diff --git a/mteb/model_meta.py b/mteb/model_meta.py
index 7d6b6276a6..22e5d85ba6 100644
--- a/mteb/model_meta.py
+++ b/mteb/model_meta.py
@@ -89,6 +89,7 @@ class ModelMeta(BaseModel):
             a benchmark as well as mark dataset contaminations.
         adapted_from: Name of the model from which this model is adapted. For quantizations, fine-tunes, long doc extensions, etc.
         superseded_by: Name of the model that supersedes this model, e.g., nvidia/NV-Embed-v2 supersedes v1.
+        is_cross_encoder: Whether the model can act as a cross-encoder or not.
         modalities: A list of strings representing the modalities the model supports. Default is ["text"].
     """
 
@@ -114,6 +115,7 @@ class ModelMeta(BaseModel):
     training_datasets: dict[str, list[str]] | None
     adapted_from: str | None = None
     superseded_by: str | None = None
+    is_cross_encoder: bool | None = None
     modalities: list[MODALITIES] = ["text"]
 
     def to_dict(self):
diff --git a/mteb/models/rerankers_custom.py b/mteb/models/rerankers_custom.py
index 7c966cdf78..0061ffc1c3 100644
--- a/mteb/models/rerankers_custom.py
+++ b/mteb/models/rerankers_custom.py
@@ -216,6 +216,7 @@ def loader_inner(**kwargs: Any) -> Encoder:
     use_instructions=None,
     training_datasets=None,
     framework=["Sentence Transformers", "PyTorch"],
+    is_cross_encoder=True,
 )
 
 # languages unclear: https://huggingface.co/jinaai/jina-reranker-v2-base-multilingual/discussions/28
@@ -242,6 +243,7 @@ def loader_inner(**kwargs: Any) -> Encoder:
     use_instructions=None,
     training_datasets=None,
     framework=["Sentence Transformers", "PyTorch"],
+    is_cross_encoder=True,
 )
 
 bge_reranker_v2_m3 = ModelMeta(
@@ -300,4 +302,5 @@ def loader_inner(**kwargs: Any) -> Encoder:
     use_instructions=None,
     training_datasets=bge_m3_training_data,
     framework=["Sentence Transformers", "PyTorch"],
+    is_cross_encoder=True,
 )
diff --git a/mteb/models/rerankers_monot5_based.py b/mteb/models/rerankers_monot5_based.py
index afc31e5a2d..f94508c548 100644
--- a/mteb/models/rerankers_monot5_based.py
+++ b/mteb/models/rerankers_monot5_based.py
@@ -307,6 +307,7 @@ def get_prediction_tokens(self, *args, **kwargs):
     use_instructions=None,
     training_datasets=None,
     framework=["PyTorch"],
+    is_cross_encoder=True,
 )
 
 monot5_base = ModelMeta(
@@ -332,6 +333,7 @@ def get_prediction_tokens(self, *args, **kwargs):
     use_instructions=None,
     training_datasets=None,
     framework=["PyTorch"],
+    is_cross_encoder=True,
 )
 
 monot5_large = ModelMeta(
@@ -357,6 +359,7 @@ def get_prediction_tokens(self, *args, **kwargs):
     use_instructions=None,
     training_datasets=None,
     framework=["PyTorch"],
+    is_cross_encoder=True,
 )
 
 monot5_3b = ModelMeta(
@@ -382,6 +385,7 @@ def get_prediction_tokens(self, *args, **kwargs):
     use_instructions=None,
     training_datasets=None,
     framework=["PyTorch"],
+    is_cross_encoder=True,
 )
 
 flant5_base = ModelMeta(
@@ -408,16 +412,17 @@ def get_prediction_tokens(self, *args, **kwargs):
         "quasc": ["train"],
         "qed": ["train"],
     },
-    n_parameters=None,
+    n_parameters=248_000_000,
     memory_usage_mb=944,
     max_tokens=None,
-    embed_dim=None,
-    license=None,
+    embed_dim=768,
+    license="apache-2.0",
     public_training_code=None,
     public_training_data=None,
     similarity_fn_name=None,
-    use_instructions=None,
+    use_instructions=True,
     framework=["PyTorch"],
+    is_cross_encoder=True,
 )
 
