diff --git a/mteb/encoder_interface.py b/mteb/encoder_interface.py
index abb88f3ac4..6ebee5620a 100644
--- a/mteb/encoder_interface.py
+++ b/mteb/encoder_interface.py
@@ -14,7 +14,7 @@
 
 class PromptType(str, Enum):
     query = "query"
-    passage = "passage"
+    document = "document"
 
 
 @runtime_checkable
diff --git a/mteb/evaluation/evaluators/Image/Any2AnyRetrievalEvaluator.py b/mteb/evaluation/evaluators/Image/Any2AnyRetrievalEvaluator.py
index 06fb66c0a9..79388451ca 100644
--- a/mteb/evaluation/evaluators/Image/Any2AnyRetrievalEvaluator.py
+++ b/mteb/evaluation/evaluators/Image/Any2AnyRetrievalEvaluator.py
@@ -195,7 +195,7 @@ def search(
                 sub_corpus_embeddings = self.model.get_text_embeddings(
                     texts=corpus_texts,
                     task_name=task_name,
-                    prompt_type=PromptType.passage,
+                    prompt_type=PromptType.document,
                     **self.encode_kwargs,
                 )
             else:
@@ -213,7 +213,7 @@ def search(
                     sub_corpus_embeddings = self.model.get_image_embeddings(
                         images=corpus_image_dataloader,
                         task_name=task_name,
-                        prompt_type=PromptType.passage,
+                        prompt_type=PromptType.document,
                         **self.encode_kwargs,
                     )
                 elif corpus_modality == "image,text":
@@ -222,7 +222,7 @@ def search(
                         texts=corpus_texts,
                         images=corpus_image_dataloader,
                         task_name=task_name,
-                        prompt_type=PromptType.passage,
+                        prompt_type=PromptType.document,
                         **self.encode_kwargs,
                     )
                 else:
diff --git a/mteb/evaluation/evaluators/RerankingEvaluator.py b/mteb/evaluation/evaluators/RerankingEvaluator.py
index 3c45126bdf..259a57b782 100644
--- a/mteb/evaluation/evaluators/RerankingEvaluator.py
+++ b/mteb/evaluation/evaluators/RerankingEvaluator.py
@@ -180,7 +180,7 @@ def _encode_candidates_batched(
             all_docs,
             model,
             task_name=self.task_name,
-            prompt_type=PromptType.passage,
+            prompt_type=PromptType.document,
             **self.encode_kwargs,
         )
 
@@ -245,7 +245,7 @@ def _encode_candidates_individual(
                 model.encode(
                     docs,
                     task_name=self.task_name,
-                    prompt_type=PromptType.passage,
+                    prompt_type=PromptType.document,
                     **self.encode_kwargs,
                 )
             )
@@ -293,7 +293,7 @@ def _encode_candidates_miracl_batched(self, all_query_embs, model: Encoder):
             model.encode(
                 all_docs,
                 task_name=self.task_name,
-                prompt_type=PromptType.passage,
+                prompt_type=PromptType.document,
                 **self.encode_kwargs,
             )
         )
@@ -345,7 +345,7 @@ def _encode_candidates_miracl_individual(self, model: Encoder):
                     model.encode(
                         docs,
                         task_name=self.task_name,
-                        prompt_type=PromptType.passage,
+                        prompt_type=PromptType.document,
                         **self.encode_kwargs,
                     )
                 )
diff --git a/mteb/evaluation/evaluators/RetrievalEvaluator.py b/mteb/evaluation/evaluators/RetrievalEvaluator.py
index 69034b1741..9f06df2880 100644
--- a/mteb/evaluation/evaluators/RetrievalEvaluator.py
+++ b/mteb/evaluation/evaluators/RetrievalEvaluator.py
@@ -159,7 +159,7 @@ def search(
                 sub_corpus_embeddings = self.model.encode(
                     corpus[corpus_start_idx:corpus_end_idx],  # type: ignore
                     task_name=task_name,
-                    prompt_type=PromptType.passage,
+                    prompt_type=PromptType.document,
                     request_qid=request_qid,
                     **self.encode_kwargs,
                 )
@@ -385,7 +385,7 @@ def encode_corpus(
         corpus: list[dict[str, str]],
         task_name: str,
         batch_size: int,
-        prompt_type: PromptType = PromptType.passage,
+        prompt_type: PromptType = PromptType.document,
         request_qid: str | None = None,
         **kwargs,
     ):
@@ -416,7 +416,7 @@ def encode(
         prompt_type: PromptType | None = None,
         **kwargs,
     ):
-        if prompt_type and prompt_type == PromptType.passage:
+        if prompt_type and prompt_type == PromptType.document:
             return self.encode_corpus(
                 sentences, task_name, prompt_type=prompt_type, **kwargs
             )
diff --git a/mteb/models/cadet_models.py b/mteb/models/cadet_models.py
index c2717128cb..81e0dffdb0 100644
--- a/mteb/models/cadet_models.py
+++ b/mteb/models/cadet_models.py
@@ -35,7 +35,7 @@
         revision="8056d118be37a566f20972a5f35cda815f6bc47e",
         model_prompts={
             "query": "query: ",
-            "passage": "passage: ",
+            "document": "passage: ",
         },
     ),
     name="manveertamber/cadet-embed-base-v1",
diff --git a/mteb/models/cohere_models.py b/mteb/models/cohere_models.py
index 2a40f47218..606195417a 100644
--- a/mteb/models/cohere_models.py
+++ b/mteb/models/cohere_models.py
@@ -211,7 +211,7 @@ def encode(
     "MultilabelClassification": "classification",
     "Clustering": "clustering",
     PromptType.query.value: "search_query",
-    PromptType.passage.value: "search_document",
+    PromptType.document.value: "search_document",
 }
 
