diff --git a/tests/models/test_static_embedding.py b/tests/models/test_static_embedding.py
new file mode 100644
index 000000000..983cb4a8d
--- /dev/null
+++ b/tests/models/test_static_embedding.py
@@ -0,0 +1,67 @@
+from __future__ import annotations
+
+from pathlib import Path
+
+import numpy as np
+import pytest
+from tokenizers import Tokenizer
+
+from sentence_transformers.models.StaticEmbedding import StaticEmbedding
+
+
+@pytest.fixture
+def tokenizer() -> Tokenizer:
+    return Tokenizer.from_pretrained("bert-base-uncased")
+
+
+@pytest.fixture
+def embedding_weights():
+    return np.random.rand(30522, 768)
+
+
+@pytest.fixture
+def static_embedding(tokenizer: Tokenizer, embedding_weights) -> StaticEmbedding:
+    return StaticEmbedding(tokenizer, embedding_weights=embedding_weights)
+
+
+def test_initialization_with_embedding_weights(tokenizer: Tokenizer, embedding_weights) -> None:
+    model = StaticEmbedding(tokenizer, embedding_weights=embedding_weights)
+    assert model.embedding.weight.shape == (30522, 768)
+
+
+def test_initialization_with_embedding_dim(tokenizer: Tokenizer) -> None:
+    model = StaticEmbedding(tokenizer, embedding_dim=768)
+    assert model.embedding.weight.shape == (30522, 768)
+
+
+def test_tokenize(static_embedding: StaticEmbedding) -> None:
+    texts = ["Hello world!", "How are you?"]
+    tokens = static_embedding.tokenize(texts)
+    assert "input_ids" in tokens
+    assert "offsets" in tokens
+
+
+def test_forward(static_embedding: StaticEmbedding) -> None:
+    texts = ["Hello world!", "How are you?"]
+    tokens = static_embedding.tokenize(texts)
+    output = static_embedding(tokens)
+    assert "sentence_embedding" in output
+
+
+def test_save_and_load(tmp_path: Path, static_embedding: StaticEmbedding) -> None:
+    save_dir = tmp_path / "model"
+    save_dir.mkdir()
+    static_embedding.save(str(save_dir))
+
+    loaded_model = StaticEmbedding.load(str(save_dir))
+    assert loaded_model.embedding.weight.shape == static_embedding.embedding.weight.shape
+
+
+def test_from_distillation() -> None:
+    model = StaticEmbedding.from_distillation("sentence-transformers-testing/stsb-bert-tiny-safetensors", pca_dims=32)
+    assert model.embedding.weight.shape == (29528, 32)
+
+
+def test_from_model2vec() -> None:
+    model = StaticEmbedding.from_model2vec("minishlab/M2V_base_output")
+    assert model.embedding.weight.shape == (29528, 256)