diff --git a/python/python/lancedb/rerankers/__init__.py b/python/python/lancedb/rerankers/__init__.py
index af833fd7..bbc5bbcf 100644
--- a/python/python/lancedb/rerankers/__init__.py
+++ b/python/python/lancedb/rerankers/__init__.py
@@ -4,6 +4,7 @@ from .colbert import ColbertReranker
 from .cross_encoder import CrossEncoderReranker
 from .linear_combination import LinearCombinationReranker
 from .openai import OpenaiReranker
+from .jina import JinaReranker
 
 __all__ = [
     "Reranker",
@@ -12,4 +13,5 @@ __all__ = [
     "LinearCombinationReranker",
     "OpenaiReranker",
     "ColbertReranker",
+    "JinaReranker",
 ]
diff --git a/python/python/lancedb/rerankers/jina.py b/python/python/lancedb/rerankers/jina.py
new file mode 100644
index 00000000..3af17a98
--- /dev/null
+++ b/python/python/lancedb/rerankers/jina.py
@@ -0,0 +1,103 @@
+from functools import cached_property
+from typing import Union
+
+import pyarrow as pa
+
+from ..util import attempt_import_or_raise
+from .base import Reranker
+
+
+class JinaReranker(Reranker):
+    """
+    Reranks the results using Jina reranker model.
+
+    Parameters
+    ----------
+    model_name : str, default "jinaai/jina-reranker-v1-turbo-en"
+        The name of the reranker to use. For all models, see
+          https://huggingface.co/jinaai/jina-reranker-v1-turbo-en
+    column : str, default "text"
+        The name of the column to use as input to the cross encoder model.
+    device : str, default None
+        The device to use for the cross encoder model. If None, will use "cuda"
+        if available, otherwise "cpu".
+    """
+
+    def __init__(
+        self,
+        model_name: str = "jinaai/jina-reranker-v1-turbo-en",
+        column: str = "text",
+        device: Union[str, None] = None,
+        return_score="relevance",
+    ):
+        super().__init__(return_score)
+        torch = attempt_import_or_raise("torch")
+        self.model_name = model_name
+        self.column = column
+        self.device = device
+        if self.device is None:
+            self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    @cached_property
+    def model(self):
+        transformers = attempt_import_or_raise("transformers")
+        model = transformers.AutoModelForSequenceClassification.from_pretrained(
+            self.model_name, num_labels=1, trust_remote_code=True
+        )
+
+        return model
+
+    def _rerank(self, result_set: pa.Table, query: str):
+        passages = result_set[self.column].to_pylist()
+        cross_inp = [[query, passage] for passage in passages]
+        cross_scores = self.model.compute_score(cross_inp)
+        result_set = result_set.append_column(
+            "_relevance_score", pa.array(cross_scores, type=pa.float32())
+        )
+
+        return result_set
+
+    def rerank_hybrid(
+        self,
+        query: str,
+        vector_results: pa.Table,
+        fts_results: pa.Table,
+    ):
+        combined_results = self.merge_results(vector_results, fts_results)
+        combined_results = self._rerank(combined_results, query)
+        # sort the results by _score
+        if self.score == "relevance":
+            combined_results = combined_results.drop_columns(["score", "_distance"])
+        elif self.score == "all":
+            raise NotImplementedError(
+                "return_score='all' not implemented for CrossEncoderReranker"
+            )
+        combined_results = combined_results.sort_by(
+            [("_relevance_score", "descending")]
+        )
+
+        return combined_results
+
+    def rerank_vector(
+        self,
+        query: str,
+        vector_results: pa.Table,
+    ):
+        vector_results = self._rerank(vector_results, query)
+        if self.score == "relevance":
+            vector_results = vector_results.drop_columns(["_distance"])
+
+        vector_results = vector_results.sort_by([("_relevance_score", "descending")])
+        return vector_results
+
+    def rerank_fts(
+        self,
+        query: str,
+        fts_results: pa.Table,
+    ):
+        fts_results = self._rerank(fts_results, query)
+        if self.score == "relevance":
+            fts_results = fts_results.drop_columns(["score"])
+
+        fts_results = fts_results.sort_by([("_relevance_score", "descending")])
+        return fts_results
diff --git a/python/python/tests/test_rerankers.py b/python/python/tests/test_rerankers.py
index 7775d598..645b15ec 100644
--- a/python/python/tests/test_rerankers.py
+++ b/python/python/tests/test_rerankers.py
@@ -1,5 +1,3 @@
-import os
-
 import lancedb
 import numpy as np
 import pytest
@@ -11,6 +9,7 @@ from lancedb.rerankers import (
     ColbertReranker,
     CrossEncoderReranker,
     OpenaiReranker,
+    JinaReranker,
 )
 from lancedb.table import LanceTable
 
@@ -119,136 +118,18 @@ def test_linear_combination(tmp_path):
     )
 