 cohere_mult_3 = ModelMeta(
diff --git a/mteb/models/e5_models.py b/mteb/models/e5_models.py
index 395d1da311..6a62f6e8fd 100644
--- a/mteb/models/e5_models.py
+++ b/mteb/models/e5_models.py
@@ -110,7 +110,7 @@
 
 model_prompts = {
     PromptType.query.value: "query: ",
-    PromptType.passage.value: "passage: ",
+    PromptType.document.value: "passage: ",
 }
 
 E5_TRAINING_DATA = {
diff --git a/mteb/models/gme_v_models.py b/mteb/models/gme_v_models.py
index 29e7de8c78..f3a2277ab8 100644
--- a/mteb/models/gme_v_models.py
+++ b/mteb/models/gme_v_models.py
@@ -182,7 +182,7 @@ def encode_corpus(self, corpus: list[dict[str, str]], **kwargs):
                 else doc["text"].strip()
                 for doc in corpus
             ]
-        embeddings = self.encode(sentences, prompt_type=PromptType.passage**kwargs)
+        embeddings = self.encode(sentences, prompt_type=PromptType.document**kwargs)
         return embeddings
 
     def get_image_embeddings(self, images: list[Image.Image] | DataLoader, **kwargs):
@@ -210,7 +210,7 @@ def get_fused_embeddings(
         instruction=None,
         **kwargs: Any,
     ):
-        if prompt_type == PromptType.passage:
+        if prompt_type == PromptType.document:
             instruction = None
         elif instruction is None:
             instruction = self.get_instruction(task_name, prompt_type)
diff --git a/mteb/models/google_models.py b/mteb/models/google_models.py
index 1ad9aa0a61..667bf31cda 100644
--- a/mteb/models/google_models.py
+++ b/mteb/models/google_models.py
@@ -39,7 +39,7 @@
     "Clustering": "CLUSTERING",
     "STS": "SEMANTIC_SIMILARITY",
     PromptType.query.value: "RETRIEVAL_QUERY",
-    PromptType.passage.value: "RETRIEVAL_DOCUMENT",
+    PromptType.document.value: "RETRIEVAL_DOCUMENT",
 }
 
 GECKO_TRAINING_DATA = {
diff --git a/mteb/models/instruct_wrapper.py b/mteb/models/instruct_wrapper.py
index 1cd7ae9db6..c5be2a672d 100644
--- a/mteb/models/instruct_wrapper.py
+++ b/mteb/models/instruct_wrapper.py
@@ -147,10 +147,13 @@ def encode(
         )
 
         # to passage prompts won't be applied to passages
-        if not self.apply_instruction_to_passages and prompt_type == PromptType.passage:
+        if (
+            not self.apply_instruction_to_passages
+            and prompt_type == PromptType.document
+        ):
             instruction = None
             logger.info(
-                f"No instruction used, because prompt type = {prompt_type.passage}"
+                f"No instruction used, because prompt type = {prompt_type.document}"
             )
 
         if instruction:
diff --git a/mteb/models/jasper_models.py b/mteb/models/jasper_models.py
index b3a3f62516..cfdc3849df 100644
--- a/mteb/models/jasper_models.py
+++ b/mteb/models/jasper_models.py
@@ -44,7 +44,7 @@ def encode(
         instruction = self.get_task_instruction(task_name, prompt_type)
 
