == 27
         chunk_num += 1
+
+
+###################################
+# --------Parameter-Tests-------- #
+###################################
+
+prompt_1 = "Say: 'Hello, My name is Erick!'"
+
+
+def test_init_with_params_1() -> None:
+    params_1 = None
+    chat = ChatWatsonx(
+        model_id=MODEL_ID_TOOL,
+        url=URL,  # type: ignore[arg-type]
+        project_id=WX_PROJECT_ID,
+        params=params_1,
+    )
+    assert chat.params == {}
+
+
+def test_init_with_params_2() -> None:
+    params_1 = {"max_tokens": 10}
+    chat = ChatWatsonx(
+        model_id=MODEL_ID_TOOL,
+        url=URL,  # type: ignore[arg-type]
+        project_id=WX_PROJECT_ID,
+        params=params_1,
+    )
+    assert chat.params == params_1
+
+
+def test_init_with_params_3() -> None:
+    params_1 = {"max_tokens": 10}
+    chat = ChatWatsonx(
+        model_id=MODEL_ID_TOOL,
+        url=URL,  # type: ignore[arg-type]
+        project_id=WX_PROJECT_ID,
+        **params_1,  # type: ignore[arg-type]
+    )
+    assert chat.params == params_1
+
+
+def test_init_with_params_4() -> None:
+    params_1 = {"max_tokens": 10}
+    params_2 = {"temperature": 0.5}
+    chat = ChatWatsonx(
+        model_id=MODEL_ID_TOOL,
+        url=URL,  # type: ignore[arg-type]
+        project_id=WX_PROJECT_ID,
+        params=params_1,
+        **params_2,  # type: ignore[arg-type]
+    )
+    assert chat.params == params_1 | params_2
+
+
+def test_init_with_params_5() -> None:
+    params_1 = {"max_tokens": 10}
+    params_2 = {"max_tokens": 20}
+
+    with pytest.raises(ValueError) as e:
+        ChatWatsonx(
+            model_id=MODEL_ID_TOOL,
+            url=URL,  # type: ignore[arg-type]
+            project_id=WX_PROJECT_ID,
+            params=params_1,
+            **params_2,  # type: ignore[arg-type]
+        )
+    assert (
+        "Duplicate parameters found in params and keyword arguments: ['max_tokens']"
+        in str(e.value)
+    )
+
+
+def test_invoke_with_params_1() -> None:
+    params_1 = None
+    chat = ChatWatsonx(
+        model_id=MODEL_ID_TOOL,
+        url=URL,  # type: ignore[arg-type]
+        project_id=WX_PROJECT_ID,
+    )
+    resp = chat.invoke(prompt_1, params=params_1)
+    completion_tokens = resp.response_metadata.get("token_usage", {}).get(
+        "completion_tokens"
+    )
+
+    assert chat.params == {}
+    assert 7 < completion_tokens < 11
+
+
+def test_invoke_with_params_2() -> None:
+    params_1 = {"max_tokens": 5}
+    chat = ChatWatsonx(
+        model_id=MODEL_ID_TOOL,
+        url=URL,  # type: ignore[arg-type]
+        project_id=WX_PROJECT_ID,
+    )
+    resp = chat.invoke(prompt_1, params=params_1)
+    completion_tokens = resp.response_metadata.get("token_usage", {}).get(
+        "completion_tokens"
+    )
+
+    assert chat.params == {}
+    assert completion_tokens == 5
+
+
+def test_invoke_with_params_3() -> None:
+    params_1_a = {"max_tokens": 5}
+    params_1_b = {"max_tokens": 10}
+    params_2_a = {"logprobs": False}
+    params_2_b = {"logprobs": True}
+    chat = ChatWatsonx(
+        model_id=MODEL_ID_TOOL,
+        url=URL,  # type: ignore[arg-type]
+        project_id=WX_PROJECT_ID,
+    )
+    resp_1 = chat.invoke(prompt_1, params=params_1_a, **params_2_a)  # type: ignore[arg-type]
+    completion_tokens = resp_1.response_metadata.get("token_usage", {}).get(
+        "completion_tokens"
+    )
+    logprobs = resp_1.response_metadata.get("logprobs")
+
+    assert chat.params == {}
+    assert completion_tokens == 5
+    assert not logprobs
+
+    resp_2 = chat.invoke(prompt_1, params=params_1_a, **params_2_b)  # type: ignore[arg-type]
+    completion_tokens = resp_2.response_metadata.get("token_usage", {}).get(
+        "completion_tokens"
+    )
+    logprobs = resp_2.response_metadata.get("logprobs")
+
+    assert chat.params == {}
+    assert completion_tokens == 5
+    assert logprobs
+
+    resp_3 = chat.invoke(prompt_1, **params_1_b, **params_2_b)  # type: ignore[arg-type]
+    completion_tokens = resp_3.response_metadata.get("token_usage", {}).get(
+        "completion_tokens"
+    )
+    logprobs = resp_3.response_metadata.get("logprobs")
+
+    assert chat.params == {}
+    assert 7 < completion_tokens < 11
+    assert logprobs
+
+
+def test_invoke_with_params_4() -> None:
+    params_1 = {"max_tokens": 5}
