diff --git a/python/sglang/srt/debug_utils/text_comparator.py b/python/sglang/srt/debug_utils/text_comparator.py
index 5917fcfb6..3a6df19b9 100644
--- a/python/sglang/srt/debug_utils/text_comparator.py
+++ b/python/sglang/srt/debug_utils/text_comparator.py
@@ -1,4 +1,5 @@
 import argparse
+import hashlib
 import json
 from pathlib import Path
 
@@ -13,7 +14,11 @@ Supported inputs:
 
 
 def main(args):
-    df_input = _transform_df_input(_compute_df_raw(args))
+    if args.data_type == "simple_evals":
+        df_input = _compute_df_input_mode_simple_evals(args)
+    else:
+        df_input = _transform_df_input(_compute_df_raw(args))
+
     assert all(
         c in df_input.columns
         for c in ["category", "trial_index", "prompt_id", "prompt", "output", "correct"]
@@ -37,8 +42,9 @@ def main(args):
                 df_meta=df_meta.to_dicts(),
                 df_good_to_bad=df_good_to_bad.to_dicts(),
                 df_bad_to_good=df_bad_to_good.to_dicts(),
-            )
-        )
+            ),
+            indent=4,
+        ),
     )
 
     if not args.disable_print_details:
@@ -65,19 +71,70 @@ def main(args):
                 print(df)
 
 
+def _compute_df_input_mode_simple_evals(args):
+    return pl.concat(
+        [
+            _compute_df_input_one_mode_simple_evals(**info)
+            for info in _get_file_infos(args=args)
+        ]
+    )
+
+
+def _compute_df_input_one_mode_simple_evals(path, category, trial_index):
+    data = json.loads(Path(path).read_text())
+    rows = []
+
+    for single_eval_result in data["metadata"]["single_eval_results"]:
+        prompt = single_eval_result["example_level_metadata"][
+            "actual_queried_prompt_messages"
+        ]
+        score = single_eval_result["score"]
+        assert score in {0.0, 1.0}, f"{score=}"
+
+        row = dict(
+            category=category,
+            trial_index=trial_index,
+            prompt_id=_compute_id_from_object(prompt),
+            prompt=json.dumps(prompt),
+            output=single_eval_result["example_level_metadata"]["response_text"],
+            correct=score == 1.0,
+        )
+        rows.append(row)
+
+    return pl.DataFrame(rows)
+
+
+def _compute_id_from_object(obj):
+    if isinstance(obj, pl.Series):
+        obj = obj.to_list()
+    json_str = json.dumps(obj, sort_keys=True, ensure_ascii=False)
+    return hashlib.sha256(json_str.encode("utf-8")).hexdigest()
+
+
 def _compute_df_raw(args):
     return pl.concat(
         [
-            _read_df_raw(p, category=category, trial_index=i)
-            for category, paths in [
-                ("baseline", args.baseline_path),
-                ("target", args.target_path),
-            ]
-            for i, p in enumerate(paths)
+            _read_df_raw(
+                path=info["path"],
+                category=info["category"],
+                trial_index=info["trial_index"],
+            )
+            for info in _get_file_infos(args=args)
         ]
     )
 
 
+def _get_file_infos(args):
+    return [
+        dict(path=path, category=category, trial_index=trial_index)
+        for category, paths in [
+            ("baseline", args.baseline_path),
+            ("target", args.target_path),
+        ]
+        for trial_index, path in enumerate(paths)
+    ]
+
+
 def _read_df_raw(path: str, category: str, trial_index: int):
     return pl.read_ndjson(path).with_columns(
         category=pl.lit(category), trial_index=trial_index
@@ -108,7 +165,9 @@ def _transform_df_input(df: pl.DataFrame):
         print("Transform mode: SGLang bench")
         return df
     else:
-        raise Exception(f"Unknown data: {df.columns}")
+        raise Exception(
+            f"Unknown data: {df.columns}. You may need to set `--data-type` if using e.g. simple_evals."
+        )
 
 
 def _compute_df_meta(df_input: pl.DataFrame):
@@ -127,7 +186,9 @@ def _compute_df_meta(df_input: pl.DataFrame):
 
 
 def _handle_one_prompt(df_one_prompt: pl.DataFrame):
-    assert len(set(df_one_prompt["prompt"])) == 1
+    assert (
+        len(set(_compute_id_from_object(obj) for obj in df_one_prompt["prompt"])) == 1
+    )
 
     df_baseline = df_one_prompt.filter(pl.col("category") == "baseline")
     df_target = df_one_prompt.filter(pl.col("category") == "target")
@@ -162,6 +223,7 @@ def _compute_str_prefix_len(a: str, b: str) -> int:
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description=_DESCRIPTION)
+    parser.add_argument("--data-type", type=str, default="auto")
     parser.add_argument("--baseline-path", type=str, nargs="+")
     parser.add_argument("--target-path", type=str, nargs="+")
     parser.add_argument(