         # to passage prompts won't be applied to passages
-        if prompt_type == PromptType.passage and task.metadata.category == "s2p":
+        if prompt_type == PromptType.document and task.metadata.category == "s2p":
             instruction = None
 
         embeddings = self.model.encode(
diff --git a/mteb/models/jina_models.py b/mteb/models/jina_models.py
index bbce9748af..30b7878583 100644
--- a/mteb/models/jina_models.py
+++ b/mteb/models/jina_models.py
@@ -269,8 +269,8 @@ def _resolve_task_parameters(
         # Determine prompt name parameter
         if jina_task_name and "query" in jina_task_name:
             prompt_name_param = "query"
-        elif jina_task_name and "passage" in jina_task_name:
-            prompt_name_param = "passage"
+        elif jina_task_name and "document" in jina_task_name:
+            prompt_name_param = "document"
         else:
             prompt_name_param = "query"  # default fallback
 
@@ -549,7 +549,7 @@ def get_programming_task_override(
         trust_remote_code=True,
         model_prompts={
             "Retrieval-query": "retrieval.query",
-            "Retrieval-passage": "retrieval.passage",
+            "Retrieval-document": "retrieval.passage",
             "STS": "text-matching",
             "DocumentUnderstanding": "retrieval.query",
         },
@@ -584,7 +584,7 @@ def get_programming_task_override(
         trust_remote_code=True,
         model_prompts={
             "Retrieval-query": "retrieval.query",
-            "Retrieval-passage": "retrieval.passage",
+            "Retrieval-document": "retrieval.passage",
             "Clustering": "separation",
             "Classification": "classification",
             "STS": "text-matching",
diff --git a/mteb/models/kalm_models.py b/mteb/models/kalm_models.py
index 68f17460c3..7ea133a84c 100644
--- a/mteb/models/kalm_models.py
+++ b/mteb/models/kalm_models.py
@@ -7,6 +7,7 @@
 
 import numpy as np
 import torch
+
 from mteb.encoder_interface import PromptType
 from mteb.model_meta import ModelMeta
 from mteb.models.instruct_wrapper import InstructSentenceTransformerWrapper
@@ -37,10 +38,13 @@ def encode(
         task = get_task(task_name)
 
         # to passage prompts won't be applied to passages
-        if not self.apply_instruction_to_passages and prompt_type == PromptType.passage:
+        if (
+            not self.apply_instruction_to_passages
+            and prompt_type == PromptType.document
+        ):
             instruction = None
             logger.info(
-                f"No instruction used, because prompt type = {prompt_type.passage}"
+                f"No instruction used, because prompt type = {prompt_type.document}"
             )
 
