diff --git a/nodes/model_optimization_nodes.py b/nodes/model_optimization_nodes.py
index 2338b9c..1b80851 100644
--- a/nodes/model_optimization_nodes.py
+++ b/nodes/model_optimization_nodes.py
@@ -813,7 +813,7 @@ class WanVideoTeaCacheKJ:
         return {
             "required": {
                 "model": ("MODEL",),
-                "rel_l1_thresh": ("FLOAT", {"default": 0.275, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Threshold for to determine when to apply the cache, compromise between speed and accuracy. When using coefficients a good value range is something between 0.2-0.4, and without it shold be about 10 times smaller."}),
+                "rel_l1_thresh": ("FLOAT", {"default": 0.275, "min": 0.0, "max": 10.0, "step": 0.001, "tooltip": "Threshold for to determine when to apply the cache, compromise between speed and accuracy. When using coefficients a good value range is something between 0.2-0.4 for all but 1.3B model, which should be about 10 times smaller, same as when not using coefficients."}),
                 "start_percent": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The start percentage of the steps to use with TeaCache."}),
                 "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "The end percentage of the steps to use with TeaCache."}),
                 "cache_device": (["main_device", "offload_device"], {"default": "offload_device", "tooltip": "Device to cache to"}),
@@ -826,10 +826,29 @@ class WanVideoTeaCacheKJ:
     FUNCTION = "patch_teacache"
     CATEGORY = "KJNodes/teacache"
     DESCRIPTION = """
-Patch WanVideo model to use TeaCache. Speeds up inference by caching the output and applying it instead of doing the step.   
-Best results are achieved by choosing the appropriate coefficients for the model.  
-Early steps should never be skipped, with too aggressive values this can happen and the motion suffers. Starting later can help with that too.   
-When NOT using coefficients the threshold value should be about 10 times smaller than the value used with coefficients.  
+Patch WanVideo model to use TeaCache. Speeds up inference by caching the output and  
+applying it instead of doing the step.  Best results are achieved by choosing the  
+appropriate coefficients for the model. Early steps should never be skipped, with too  
+aggressive values this can happen and the motion suffers. Starting later can help with that too.   
+When NOT using coefficients, the threshold value should be  
+about 10 times smaller than the value used with coefficients.  
+
+Official recommended values https://github.com/ali-vilab/TeaCache/tree/main/TeaCache4Wan2.1:
+
+
+<pre style='font-family:monospace'>
++-------------------+--------+---------+--------+
+|       Model       |  Low   | Medium  |  High  |
++-------------------+--------+---------+--------+
+| Wan2.1 t2v 1.3B  |  0.05  |  0.07   |  0.08  |
+| Wan2.1 t2v 14B   |  0.14  |  0.15   |  0.20  |
+| Wan2.1 i2v 480P  |  0.13  |  0.19   |  0.26  |
+| Wan2.1 i2v 720P  |  0.18  |  0.20   |  0.30  |
++-------------------+--------+---------+--------+
+</pre> 
+
+
+
 """
     EXPERIMENTAL = True