diff --git a/nemo_skills/evaluation/evaluator/mcq.py b/nemo_skills/evaluation/evaluator/mcq.py
index 522aeca8c3..e057fb3a12 100644
--- a/nemo_skills/evaluation/evaluator/mcq.py
+++ b/nemo_skills/evaluation/evaluator/mcq.py
@@ -19,34 +19,56 @@
 from tqdm import tqdm
 
 from nemo_skills.evaluation.math_grader import extract_answer
-from nemo_skills.utils import get_logger_name, unroll_files
+from nemo_skills.utils import get_logger_name, nested_dataclass, unroll_files
 
 LOG = logging.getLogger(get_logger_name(__file__))
 
 
+@nested_dataclass(kw_only=True)
+class MCQEvaluatorConfig:
+    extract_from_boxed: bool = True
+    # only used if extract_from_boxed is False
+    extract_regex: str = r"The final answer is (.+)$"
+
+
 def eval_mcq(cfg):
+    # Create config from cfg.eval_config (following pattern from other evaluators)
+    eval_config = MCQEvaluatorConfig(**cfg.eval_config)
+
     def extract_letter(text, extract_from_boxed: bool = True, extract_regex: str = r"The final answer is (.+)$"):
-        # extract prediction from boxed{}
-        parsed = extract_answer(text, extract_from_boxed=extract_from_boxed, extract_regex=extract_regex)
-        if parsed is not None and len(parsed) != 1:
-            match = re.findall(r"\b[A-J]\b(?!.*\b[A-J]\b)", parsed, re.DOTALL)
-            if len(match) > 0:
-                parsed = match[-1].strip()
+        # extract prediction from boxed{} or regex
+        extracted_answer = extract_answer(text, extract_from_boxed=extract_from_boxed, extract_regex=extract_regex)
+        parsed_letter = None
+
+        if extracted_answer is not None:
+            if len(extracted_answer) == 1:
+                parsed_letter = extracted_answer
+            elif len(extracted_answer) > 1:
+                # try to extract the letter from extracted answer, useful to match <A>, {A}, *A*, etc.
+                match = re.findall(r"\b[A-Z]\b(?!.*\b[A-Z]\b)", extracted_answer, re.DOTALL)
+                if len(match) > 0:
+                    parsed_letter = match[-1].strip()
 
         # adapted from https://artificialanalysis.ai/methodology/intelligence-benchmarking#intelligence-index-evaluation-suite-overview
-        if parsed is None:
+        if parsed_letter is None:
             match = re.findall(r"(?i)[\*\_]{0,2}Answer[\*\_]{0,2}\s*:[\s\*\_]{0,2}\s*([A-Z])(?![a-zA-Z0-9])", text)
             if match:
-                parsed = match[-1].strip()
-        return parsed
+                parsed_letter = match[-1].strip().upper()
+
+        LOG.info(
+            f"Final parsed letter: {parsed_letter}, extract_from_boxed: {extract_from_boxed}, extract_regex: {extract_regex}, extracted_answer: {extracted_answer}"
+        )
+
+        return parsed_letter
 
     for file in unroll_files(cfg.input_files):
         with open(file, "rt", encoding="utf-8") as fin:
             data = [json.loads(line) for line in fin]
         with open(file, "wt", encoding="utf-8") as fout:
             for sample in tqdm(data):
-                extract_from_boxed = sample.get("extract_from_boxed", True)
-                extract_regex = sample.get("extract_regex", r"The final answer is (.+)$")
+                # Per-sample values override config defaults for backward compatibility
+                extract_from_boxed = sample.get("extract_from_boxed", eval_config.extract_from_boxed)
+                extract_regex = sample.get("extract_regex", eval_config.extract_regex)
                 sample["predicted_answer"] = extract_letter(
                     sample["generation"], extract_from_boxed=extract_from_boxed, extract_regex=extract_regex
                 )