         if task.metadata.type in ["STS", "PairClassification", "Summarization"]:
@@ -375,26 +379,26 @@ def encode(
     "ThuNewsClusteringS2S": "Identify the topic or theme of the given news articles based on the titles",
     "ThuNewsClusteringP2P": "Identify the topic or theme of the given news articles based on the titles and contents",
     "Cmnli-query": "Retrieve semantically similar text",
-    "Cmnli-passage": "Retrieve semantically similar text",
+    "Cmnli-document": "Retrieve semantically similar text",
     "Ocnli-query": "Retrieve semantically similar text",
-    "Ocnli-passage": "Retrieve semantically similar text",
+    "Ocnli-document": "Retrieve semantically similar text",
     "SprintDuplicateQuestions-query": "Retrieve semantically similar questions",
-    "SprintDuplicateQuestions-passage": "Retrieve semantically similar questions",
+    "SprintDuplicateQuestions-document": "Retrieve semantically similar questions",
     "TwitterSemEval2015-query": "Retrieve semantically similar text",
-    "TwitterSemEval2015-passage": "Retrieve semantically similar text",
+    "TwitterSemEval2015-document": "Retrieve semantically similar text",
     "TwitterURLCorpus-query": "Retrieve semantically similar text",
-    "TwitterURLCorpus-passage": "Retrieve semantically similar text",
+    "TwitterURLCorpus-document": "Retrieve semantically similar text",
     "CMedQAv1-reranking": "Given a query, retrieve documents that answer the query",
     "CMedQAv2-reranking": "Given a query, retrieve documents that answer the query",
     "MMarcoReranking": "Given a query, retrieve documents that answer the query",
     "T2Reranking": "Given a query, retrieve documents that answer the query",
     "AskUbuntuDupQuestions-query": "Retrieve semantically similar questions",
-    "AskUbuntuDupQuestions-passage": "Retrieve semantically similar questions",
+    "AskUbuntuDupQuestions-document": "Retrieve semantically similar questions",
     "MindSmallReranking": "Given a query, retrieve documents that answer the query",
     "SciDocsRR-query": "Retrieve relevant paper titles",
-    "SciDocsRR-passage": "Retrieve relevant paper titles",
+    "SciDocsRR-document": "Retrieve relevant paper titles",
     "StackOverflowDupQuestions-query": "Retrieve semantically similar questions",
-    "StackOverflowDupQuestions-passage": "Retrieve semantically similar questions",
+    "StackOverflowDupQuestions-document": "Retrieve semantically similar questions",
     "CmedqaRetrieval": "Given a query, retrieve documents that answer the query",
     "CovidRetrieval": "Given a query, retrieve documents that answer the query",
     "DuRetrieval": "Given a query, retrieve documents that answer the query",
@@ -407,29 +411,29 @@ def encode(
     "ArguAna": "Given a query, retrieve documents that answer the query",
     "ClimateFEVER": "Given a query, retrieve documents that answer the query",
     "CQADupstackAndroidRetrieval-query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
-    "CQADupstackAndroidRetrieval-passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
+    "CQADupstackAndroidRetrieval-document": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
     "CQADupstackEnglishRetrieval-query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
-    "CQADupstackEnglishRetrieval-passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
+    "CQADupstackEnglishRetrieval-document": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
     "CQADupstackGamingRetrieval-query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
-    "CQADupstackGamingRetrieval-passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
+    "CQADupstackGamingRetrieval-document": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
     "CQADupstackGisRetrieval-query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
-    "CQADupstackGisRetrieval-passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
+    "CQADupstackGisRetrieval-document": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
     "CQADupstackMathematicaRetrieval-query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
-    "CQADupstackMathematicaRetrieval-passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
+    "CQADupstackMathematicaRetrieval-document": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
     "CQADupstackPhysicsRetrieval-query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
-    "CQADupstackPhysicsRetrieval-passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
+    "CQADupstackPhysicsRetrieval-document": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
     "CQADupstackProgrammersRetrieval-query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
-    "CQADupstackProgrammersRetrieval-passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
+    "CQADupstackProgrammersRetrieval-document": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
     "CQADupstackStatsRetrieval-query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
-    "CQADupstackStatsRetrieval-passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
+    "CQADupstackStatsRetrieval-document": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
     "CQADupstackTexRetrieval-query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
-    "CQADupstackTexRetrieval-passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
+    "CQADupstackTexRetrieval-document": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
     "CQADupstackUnixRetrieval-query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
-    "CQADupstackUnixRetrieval-passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
+    "CQADupstackUnixRetrieval-document": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
     "CQADupstackWebmastersRetrieval-query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
-    "CQADupstackWebmastersRetrieval-passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
+    "CQADupstackWebmastersRetrieval-document": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
     "CQADupstackWordpressRetrieval-query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
-    "CQADupstackWordpressRetrieval-passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
+    "CQADupstackWordpressRetrieval-document": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
     "DBPedia": "Given a query, retrieve documents that answer the query",
     "FEVER": "Given a query, retrieve documents that answer the query",
     "FiQA2018": "Given a query, retrieve documents that answer the query",
@@ -437,48 +441,48 @@ def encode(
     "NFCorpus": "Given a query, retrieve documents that answer the query",
     "NQ": "Given a query, retrieve documents that answer the query",
     "QuoraRetrieval-query": "Retrieve semantically similar questions",
-    "QuoraRetrieval-passage": "Retrieve semantically similar questions",
+    "QuoraRetrieval-document": "Retrieve semantically similar questions",
     "SCIDOCS-query": "Given a query, retrieve documents that answer the query",
-    "SCIDOCS-passage": "Given a query, retrieve documents that answer the query",
+    "SCIDOCS-document": "Given a query, retrieve documents that answer the query",
     "SciFact": "Given a query, retrieve documents that answer the query",
     "Touche2020": "Given a query, retrieve documents that answer the query",
     "TRECCOVID": "Given a query, retrieve documents that answer the query",
     "AFQMC-query": "Retrieve semantically similar text",
-    "AFQMC-passage": "Retrieve semantically similar text",
+    "AFQMC-document": "Retrieve semantically similar text",
     "ATEC-query": "Retrieve semantically similar text",
-    "ATEC-passage": "Retrieve semantically similar text",
+    "ATEC-document": "Retrieve semantically similar text",
     "BQ-query": "Retrieve semantically similar text",
-    "BQ-passage": "Retrieve semantically similar text",
+    "BQ-document": "Retrieve semantically similar text",
     "LCQMC-query": "Retrieve semantically similar text",
-    "LCQMC-passage": "Retrieve semantically similar text",
+    "LCQMC-document": "Retrieve semantically similar text",
     "PAWSX-query": "Retrieve semantically similar text",
-    "PAWSX-passage": "Retrieve semantically similar text",
+    "PAWSX-document": "Retrieve semantically similar text",
     "QBQTC-query": "Retrieve semantically similar text",
-    "QBQTC-passage": "Retrieve semantically similar text",
+    "QBQTC-document": "Retrieve semantically similar text",
     "STSB-query": "Retrieve semantically similar text",
-    "STSB-passage": "Retrieve semantically similar text",
+    "STSB-document": "Retrieve semantically similar text",
     "BIOSSES-query": "Retrieve semantically similar text",
-    "BIOSSES-passage": "Retrieve semantically similar text",
+    "BIOSSES-document": "Retrieve semantically similar text",
     "SICK-R-query": "Retrieve semantically similar text",
-    "SICK-R-passage": "Retrieve semantically similar text",
+    "SICK-R-document": "Retrieve semantically similar text",
     "STS12-query": "Retrieve semantically similar text",
-    "STS12-passage": "Retrieve semantically similar text",
+    "STS12-document": "Retrieve semantically similar text",
     "STS13-query": "Retrieve semantically similar text",
-    "STS13-passage": "Retrieve semantically similar text",
+    "STS13-document": "Retrieve semantically similar text",
     "STS14-query": "Retrieve semantically similar text",
-    "STS14-passage": "Retrieve semantically similar text",
+    "STS14-document": "Retrieve semantically similar text",
     "STS15-query": "Retrieve semantically similar text",
-    "STS15-passage": "Retrieve semantically similar text",
+    "STS15-document": "Retrieve semantically similar text",
     "STS16-query": "Retrieve semantically similar text",
-    "STS16-passage": "Retrieve semantically similar text",
+    "STS16-document": "Retrieve semantically similar text",
     "STS17-query": "Retrieve semantically similar text",
-    "STS17-passage": "Retrieve semantically similar text",
+    "STS17-document": "Retrieve semantically similar text",
     "STS22-query": "Retrieve semantically similar text",
-    "STS22-passage": "Retrieve semantically similar text",
+    "STS22-document": "Retrieve semantically similar text",
     "STSBenchmark-query": "Retrieve semantically similar text",
-    "STSBenchmark-passage": "Retrieve semantically similar text",
+    "STSBenchmark-document": "Retrieve semantically similar text",
     "SummEval-query": "Retrieve semantically similar summaries",
-    "SummEval-passage": "Retrieve semantically similar summaries",
+    "SummEval-document": "Retrieve semantically similar summaries",
 }
 
