diff --git a/models/demos/llama3/PERF.md b/models/demos/llama3/PERF.md
index d8e6dbb95ce1..dd060a14c1c7 100644
--- a/models/demos/llama3/PERF.md
+++ b/models/demos/llama3/PERF.md
@@ -20,6 +20,7 @@ This configuration uses bfp4 MLP FF1+FF3 for all models.
 | 8b | N300 | 84 | 98 | 38.6 |
 | 8b | T3K | 84 | 98 | 52.6 |
 | 11b | N300 | 86 | 97 | 38.6 |
+| 11b | T3K | 84 | 98 | 52.6 |
 | 70b | T3K | 95 | 100 | 14.3 |
 
 ## LlamaOptimizations.accuracy
@@ -38,4 +39,5 @@ This configuration uses bfp4 MLP FF1+FF3 only for the 3.1-70B model.
 | 8b | N300 | 90 | 98 | 34.1 |
 | 8b | T3K | 88 | 97 | 49.9 |
 | 11b | N300 | 90 | 97 | 33.8 |
+| 11b | T3K | 88 | 97 | 52.6 |
 | 70b | T3K | 95 | 100 | 14.5 |
diff --git a/models/demos/llama3/tests/test_llama_accuracy.py b/models/demos/llama3/tests/test_llama_accuracy.py
index e5f81a8840e4..8ce5648a8a82 100644
--- a/models/demos/llama3/tests/test_llama_accuracy.py
+++ b/models/demos/llama3/tests/test_llama_accuracy.py
@@ -34,8 +34,6 @@ def get_accuracy_thresholds(model_name: str, device_name: str, optimizations: Ll
     sections = content.split("## ")
     target_section = next(s for s in sections if s.startswith(f"LlamaOptimizations.{optimizations.__name__}\n"))
 
-    print(target_section)
-
     # Parse the table and find the row for our model and device
     rows = [
         line.split("|")[1:]  # Each row starts with a separator
@@ -87,13 +85,6 @@ def test_tt_model_accuracy(mesh_device, prefill_len, decode_len, use_program_cac
     model_args = TtModelArgs(mesh_device, optimizations=optimizations)
     tokenizer = Tokenizer(model_args.tokenizer_path)
 
-    # Get accuracy thresholds from PERF.md
-    min_top1_acc, min_top5_acc = get_accuracy_thresholds(
-        model_args.model_name,
-        model_args.device_name,
-        optimizations,
-    )
-
     # Load state_dict for TT model
     logger.info("Loading weights...")
     state_dict = model_args.load_state_dict()
@@ -320,6 +311,13 @@ def test_tt_model_accuracy(mesh_device, prefill_len, decode_len, use_program_cac
             true_word = sanitize(tokenizer.decode([true_token]))
             logger.info(f"{error['position']}: {context}[{incorrect}] != [{expected}], true: [{true_word}]")
 
+    # Get accuracy thresholds from PERF.md
+    min_top1_acc, min_top5_acc = get_accuracy_thresholds(
+        model_args.model_name,
+        model_args.device_name,
+        optimizations,
+    )
+
     logger.info(f"Top-1: {total_top1_acc:.0f}% | Top-5: {total_top5_acc:.0f}%")
     assert total_top1_acc > min_top1_acc, f"Top-1 accuracy {total_top1_acc:.1f}% is too low (expected >{min_top1_acc}%)"
     assert total_top5_acc > min_top5_acc, f"Top-5 accuracy {total_top5_acc:.1f}% is too low (expected >{min_top5_acc}%)"