+    params_2 = {"max_tokens": 20}
+    chat = ChatWatsonx(
+        model_id=MODEL_ID_TOOL,
+        url=URL,  # type: ignore[arg-type]
+        project_id=WX_PROJECT_ID,
+    )
+    with pytest.raises(ValueError) as e:
+        chat.invoke(prompt_1, params=params_1, **params_2)  # type: ignore[arg-type]
+
+    assert (
+        "Duplicate parameters found in params and keyword arguments: ['max_tokens']"
+        in str(e.value)
+    )
+
+
+def test_invoke_with_params_5() -> None:
+    params_1 = {"max_tokens": 5, "logprobs": False}
+    params_2 = {"max_tokens": 10}
+    params_3 = {"logprobs": True}
+    chat = ChatWatsonx(
+        model_id=MODEL_ID_TOOL,
+        url=URL,  # type: ignore[arg-type]
+        project_id=WX_PROJECT_ID,
+    )
+    with pytest.raises(ValueError) as e:
+        chat.invoke(prompt_1, params=params_1, **params_2, **params_3)  # type: ignore[arg-type]
+
+    assert (
+        "Duplicate parameters found in params and keyword arguments: " in str(e.value)
+        and "'logprobs'" in str(e.value)
+        and "'max_tokens'" in str(e.value)
+    )
+
+
+def test_init_and_invoke_with_params_1() -> None:
+    params_1 = None
+    chat = ChatWatsonx(
+        model_id=MODEL_ID_TOOL,
+        url=URL,  # type: ignore[arg-type]
+        project_id=WX_PROJECT_ID,
+        params=params_1,
+    )
+    resp = chat.invoke(prompt_1, params=params_1)
+    completion_tokens = resp.response_metadata.get("token_usage", {}).get(
+        "completion_tokens"
+    )
+    assert chat.params == {}
+    assert 7 < completion_tokens < 11
+
+
+def test_init_and_invoke_with_params_2() -> None:
+    params_1_a = {"max_tokens": 4}
+    params_1_b = {"max_tokens": 5}
+    params_1_c = {"max_tokens": 6}
+
+    chat = ChatWatsonx(
+        model_id=MODEL_ID_TOOL,
+        url=URL,  # type: ignore[arg-type]
+        project_id=WX_PROJECT_ID,
+        params=params_1_a,
+    )
+    resp_1 = chat.invoke(prompt_1, params=params_1_b)
+    completion_tokens = resp_1.response_metadata.get("token_usage", {}).get(
+        "completion_tokens"
+    )
+    assert chat.params == params_1_a
+    assert completion_tokens == 5
+
+    resp_2 = chat.invoke(prompt_1, **params_1_c)  # type: ignore[arg-type]
+    completion_tokens = resp_2.response_metadata.get("token_usage", {}).get(
+        "completion_tokens"
+    )
+    assert chat.params == params_1_a
+    assert completion_tokens == 6
+
+    resp_2 = chat.invoke(prompt_1)
+    completion_tokens = resp_2.response_metadata.get("token_usage", {}).get(
+        "completion_tokens"
+    )
+    assert chat.params == params_1_a
+    assert completion_tokens == 4
+
+
+def test_init_and_invoke_with_params_3() -> None:
+    params_1_a = {"max_tokens": 2}
+    params_1_b = {"max_tokens": 5}
+    params_2_a = {"logprobs": False}
+    params_2_b = {"logprobs": True}
+    chat = ChatWatsonx(
+        model_id=MODEL_ID_TOOL,
+        url=URL,  # type: ignore[arg-type]
+        project_id=WX_PROJECT_ID,
+        params=params_1_a,
+        **params_2_a,  # type: ignore[arg-type]
+    )
+    resp_1 = chat.invoke(prompt_1, params=params_1_b, **params_2_b)  # type: ignore[arg-type]
+    completion_tokens = resp_1.response_metadata.get("token_usage", {}).get(
+        "completion_tokens"
+    )
+    logprobs = resp_1.response_metadata.get("logprobs")
+
+    assert chat.params == params_1_a | params_2_a
+    assert completion_tokens == 5
+    assert logprobs
+
+    resp_2 = chat.invoke(prompt_1)
+    completion_tokens = resp_2.response_metadata.get("token_usage", {}).get(
+        "completion_tokens"
+    )
+    logprobs = resp_2.response_metadata.get("logprobs")
+
+    assert chat.params == params_1_a | params_2_a
+    assert completion_tokens == 2
+    assert not logprobs
+
+
+def test_init_and_invoke_with_params_4() -> None:
+    params_1_a = {"max_tokens": 4}
+    params_1_b = {"max_tokens": 5}
+    params_1_c = {"max_tokens": 6}
+
+    chat = ChatWatsonx(
+        model_id=MODEL_ID_TOOL,
+        url=URL,  # type: ignore[arg-type]
+        project_id=WX_PROJECT_ID,
+        params=params_1_a,
+    )
+    with pytest.raises(ValueError) as e:
+        chat.invoke(prompt_1, params=params_1_b, **params_1_c)  # type: ignore[arg-type]
+
+    assert (
+        "Duplicate parameters found in params and keyword arguments: ['max_tokens']"
+        in str(e.value)
+    )