diff --git a/fastembed/text/onnx_embedding.py b/fastembed/text/onnx_embedding.py
index 1e9978f75..1ad498909 100644
--- a/fastembed/text/onnx_embedding.py
+++ b/fastembed/text/onnx_embedding.py
@@ -180,6 +180,20 @@
         sources=ModelSource(hf="jinaai/jina-clip-v1"),
         model_file="onnx/text_model.onnx",
     ),
+    DenseModelDescription(
+        model="BAAI/bge-m3",
+        dim=1024,
+        description=(
+            "Text embeddings, Unimodal (text), Multilingual (100+ languages), "
+            "8192 input tokens truncation, "
+            "versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity."
+        ),
+        license="mit",
+        size_in_GB=2.27,
+        sources=ModelSource(hf="BAAI/bge-m3"),
+        model_file="onnx/model.onnx",
+        additional_files=["onnx/model.onnx_data", "onnx/sentencepiece.bpe.model"],
+    ),
 ]
 
 
diff --git a/tests/test_text_onnx_embeddings.py b/tests/test_text_onnx_embeddings.py
index e919faf9d..11aa90a6f 100644
--- a/tests/test_text_onnx_embeddings.py
+++ b/tests/test_text_onnx_embeddings.py
@@ -68,6 +68,7 @@
     "Qdrant/clip-ViT-B-32-text": np.array([0.0083, 0.0103, -0.0138, 0.0199, -0.0069]),
     "thenlper/gte-base": np.array([0.0038, 0.0355, 0.0181, 0.0092, 0.0654]),
     "jinaai/jina-clip-v1": np.array([-0.0862, -0.0101, -0.0056, 0.0375, -0.0472]),
+    "BAAI/bge-m3": np.array([-0.0404, 0.037, -0.029, 0.0161, -0.0357]),
 }
 
 MULTI_TASK_MODELS = ["jinaai/jina-embeddings-v3"]