 KaLM_X_task_prompts = {
@@ -530,7 +534,7 @@ def encode(
     "CMedQAv1-reranking-query": "Given a Chinese community medical question, retrieve replies that best answer the question",
     "CMedQAv2-reranking-query": "Given a Chinese community medical question, retrieve replies that best answer the question",
     "ArguAna-query": "Given a claim, find documents that refute the claim",
-    "ArguAna-passage": "Given a claim, find documents that refute the claim",
+    "ArguAna-document": "Given a claim, find documents that refute the claim",
     "ClimateFEVER-query": "Given a claim about climate change, retrieve documents that support or refute the claim",
     "ClimateFEVERHardNegatives-query": "Given a claim about climate change, retrieve documents that support or refute the claim",
     "DBPedia-query": "Given a query, retrieve relevant entity descriptions from DBPedia",
@@ -677,9 +681,9 @@ def encode(
     "MIRACLRetrievalHardNegatives-query": "Retrieval relevant passage for the given query",
     "CQADupstackRetrieval-query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
     "CQADupstackGamingRetrieval-query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
-    "CQADupstackGamingRetrieval-passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
+    "CQADupstackGamingRetrieval-document": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
     "CQADupstackUnixRetrieval-query": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
-    "CQADupstackUnixRetrieval-passage": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
+    "CQADupstackUnixRetrieval-document": "Given a question, retrieve detailed question descriptions from Stackexchange that are duplicates to the given question",
 }
 
 KaLM_INSTRUCTION = "Instruct: {instruction} \n Query: "
diff --git a/mteb/models/mcinext_models.py b/mteb/models/mcinext_models.py
index a5f02279ca..ce70a4bbb9 100644
--- a/mteb/models/mcinext_models.py
+++ b/mteb/models/mcinext_models.py
@@ -256,7 +256,7 @@ def _preprocess_sample(
         if task_id == 3:
             if sub == "sentence1" or (prompt_type and prompt_type.value == "query"):
                 return f"{task_prompt} | متن اول : {sample}"
-            if sub == "sentence2" or (prompt_type and prompt_type.value == "passage"):
+            if sub == "sentence2" or (prompt_type and prompt_type.value == "document"):
                 return f"{task_prompt} | متن دوم : {sample}"
         return sample
 
