diff --git a/python/sglang/multimodal_gen/docs/profiling.md b/python/sglang/multimodal_gen/docs/profiling.md
index 7d039557f..cf7ddc215 100644
--- a/python/sglang/multimodal_gen/docs/profiling.md
+++ b/python/sglang/multimodal_gen/docs/profiling.md
@@ -67,6 +67,8 @@ Besides profiler traces, you can also dump a lightweight JSON report that contai
 
 This is useful to quickly identify which stage dominates end-to-end latency, and whether denoising steps have uniform runtimes (and if not, which step has an abnormal spike).
 
+The dumped JSON contains a `denoise_steps_ms` field formatted as an array of objects, each with a `step` key (the step index) and a `duration_ms` key.
+
 Example:
 
 ```bash
diff --git a/python/sglang/multimodal_gen/runtime/utils/perf_logger.py b/python/sglang/multimodal_gen/runtime/utils/perf_logger.py
index 2afa203fc..d76233696 100644
--- a/python/sglang/multimodal_gen/runtime/utils/perf_logger.py
+++ b/python/sglang/multimodal_gen/runtime/utils/perf_logger.py
@@ -207,10 +207,9 @@ class PerformanceLogger:
             for name, duration_ms in timings.stages.items()
         ]
 
-        denoise_steps_ms = list(timings.steps)
-        denoise_steps = [
-            {"index": idx, "duration_ms": duration_ms}
-            for idx, duration_ms in enumerate(denoise_steps_ms)
+        denoise_steps_ms = [
+            {"step": idx, "duration_ms": duration_ms}
+            for idx, duration_ms in enumerate(timings.steps)
         ]
 
         report = {
@@ -221,7 +220,6 @@ class PerformanceLogger:
             "total_duration_ms": timings.total_duration_ms,
             "steps": formatted_steps,
             "denoise_steps_ms": denoise_steps_ms,
-            "denoise_steps": denoise_steps,
             "meta": meta or {},
         }