diff --git a/configs/cot_eval_api.json b/configs/cot_eval_api.json
new file mode 100644
index 0000000..129d89e
--- /dev/null
+++ b/configs/cot_eval_api.json
@@ -0,0 +1,12 @@
+{
+    "job_type": "cot_eval_api",
+    "dataset": {
+      "input_path": "cot_input.json",
+      "output_path": "cot_output.json"
+    },
+    "inference":{
+      "base_url": "ENDPOINT",
+      "api_key": "TOKEN",
+      "max_new_tokens": 8196
+    }
+}
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diff --git a/easydistill/eval/cot_eval.py b/easydistill/eval/cot_eval.py
new file mode 100644
index 0000000..726050c
--- /dev/null
+++ b/easydistill/eval/cot_eval.py
@@ -0,0 +1,177 @@
+
+# Copyright 2024 Alibaba Group Holding Limited. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import json, jsonlines
+import argparse
+import logging
+import os
+import re
+from tqdm import tqdm
+from openai import OpenAI
+
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+
+def build_cot_prompts(instruction, output):
+    rv_prompt_template = (
+        "You are an expert judge tasked with evaluating the Reasoning Verbosity of a Chain-of-Thought (CoT) "
+        "for a given problem and its answer. Reasoning Verbosity Evaluation Focus: Assess how well the CoT’s "
+        "length and step complexity match the problem’s inherent difficulty. An optimal chain is neither "
+        "missing essential steps nor padded with needless digressions. A simple question should be solved "
+        "with a brief, direct chain; a challenging one may justifiably require a longer path with reflection "
+        "and error-checking. Scoring Guidelines (0-9):\n"
+        "0-1 Minimal verbosity, straightforward expression with little to no elaboration.\n"
+        "2-3 Clear and concise reasoning with necessary explanations.\n"
+        "4-5 Moderate verbosity with detailed explanations and thorough reasoning.\n"
+        "6-7 Extensive verbosity with comprehensive justification and exploration of complex connections.\n"
+        "8-9 High verbosity with deep, exhaustive exploration of reasoning; involves extensive elaboration, nested justifications, "
+        "and consideration of counterarguments or alternative perspectives.\n"
+        "Given Problem, Answer with hain-of-Thought, you will:\n"
+        "1. Analyze the Reasoning Verbosity\n"
+        "2. Determine score using the above criteria\n"
+        "3. Output ONLY the integer score (0-9), place your score in <score></score>\n"
+        f"Problem: {instruction}\n"
+        f"Answer with Chain-of-Thought: {output}"
+    )
+    cd_prompt_template = (
+        "You are an expert judge assessing the Cognitive Difficulty of a Chain-of-Thought (CoT) "
+        "for a given problem and its answer. Cognitive Difficulty Evaluation Focus: The level of "
+        "reasoning competence required for a model to follow and reproduce the chain faithfully. "
+        "Judge the reasoning approach, techniques, and overall difficulty. Higher scores correspond "
+        "to more advanced concepts, abstractions, or multi-layer reasoning patterns. "
+        "Scoring Guidelines (0-9):\n"
+        "0-1 Elementary facts or a single trivial operation.\n"
+        "2-3 Multi-step arithmetic, explicit enumeration, basic rule chaining.\n"
+        "4-5 Early-undergraduate logic/algebra; one non-obvious insight.\n"
+        "6-7 Advanced undergraduate techniques (determinants, dynamic programming, layered code reasoning, etc).\n"
+        "8-9 Graduate-level abstraction, nested proofs, intricate algorithmic analysis.\n"
+        "Given Problem, Answer with hain-of-Thought, you will:\n"
+        "1. Analyze the Cognitive Difficulty\n"
+        "2. Determine score using the above criteria\n"
+        "3. Output ONLY the integer score (0-9), place your score in <score></score>\n"
+        f"Problem: {instruction}\n"
+        f"Answer with Chain-of-Thought: {output}"
+    )
+    lc_prompt_template = (
+        "You are a rigorous logical validator analyzing problem-solving components. "
+        "Your task is to separately assess the validity of the reasoning process and final solution. "
+        "Given Problem, Answer with hain-of-Thought, you will:\n"
+        "1. Verify stepwise logical coherence and soundness\n"
+        "2. Confirm all critical problem constraints are properly addressed\n"
+        "3. Check for self-contradictions or unsupported leaps in logic\n"
+        "4. Verify the process can actually derive the proposed solution\n"
+        "5. Output ONLY the 1/0 answer (1 for true, 0 for false) for logical correctness, place your answer in <score></score>\n"
+        f"Problem: {instruction}\n"
+        f"Answer with Chain-of-Thought: {output}"    
+    )
+    return rv_prompt_template, cd_prompt_template, lc_prompt_template
+
+
+def extract_score(text):
+    match = re.search(r"<score>(\d+)</score>", text)
+    if match:
+        return int(match.group(1))
+    else:
+        return -1
+
+
+def read_json_fields(filename):
+    try:
+        with open(filename, 'r') as file:
+            data = json.load(file)
+        return data
+    except FileNotFoundError:
+        logging.error("The file was not found.")
+    except json.JSONDecodeError:
+        logging.error("There was an error decoding the JSON file.")
+    except Exception as e:
+        logging.error(f"An error occurred: {e}")
+
+
+def write_data_to_json_file(data, file_path):
+    try:
+        with open(file_path, 'w') as file:
+            json.dump(data, file, ensure_ascii=False, indent=4)
+        logging.info(f"Data successfully written to {file_path}")
+    except Exception as e:
+        logging.error(f"An error occurred: {e}")
+
+        
+def generate_teacher_response_api(data_list, config):
+    client = OpenAI(
+        api_key = config["inference"]["api_key"],
+        base_url = config["inference"]["base_url"]
+    )
+    models = client.models.list()
+    model = models.data[0].id
+    logging.info(model)
+    outcomes = []
+    for sample in tqdm(data_list, desc="Call remote model and generating responses"):
+        instruction = sample["instruction"]
+        output = sample["output"]
+        rv_prompt_template, cd_prompt_template, lc_prompt_template = build_cot_prompts(instruction, output)
+        
+        def generate_score(sample, model, config):
+            message = [
+                {'role': 'user', 'content': sample}
+            ]
+            completion = client.chat.completions.create(
+                messages = message,
+                model = model,
+                max_completion_tokens = config["inference"]["max_new_tokens"]
+            )
+            result = completion.choices[0].message.content
+            score = extract_score(result)
+            return score
+    
+        rv_score = generate_score(rv_prompt_template, model, config)
+        cd_score = generate_score(cd_prompt_template, model, config)
+        lc_score = generate_score(lc_prompt_template, model, config)
+        if lc_score == 1:
+            lc_score = True
+        else:
+            lc_score =False
+        
+        outcomes.append(
+            {
+                'instruction': instruction,
+                 'output': output,
+                 "reasoning_verbosity": rv_score,
+                 "cognitive_difficulty": cd_score,
+                 "logical_correctness": lc_score
+            }
+        )
+    write_data_to_json_file(outcomes, config["dataset"]["output_path"])
+
+
+def infer_with_teacher_model(config):
+    logging.info('Generating distillation data from the teacher model!')
+    data_list = read_json_fields(config["dataset"]["input_path"])
+    generate_teacher_response_api(data_list, config)
+       
+
+        
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--config', type=str, required=True, help='path to the json config file')
+    args = parser.parse_args()
+    config = json.load(open(args.config))
+    infer_with_teacher_model(config)
+
+
+if __name__ == "__main__":
+    main()
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