diff --git a/mteb/models/nomic_models.py b/mteb/models/nomic_models.py
index f02157aa09..78d7163ef8 100644
--- a/mteb/models/nomic_models.py
+++ b/mteb/models/nomic_models.py
@@ -56,7 +56,7 @@ def encode(  # type: ignore
         # default to search_document if input_type and prompt_name are not provided
         prompt_name = (
             self.get_prompt_name(self.model_prompts, task_name, prompt_type)
-            or PromptType.passage.value
+            or PromptType.document.value
         )
         task = mteb.get_task(task_name)
         # normalization not applied to classification
@@ -177,7 +177,7 @@ def encode(  # type: ignore
     "STS": "classification: ",
     "Summarization": "classification: ",
     PromptType.query.value: "search_query: ",
-    PromptType.passage.value: "search_document: ",
+    PromptType.document.value: "search_document: ",
 }
 
 nomic_embed_v1_5 = ModelMeta(
diff --git a/mteb/models/qwen3_models.py b/mteb/models/qwen3_models.py
index 3571cc99c1..33862939b4 100644
--- a/mteb/models/qwen3_models.py
+++ b/mteb/models/qwen3_models.py
@@ -11,7 +11,7 @@
 def instruction_template(
     instruction: str, prompt_type: PromptType | None = None
 ) -> str:
-    if not instruction or prompt_type == PromptType.passage:
+    if not instruction or prompt_type == PromptType.document:
         return ""
     if isinstance(instruction, dict):
         if prompt_type is None:
diff --git a/mteb/models/repllama_models.py b/mteb/models/repllama_models.py
index eed13cd71b..e704cb865a 100644
--- a/mteb/models/repllama_models.py
+++ b/mteb/models/repllama_models.py
@@ -121,7 +121,7 @@ def loader_inner(**kwargs: Any) -> Encoder:
 
 model_prompts = {
     PromptType.query.value: "query:  ",
-    PromptType.passage.value: "passage:  ",
+    PromptType.document.value: "passage:  ",
 }
 
 repllama_llama2_original = ModelMeta(
diff --git a/mteb/models/ru_sentence_models.py b/mteb/models/ru_sentence_models.py
index 2a9c7063a4..9c3be1a76a 100644
--- a/mteb/models/ru_sentence_models.py
+++ b/mteb/models/ru_sentence_models.py
@@ -35,23 +35,23 @@
     "SensitiveTopicsClassification": "Классифицируй чувствительную тему по запросу \nзапрос: ",
     "RuBQRetrieval": {
         "query": "Given a question, retrieve Wikipedia passages that answer the question\nquery: ",
-        "passage": "",
+        "document": "",
     },
     "RuBQReranking": {
         "query": "Given a question, retrieve Wikipedia passages that answer the question\nquery: ",
-        "passage": "",
+        "document": "",
     },
     "RiaNewsRetrieval": {
         "query": "Given a news title, retrieve relevant news article\nquery: ",
-        "passage": "",
+        "document": "",
     },
     "MIRACLReranking": {
         "query": "Given a question, retrieve Wikipedia passages that answer the question\nquery: ",
-        "passage": "",
+        "document": "",
     },
     "MIRACLRetrieval": {
         "query": "Given a question, retrieve Wikipedia passages that answer the question\nquery: ",
-        "passage": "",
+        "document": "",
     },
 }
 
