diff --git a/examples/llama-eval/llama-eval.py b/examples/llama-eval/llama-eval.py
index 646160d1a9..f27f03f479 100755
--- a/examples/llama-eval/llama-eval.py
+++ b/examples/llama-eval/llama-eval.py
@@ -16,6 +16,17 @@ from typing import Dict, List, Optional, Any, Tuple
 import requests
 from tqdm import tqdm
 import random
+from math import sqrt
+
+def wilson_interval(correct: int, total: int, z: float = 1.96) -> Tuple[float, float]:
+    """Wilson score confidence interval for a proportion."""
+    if total == 0:
+        return (0.0, 1.0)
+    p = correct / total
+    z2 = z * z / total
+    center = (p + z2 / 2) / (1 + z2)
+    margin = z * sqrt((p * (1 - p) + z2 / 4) / total) / (1 + z2)
+    return (center - margin, center + margin)
 
 cache_dir = Path.home() / ".cache" / "huggingface" / "datasets"
 cache_dir.mkdir(parents=True, exist_ok=True)
@@ -227,8 +238,9 @@ class EvalState:
             print(f"Results: 0/0 correct (0.0%)")
             print(f"{'='*60}")
         else:
+            ci_lower, ci_upper = self.accuracy_ci()
             print(f"\n{'='*60}")
-            print(f"Results: {self.correct}/{self.total} correct ({self.correct/self.total*100:.1f}%)")
+            print(f"Results: {self.correct}/{self.total} correct ({self.correct/self.total*100:.1f}%) [{ci_lower*100:.1f}%, {ci_upper*100:.1f}%]")
             print(f"{'='*60}")
 
     def dump(self):
@@ -253,6 +265,7 @@ class EvalState:
                     "reasoning_content": None
                 }
 
+        ci_lower, ci_upper = self.accuracy_ci()
         data = {
             "id": self.dataset_type,
             "tasks": [tid for _, tid in tasks_to_save],
@@ -260,6 +273,8 @@ class EvalState:
                 "total": self.total,
                 "correct": self.correct,
                 "total_time": self.total_time,
+                "ci_lower": ci_lower,
+                "ci_upper": ci_upper,
                 "cases": all_cases,
             },
             "sampling_config": self.sampling_config
@@ -278,6 +293,7 @@ class EvalState:
         incorrect_count = len(completed) - correct_count
         pending_count = len(tasks_to_save) - len(completed)
         accuracy = correct_count / len(completed) * 100 if completed else 0.0
+        ci_lower, ci_upper = wilson_interval(correct_count, len(completed)) if completed else (0.0, 1.0)
 
         sampling_parts = []
         for k, v in self.sampling_config.items():
@@ -378,7 +394,7 @@ class EvalState:
             <tr><td>Correct</td><td class="correct">{correct_count}</td></tr>
             <tr><td>Incorrect</td><td class="incorrect">{incorrect_count}</td></tr>
             <tr><td>Pending</td><td class="pending">{pending_count}</td></tr>
-            <tr><td>Accuracy</td><td>{accuracy:.1f}%</td></tr>
+            <tr><td>Accuracy</td><td>{accuracy:.1f}% [{ci_lower*100:.1f}%, {ci_upper*100:.1f}%]</td></tr>
             <tr><td>Total Time</td><td>{self.total_time:.1f}s</td></tr>
             <tr><td>Sampling</td><td>{sampling_str}</td></tr>
         </table>
@@ -510,10 +526,19 @@ class EvalState:
             print(f"Results: 0/0 correct (0.0%)")
             print(f"{'='*60}")
         else:
+            ci_lower, ci_upper = self.accuracy_ci()
             print(f"{'='*60}")
-            print(f"Results: {correct}/{total} correct ({correct/total*100:.1f}%)")
+            print(f"Results: {correct}/{total} correct ({correct/total*100:.1f}%) [{ci_lower*100:.1f}%, {ci_upper*100:.1f}%]")
             print(f"{'='*60}")
 
+    def accuracy_ci(self) -> Tuple[float, float]:
+        """Compute Wilson score confidence interval from completed cases."""
+        cases = self.task_states.get("cases", {})
+        completed = {tid: c for tid, c in cases.items() if c.get("status") == "ok"}
+        correct = sum(1 for c in completed.values() if c.get("correct", False))
+        total = len(completed)
+        return wilson_interval(correct, total)
+
 def normalize_number(s: str) -> Optional[int]:
     match = re.match(r"\d+", s)  # match digits from the start
     if not match: