diff --git a/examples/models/core/deepseek_v3/README.md b/examples/models/core/deepseek_v3/README.md
index ddd79000ab..8fc6f00537 100644
--- a/examples/models/core/deepseek_v3/README.md
+++ b/examples/models/core/deepseek_v3/README.md
@@ -3,7 +3,7 @@
 This guide walks you through the examples to run the DeepSeek‑V3/DeepSeek-R1 models using NVIDIA's TensorRT-LLM framework with the PyTorch backend.
 **DeepSeek-R1 and DeepSeek-V3 share exact same model architecture other than weights differences, and share same code path in TensorRT-LLM, for brevity we only provide one model example, the example command to be used interchangeablely by only replacing the model name to the other one**.
 
-To benchmark the model with best configurations, refer to [DeepSeek R1 benchmarking blog](../../docs/source/blogs/Best_perf_practice_on_DeepSeek-R1_in_TensorRT-LLM.md).
+To benchmark the model with best configurations, refer to [DeepSeek R1 benchmarking blog](../../../../docs/source/blogs/Best_perf_practice_on_DeepSeek-R1_in_TensorRT-LLM.md).
 
 Please refer to [this guide](https://nvidia.github.io/TensorRT-LLM/installation/build-from-source-linux.html) for how to build TensorRT-LLM from source and start a TRT-LLM docker container.