@@ -156,7 +156,7 @@
         sentence_transformers_loader,
         model_name="deepvk/USER-base",
         revision="436a489a2087d61aa670b3496a9915f84e46c861",
-        model_prompts={"query": "query: ", "passage": "passage: "},
+        model_prompts={"query": "query: ", "document": "passage: "},
     ),
     name="deepvk/USER-base",
     languages=["rus-Cyrl"],
@@ -431,11 +431,11 @@
     "PairClassification": "classification: ",
     "Reranking": "classification: ",
     f"Reranking-{PromptType.query.value}": "search_query: ",
-    f"Reranking-{PromptType.passage.value}": "search_document: ",
+    f"Reranking-{PromptType.document.value}": "search_document: ",
     "STS": "classification: ",
     "Summarization": "classification: ",
     PromptType.query.value: "search_query: ",
-    PromptType.passage.value: "search_document: ",
+    PromptType.document.value: "search_document: ",
     # Override some prompts for ruMTEB tasks
     "HeadlineClassification": "clustering: ",
     "InappropriatenessClassification": "clustering: ",
@@ -497,11 +497,11 @@
     "PairClassification": "paraphrase: ",
     "Reranking": "paraphrase: ",
     f"Reranking-{PromptType.query.value}": "search_query: ",
-    f"Reranking-{PromptType.passage.value}": "search_document: ",
+    f"Reranking-{PromptType.document.value}": "search_document: ",
     "STS": "paraphrase: ",
     "Summarization": "categorize: ",
     PromptType.query.value: "search_query: ",
-    PromptType.passage.value: "search_document: ",
+    PromptType.document.value: "search_document: ",
     # Override some prompts for ruMTEB tasks
     "CEDRClassification": "categorize_sentiment: ",
     "GeoreviewClassification": "categorize_sentiment: ",
@@ -751,11 +751,11 @@
     "PairClassification": "classification: ",
     "Reranking": "classification: ",
     f"Reranking-{PromptType.query.value}": "search_query: ",
-    f"Reranking-{PromptType.passage.value}": "search_document: ",
+    f"Reranking-{PromptType.document.value}": "search_document: ",
     "STS": "classification: ",
     "Summarization": "clustering: ",
     PromptType.query.value: "search_query: ",
-    PromptType.passage.value: "search_document: ",
+    PromptType.document.value: "search_document: ",
 }
 user2_small = ModelMeta(
     loader=partial(
diff --git a/mteb/models/sentence_transformer_wrapper.py b/mteb/models/sentence_transformer_wrapper.py
index 872a3fffe9..e9d5492803 100644
--- a/mteb/models/sentence_transformer_wrapper.py
+++ b/mteb/models/sentence_transformer_wrapper.py
@@ -45,6 +45,13 @@ def __init__(
         ):
             try:
                 model_prompts = self.validate_task_to_prompt_name(self.model.prompts)
+
+                if (
+                    len(self.model.prompts) == 2
+                    and self.model.prompts.get("query", "") == ""
+                    and self.model.prompts.get("document", "") == ""
+                ):
+                    model_prompts = None
             except KeyError:
                 model_prompts = None
                 logger.warning(
@@ -89,24 +96,24 @@ def encode(
         Returns:
             The encoded sentences.
         """
+        prompt = None
         prompt_name = None
         if self.model_prompts is not None:
             prompt_name = self.get_prompt_name(
                 self.model_prompts, task_name, prompt_type
             )
+            prompt = self.model_prompts.get(prompt_name, None)
         if prompt_name:
             logger.info(
-                f"Using prompt_name={prompt_name} for task={task_name} prompt_type={prompt_type}"
+                f"Using {prompt_name=} for task={task_name} {prompt_type=} with {prompt=}"
             )
         else:
-            logger.info(
-                f"No model prompts found for task={task_name} prompt_type={prompt_type}"
-            )
+            logger.info(f"No model prompts found for task={task_name} {prompt_type=}")
         logger.info(f"Encoding {len(sentences)} sentences.")
 
         embeddings = self.model.encode(
             sentences,
-            prompt_name=prompt_name,
+            prompt=prompt,
             **kwargs,
         )
         if isinstance(embeddings, torch.Tensor):
diff --git a/mteb/models/vlm2vec_models.py b/mteb/models/vlm2vec_models.py
index 65ca7b4004..38823d77a7 100644
--- a/mteb/models/vlm2vec_models.py
+++ b/mteb/models/vlm2vec_models.py
@@ -20,7 +20,7 @@
 
 logger = logging.getLogger(__name__)
 
-EncodeTypes = Literal["query", "passage"]
+EncodeTypes = Literal["query", "document"]
 
 
 class VLM2VecWrapper:
diff --git a/mteb/models/voyage_models.py b/mteb/models/voyage_models.py
index b1eb33442a..c8c58baef4 100644
--- a/mteb/models/voyage_models.py
+++ b/mteb/models/voyage_models.py
@@ -140,7 +140,7 @@ def _batched_encode(
 
 model_prompts = {
     PromptType.query.value: "query",
-    PromptType.passage.value: "document",
+    PromptType.document.value: "document",
 }
 
 voyage_large_2_instruct = ModelMeta(
diff --git a/mteb/models/voyage_v.py b/mteb/models/voyage_v.py
index 48a083574c..fada131681 100644
--- a/mteb/models/voyage_v.py
+++ b/mteb/models/voyage_v.py
@@ -87,7 +87,7 @@ def get_text_embeddings(
             **kwargs: Any,
         ):
             if input_type is None and prompt_type is not None:
-                if prompt_type == PromptType.passage:
+                if prompt_type == PromptType.document:
                     input_type = "document"
                 elif prompt_type == PromptType.query:
                     input_type = "query"
@@ -119,7 +119,7 @@ def get_image_embeddings(
             **kwargs: Any,
         ):
             if input_type is None and prompt_type is not None:
-                if prompt_type == PromptType.passage:
+                if prompt_type == PromptType.document:
                     input_type = "document"
                 elif prompt_type == PromptType.query:
                     input_type = "query"
@@ -175,7 +175,7 @@ def get_fused_embeddings(
                 raise ValueError("Either texts or images must be provided")
 