 
-@pytest.mark.skipif(
-    os.environ.get("COHERE_API_KEY") is None, reason="COHERE_API_KEY not set"
+@pytest.mark.slow
+@pytest.mark.parametrize(
+    "reranker",
+    [
+        ColbertReranker(),
+        OpenaiReranker(),
+        CohereReranker(),
+        CrossEncoderReranker(),
+        JinaReranker(),
+    ],
 )
-def test_cohere_reranker(tmp_path):
-    pytest.importorskip("cohere")
-    reranker = CohereReranker()
-    table, schema = get_test_table(tmp_path)
-    # Hybrid search setting
-    result1 = (
-        table.search("Our father who art in heaven", query_type="hybrid")
-        .rerank(normalize="score", reranker=CohereReranker())
-        .to_pydantic(schema)
-    )
-    result2 = (
-        table.search("Our father who art in heaven", query_type="hybrid")
-        .rerank(reranker=reranker)
-        .to_pydantic(schema)
-    )
-    assert result1 == result2
-
-    query = "Our father who art in heaven"
-    query_vector = table.to_pandas()["vector"][0]
-    result = (
-        table.search((query_vector, query))
-        .limit(30)
-        .rerank(reranker=reranker)
-        .to_arrow()
-    )
-
-    assert len(result) == 30
-    err = (
-        "The _relevance_score column of the results returned by the reranker "
-        "represents the relevance of the result to the query & should "
-        "be descending."
-    )
-    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
-
-    # Vector search setting
-    query = "Our father who art in heaven"
-    result = table.search(query).rerank(reranker=reranker).limit(30).to_arrow()
-    assert len(result) == 30
-    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
-    result_explicit = (
-        table.search(query_vector)
-        .rerank(reranker=reranker, query_string=query)
-        .limit(30)
-        .to_arrow()
-    )
-    assert len(result_explicit) == 30
-    with pytest.raises(
-        ValueError
-    ):  # This raises an error because vector query is provided without reanking query
-        table.search(query_vector).rerank(reranker=reranker).limit(30).to_arrow()
-
-    # FTS search setting
-    result = (
-        table.search(query, query_type="fts")
-        .rerank(reranker=reranker)
-        .limit(30)
-        .to_arrow()
-    )
-    assert len(result) > 0
-    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
-
-
-def test_cross_encoder_reranker(tmp_path):
-    pytest.importorskip("sentence_transformers")
-    reranker = CrossEncoderReranker()
-    table, schema = get_test_table(tmp_path)
-    result1 = (
-        table.search("Our father who art in heaven", query_type="hybrid")
-        .rerank(normalize="score", reranker=reranker)
-        .to_pydantic(schema)
-    )
-    result2 = (
-        table.search("Our father who art in heaven", query_type="hybrid")
-        .rerank(reranker=reranker)
-        .to_pydantic(schema)
-    )
-    assert result1 == result2
-
-    query = "Our father who art in heaven"
-    query_vector = table.to_pandas()["vector"][0]
-    result = (
-        table.search((query_vector, query), query_type="hybrid")
-        .limit(30)
-        .rerank(reranker=reranker)
-        .to_arrow()
-    )
-
-    assert len(result) == 30
-
-    err = (
-        "The _relevance_score column of the results returned by the reranker "
-        "represents the relevance of the result to the query & should "
-        "be descending."
-    )
-    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
-
-    # Vector search setting
-    result = table.search(query).rerank(reranker=reranker).limit(30).to_arrow()
-    assert len(result) == 30
-    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
-
-    result_explicit = (
-        table.search(query_vector)
-        .rerank(reranker=reranker, query_string=query)
-        .limit(30)
-        .to_arrow()
-    )
-    assert len(result_explicit) == 30
-    with pytest.raises(
-        ValueError
-    ):  # This raises an error because vector query is provided without reanking query
-        table.search(query_vector).rerank(reranker=reranker).limit(30).to_arrow()
-
-    # FTS search setting
-    result = (
-        table.search(query, query_type="fts")
-        .rerank(reranker=reranker)
-        .limit(30)
-        .to_arrow()
-    )
-    assert len(result) > 0
-    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
-
-
-def test_colbert_reranker(tmp_path):
-    pytest.importorskip("transformers")
-    reranker = ColbertReranker()
+def test_colbert_reranker(tmp_path, reranker):
     table, schema = get_test_table(tmp_path)
     result1 = (
         table.search("Our father who art in heaven", query_type="hybrid")
@@ -305,67 +186,3 @@ def test_colbert_reranker(tmp_path):
     )
     assert len(result) > 0
     assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
-
-
-@pytest.mark.skipif(
-    os.environ.get("OPENAI_API_KEY") is None, reason="OPENAI_API_KEY not set"
-)
-def test_openai_reranker(tmp_path):
-    pytest.importorskip("openai")
-    table, schema = get_test_table(tmp_path)
-    reranker = OpenaiReranker()
-    result1 = (
-        table.search("Our father who art in heaven", query_type="hybrid")
-        .rerank(normalize="score", reranker=reranker)
-        .to_pydantic(schema)
-    )
-    result2 = (
-        table.search("Our father who art in heaven", query_type="hybrid")
-        .rerank(reranker=OpenaiReranker())
-        .to_pydantic(schema)
-    )
-    assert result1 == result2
-
-    # test explicit hybrid query
-    query = "Our father who art in heaven"
-    query_vector = table.to_pandas()["vector"][0]
-    result = (
-        table.search((query_vector, query))
-        .limit(30)
-        .rerank(reranker=reranker)
-        .to_arrow()
-    )
-
-    assert len(result) == 30
-
-    err = (
-        "The _relevance_score column of the results returned by the reranker "
-        "represents the relevance of the result to the query & should "
-        "be descending."
-    )
-    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
-
-    # Vector search setting
-    result = table.search(query).rerank(reranker=reranker).limit(30).to_arrow()
-    assert len(result) == 30
-    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
-    result_explicit = (
-        table.search(query_vector)
-        .rerank(reranker=reranker, query_string=query)
-        .limit(30)
-        .to_arrow()
-    )
-    assert len(result_explicit) == 30
-    with pytest.raises(
-        ValueError
-    ):  # This raises an error because vector query is provided without reanking query
-        table.search(query_vector).rerank(reranker=reranker).limit(30).to_arrow()
-    # FTS search setting
-    result = (
-        table.search(query, query_type="fts")
-        .rerank(reranker=reranker)
-        .limit(30)
-        .to_arrow()
-    )
-    assert len(result) > 0
-    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err