 flant5_large = ModelMeta(
@@ -444,16 +449,17 @@ def get_prediction_tokens(self, *args, **kwargs):
         "quasc": ["train"],
         "qed": ["train"],
     },
-    n_parameters=None,
+    n_parameters=783_000_000,
+    max_tokens=1024,
     memory_usage_mb=2987,
-    max_tokens=None,
     embed_dim=None,
-    license=None,
+    license="apache-2.0",
     public_training_code=None,
     public_training_data=None,
     similarity_fn_name=None,
     use_instructions=None,
     framework=["PyTorch"],
+    is_cross_encoder=True,
 )
 
 flant5_xl = ModelMeta(
@@ -480,16 +486,17 @@ def get_prediction_tokens(self, *args, **kwargs):
         "quasc": ["train"],
         "qed": ["train"],
     },
-    n_parameters=None,
+    n_parameters=2_850_000_000,
     memory_usage_mb=10871,
     max_tokens=None,
-    embed_dim=None,
-    license=None,
+    embed_dim=2048,
+    license="apache-2.0",
     public_training_code=None,
     public_training_data=None,
     similarity_fn_name=None,
     use_instructions=None,
     framework=["PyTorch"],
+    is_cross_encoder=True,
 )
 
 flant5_xxl = ModelMeta(
@@ -516,16 +523,17 @@ def get_prediction_tokens(self, *args, **kwargs):
         "quasc": ["train"],
         "qed": ["train"],
     },
-    n_parameters=None,
+    n_parameters=11_300_000_000,
     memory_usage_mb=42980,
     max_tokens=None,
-    embed_dim=None,
-    license=None,
+    embed_dim=4096,
+    license="apache-2.0",
     public_training_code=None,
     public_training_data=None,
     similarity_fn_name=None,
     use_instructions=None,
     framework=["PyTorch"],
+    is_cross_encoder=True,
 )
 
 
@@ -541,17 +549,18 @@ def get_prediction_tokens(self, *args, **kwargs):
     open_weights=True,
     revision="01c7f73d771dfac7d292323805ebc428287df4f9",
     release_date="2023-07-18",
-    n_parameters=None,
+    n_parameters=6_740_000_000,
     memory_usage_mb=None,
     max_tokens=None,
     embed_dim=None,
-    license=None,
+    license=None,  # llama2
     public_training_code=None,
     public_training_data=None,
     similarity_fn_name=None,
     use_instructions=None,
     training_datasets=None,
     framework=["PyTorch"],
+    is_cross_encoder=True,
 )
 
 llama2_7b_chat = ModelMeta(
@@ -577,6 +586,7 @@ def get_prediction_tokens(self, *args, **kwargs):
     use_instructions=None,
     training_datasets=None,
     framework=["PyTorch"],
+    is_cross_encoder=True,
 )
 
 mistral_7b = ModelMeta(
@@ -602,6 +612,7 @@ def get_prediction_tokens(self, *args, **kwargs):
     use_instructions=None,
     training_datasets=None,
     framework=["PyTorch"],
+    is_cross_encoder=True,
 )
 
 followir_7b = ModelMeta(
@@ -617,16 +628,17 @@ def get_prediction_tokens(self, *args, **kwargs):
     revision="4d25d437e38b510c01852070c0731e8f6e1875d1",
     release_date="2024-04-29",
     training_datasets={"jhu-clsp/FollowIR-train": ["train"]},
-    n_parameters=None,
+    n_parameters=7_240_000_000,
     memory_usage_mb=13813,
     max_tokens=None,
     embed_dim=None,
-    license=None,
+    license="apache-2.0",
     public_training_code=None,
     public_training_data=None,
     similarity_fn_name=None,
     use_instructions=None,
     framework=["PyTorch"],
+    is_cross_encoder=True,
 )
 
 
@@ -751,12 +763,13 @@ def get_prediction_tokens(self, *args, **kwargs):
     memory_usage_mb=None,
     max_tokens=None,
     embed_dim=None,
-    license=None,
+    license="mit",
     public_training_code=None,
     public_training_data=None,
     similarity_fn_name=None,
     use_instructions=None,
     framework=["PyTorch"],
+    is_cross_encoder=True,
 )
 
 mt5_13b_mmarco_100k = ModelMeta(
@@ -782,4 +795,5 @@ def get_prediction_tokens(self, *args, **kwargs):
     use_instructions=None,
     training_datasets=None,
     framework=["PyTorch"],
+    is_cross_encoder=True,
 )