             if input_type is None and prompt_type is not None:
-                if prompt_type == PromptType.passage:
+                if prompt_type == PromptType.document:
                     input_type = "document"
                 elif prompt_type == PromptType.query:
                     input_type = "query"
diff --git a/mteb/models/wrapper.py b/mteb/models/wrapper.py
index 73ebdde341..68ec09ae62 100644
--- a/mteb/models/wrapper.py
+++ b/mteb/models/wrapper.py
@@ -35,7 +35,7 @@ def get_prompt_name(
         Args:
             task_to_prompt: The tasks names and their corresponding prompt_names
             task_name: The task name to use for building the encoding prompt
-            prompt_type: The prompt type (e.g. "query" | "passage") to use for building the encoding prompt
+            prompt_type: The prompt type (e.g. "query" | "document") to use for building the encoding prompt
         """
         task = mteb.get_task(task_name=task_name)
         task_type = task.metadata.type
@@ -74,7 +74,7 @@ def validate_task_to_prompt_name(
         prompt_types = [e.value for e in PromptType]
         for task_name in task_to_prompt_name:
             if "-" in task_name and task_name.endswith(
-                (f"-{PromptType.query.value}", f"-{PromptType.passage.value}")
+                (f"-{PromptType.query.value}", f"-{PromptType.document.value}")
             ):
                 task_name, prompt_type = task_name.rsplit("-", 1)
                 if prompt_type not in prompt_types:
diff --git a/tests/test_benchmark/mock_models.py b/tests/test_benchmark/mock_models.py
index 8fd45fd6bd..4feb6820a0 100644
--- a/tests/test_benchmark/mock_models.py
+++ b/tests/test_benchmark/mock_models.py
@@ -171,7 +171,7 @@ def __init__(
             model: The SentenceTransformer model to use. Can be a string (model name), a SentenceTransformer model, or a CrossEncoder model.
             revision: The revision of the model to use.
             model_prompts: A dictionary mapping task names to prompt names.
-                First priority is given to the composed prompt of task name + prompt type (query or passage), then to the specific task prompt,
+                First priority is given to the composed prompt of task name + prompt type (query or document), then to the specific task prompt,
                 then to the composed prompt of task type + prompt type, then to the specific task type prompt,
                 and finally to the specific prompt type.
             **kwargs: Additional arguments to pass to the SentenceTransformer model.
diff --git a/tests/test_benchmark/test_benchmark.py b/tests/test_benchmark/test_benchmark.py
index 11b5f4cc7f..69487b2805 100644
--- a/tests/test_benchmark/test_benchmark.py
+++ b/tests/test_benchmark/test_benchmark.py
@@ -115,7 +115,7 @@ def encode(self, sentences, prompt_name: str | None = None, **kwargs):
 
     class EncoderWithoutInstructions(MockSentenceTransformer):
         def encode(self, sentences, **kwargs):
-            assert kwargs["prompt_name"] is None
+            assert kwargs["prompt"] is None
             return super().encode(sentences, **kwargs)
 
     if isinstance(task_name, mteb.AbsTask):
@@ -302,7 +302,7 @@ def encode(self, sentences, prompt_name: str | None = None, **kwargs):
     )
 
 
-@pytest.mark.parametrize("task_name", ["NQ-NL-query", "NQ-NL-passage"])
+@pytest.mark.parametrize("task_name", ["NQ-NL-query", "NQ-NL-document"])
 def test_prompt_name_split_correctly(task_name: str, tmp_path: Path):
     """Test that the task name is split correctly into task name and prompt type
     for tasks with multiple `-` in their names.
@@ -331,12 +331,12 @@ def test_model_query_passage_prompts_task_type(
     task_name = task.metadata.name if is_task_name else task.metadata.type
 
     def check_prompt(prompt_name, is_query):
-        prompt_type = "query" if is_query else "passage"
+        prompt_type = "query" if is_query else "document"
         assert prompt_name == f"{task_name}-{prompt_type}"
 
     prompt_list = {
         f"{task_name}-query": "query",
-        f"{task_name}-passage": "passage",
+        f"{task_name}-document": "document",
     }
 
     class MockEncoderWithPrompts(mteb.Encoder):
diff --git a/tests/test_reproducible_workflow.py b/tests/test_reproducible_workflow.py
index d584a98852..738392c623 100644
--- a/tests/test_reproducible_workflow.py
+++ b/tests/test_reproducible_workflow.py
@@ -57,10 +57,12 @@ def test_validate_task_to_prompt_name(task_name: str | mteb.AbsTask):
 
     model_prompts = dict.fromkeys(task_names, "prompt_name")
     model_prompts |= {task_name + "-query": "prompt_name" for task_name in task_names}
-    model_prompts |= {task_name + "-passage": "prompt_name" for task_name in task_names}
+    model_prompts |= {
+        task_name + "-document": "prompt_name" for task_name in task_names
+    }
     model_prompts |= {
         "query": "prompt_name",
-        "passage": "prompt_name",
+        "document": "prompt_name",
     }
     Wrapper.validate_task_to_prompt_name(model_prompts)