diff --git a/assets/ar/factuality_disinformation_harmful_content/subjectivity/ThatiARSubjectivity_Sonnet_ZeroShot_en.py b/assets/ar/factuality_disinformation_harmful_content/subjectivity/ThatiARSubjectivity_Sonnet_ZeroShot_en.py
new file mode 100644
index 00000000..71662c9b
--- /dev/null
+++ b/assets/ar/factuality_disinformation_harmful_content/subjectivity/ThatiARSubjectivity_Sonnet_ZeroShot_en.py
@@ -0,0 +1,63 @@
+import json
+
+from llmebench.datasets import ThatiARDataset
+from llmebench.models import AnthropicModel
+from llmebench.tasks import SubjectivityTask
+
+
+def metadata():
+    return {
+        "author": "Arabic Language Technologies, QCRI, HBKU",
+        "model": "claude-3-5-sonnet-20240620",
+        "description": "Anthropic model - claude-3-5-sonnet. Find more https://www.anthropic.com/news/claude-3-5-sonnet",
+        "scores": {},
+    }
+
+
+def config():
+    system_msg = "AI assistant specialized in classifying news article sentences into subjective or objective. A subjective sentence expresses personal opinions, feelings, or beliefs, while an objective sentence presents facts, data, or unbiased information."
+    return {
+        "dataset": ThatiARDataset,
+        "task": SubjectivityTask,
+        "model": AnthropicModel,
+        "model_args": {
+            "class_labels": ["SUBJ", "OBJ"],
+            "max_tries": 30,
+            "system": system_msg,
+        },
+    }
+
+
+def prompt(input_sample):
+
+    prompt = f"""
+        Classify the following Arabic 'sentence' as subjective or objective. Provide only the label. 
+        Provide your response in the following JSON format: {{"label": "your label"}}. 
+        Please provide JSON output only. No additional text.
+
+        sentence: {input_sample}
+        """
+    return [
+        {
+            "role": "user",
+            "content": prompt,
+        },
+    ]
+
+
+def post_process(response):
+    data = response["content"][0]["text"].lower()
+    data = json.loads(data)
+    label = data["label"]
+    if "label: objective" in label:
+        label_fixed = "OBJ"
+    elif "label: subjective" in label:
+        label_fixed = "SUBJ"
+    elif label == "objective" or label == "objective.":
+        label_fixed = "OBJ"
+    elif label == "subjective" or label == "subjective.":
+        label_fixed = "SUBJ"
+    else:
+        label_fixed = None
+
+    return label_fixed
diff --git a/llmebench/models/Anthropic.py b/llmebench/models/Anthropic.py
new file mode 100644
index 00000000..a5436de8
--- /dev/null
+++ b/llmebench/models/Anthropic.py
@@ -0,0 +1,130 @@
+import json
+import logging
+import os
+
+import anthropic
+
+from llmebench.models.model_base import ModelBase
+
+
+class AnthropicFailure(Exception):
+    """Exception class to map various failure types from the AzureModel server"""
+
+    def __init__(self, failure_type, failure_message):
+        self.type_mapping = {
+            "processing": "Model Inference failure",
+            "connection": "Failed to connect to the API endpoint",
+        }
+        self.type = failure_type
+        self.failure_message = failure_message
+
+    def __str__(self):
+        return (
+            f"{self.type_mapping.get(self.type, self.type)}: \n {self.failure_message}"
+        )
+
+
+class AnthropicModel(ModelBase):
+    """
+    Anthropic Model interface.
+
+    Arguments
+    ---------
+    api_url : EMPTY
+    timeout : int
+        Number of seconds before the request to the server is timed out
+    temperature : float
+        Temperature value to use for the model. Defaults to zero for reproducibility.
+    top_p : float
+        Top P value to use for the model. Defaults to 0.95
+    max_tokens : int
+        Maximum number of tokens to pass to the model. Defaults to 1512
+    """
+
+    def __init__(
+        self,
+        api_base=None,
+        api_key=None,
+        model_name=None,
+        timeout=20,
+        temperature=0,
+        top_p=0.95,
+        max_tokens=2000,
+        **kwargs,
+    ):
+        # API parameters
+        self.api_base = api_base or os.getenv("ANTHROPIC_API_URL")
+        self.api_key = api_key or os.getenv("ANTHROPIC_API_KEY")
+        self.model_name = model_name or os.getenv("ANTHROPIC_MODEL")
+
+        # Parameters
+        self.api_timeout = timeout
+        tolerance = 1e-7
+        self.temperature = temperature
+        if self.temperature < tolerance:
+            # Currently, the model inference fails if temperature
+            # is exactly 0, so we nudge it slightly to work around
+            # the issue
+            self.temperature += tolerance
+        self.top_p = top_p
+        self.max_tokens = max_tokens
+
+        if self.api_key is None:
+            raise Exception(
+                "API key must be provided as model config or environment variable (`ANTHROPIC_API_KEY`)"
+            )
+        if self.model_name is None:
+            raise Exception(
+                "Model name must be provided as model config or environment variable (`ANTHROPIC_MODEL`)"
+            )
+        self.model = self.model_name
+        # GPT parameters
+        self.model_params = {}
+        self.model_params["system"] = (
+            kwargs.get("system_msg")
+            if "system_msg" in kwargs and kwargs["system_msg"]
+            else "You are an expert AI assistant"
+        )
+        self.model_params["temperature"] = temperature
+        self.model_params["top_p"] = top_p
+        self.model_params["max_tokens"] = max_tokens
+        self.client = anthropic.Anthropic(api_key=self.api_key)
+
+        super(AnthropicModel, self).__init__(
+            retry_exceptions=(TimeoutError, AnthropicFailure), **kwargs
+        )
+
+    def summarize_response(self, response):
+        """Returns the first reply from the "assistant", if available"""
+        if (
+            "choices" in response
+            and isinstance(response["choices"], list)
+            and len(response["choices"]) > 0
+            and "message" in response["choices"][0]
+            and "content" in response["choices"][0]["message"]
+            and response["choices"][0]["message"]["role"] == "assistant"
+        ):
+            return response["choices"][0]["message"]["content"]
+
+        return response
+
+    def prompt(self, processed_input):
+        """
+        AnthropicModel API Implementation
+
+        Arguments
+        ---------
+        processed_input : dictionary
+            Must be a dictionary with one key "prompt", the value of which
+            must be a string.
+
+        Returns
+        -------
+        response : AnthropicModel API response
+        """
+
+        response = self.client.messages.create(
+            model=self.model, messages=processed_input, **self.model_params
+        )
+        response = json.loads(response.json())
+        return response
diff --git a/llmebench/models/__init__.py b/llmebench/models/__init__.py
index 92e44a30..16b0524a 100644
--- a/llmebench/models/__init__.py
+++ b/llmebench/models/__init__.py
@@ -1,3 +1,4 @@
+from .Anthropic import AnthropicModel
 from .AzureModel import AzureModel
 from .FastChat import FastChatModel
 from .HuggingFaceInferenceAPI import HuggingFaceInferenceAPIModel, HuggingFaceTaskTypes
diff --git a/setup.cfg b/setup.cfg
index eb8df2cc..97e1dc82 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -23,6 +23,7 @@ install_requires =
     datasets==2.14.6
     nltk==3.8.1
     openai==1.35.10
+    anthropic==0.31.2
     pandas==2.0.2
     pooch==1.7.0
     python-dotenv==1.0.0