diff --git a/assets/tr/QA/MultiNativQA_GPT4_ZeroShot.py b/assets/tr/QA/MultiNativQA_GPT4_ZeroShot.py
new file mode 100644
index 00000000..a07b252b
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+++ b/assets/tr/QA/MultiNativQA_GPT4_ZeroShot.py
@@ -0,0 +1,53 @@
+from llmebench.datasets import MultiNativQADataset
+from llmebench.models import OpenAIModel
+from llmebench.tasks import MultiNativQATask
+
+
+def metadata():
+    return {
+        "author": "Arabic Language Technologies, QCRI, HBKU",
+        "model": "GPT4-o",
+        "description": "Deployed on Azure.",
+        "scores": {},
+    }
+
+
+def config():
+    return {
+        "dataset": MultiNativQADataset,
+        "task": MultiNativQATask,
+        "model": OpenAIModel,
+    }
+
+
+def prompt(input_sample):
+    # Define the question prompt
+    question_prompt = f"""
+    Please use your expertise to answer the following Turkish question. Answer in Turkish and rate your confidence level from 1 to 10.
+    Provide your response in the following JSON format: {{"answer": "your answer", "score": your confidence score}}. 
+    Please provide JSON output only. No additional text. Answer should be limited to less or equal to {input_sample['length']} words.
+
+    Question: {input_sample['question']}
+    """
+
+    # Define the assistant prompt
+    assistant_prompt = """
+    You are an Turkish AI assistant specialized in providing detailed and accurate answers across various fields. 
+    Your task is to deliver clear, concise, and relevant information. 
+    """
+
+    return [
+        {"role": "user", "content": question_prompt},
+        {"role": "assistant", "content": assistant_prompt},
+    ]
+
+
+def post_process(response):
+    content = response["choices"][0]["message"]["content"].strip()
+    content = content.replace("\n", "").strip()
+    if "```json" in content:
+        # content = content.replace("```json", "").replace('```', '').replace("\n}", "}")
+        # content = content.replace("{\n", "{").replace("\",\n", "\",")
+
+        content = re.search(r"```json(.*)```", content).group(1)
+    return json.loads(content)["answer"]