The Best Embedding Model for Code in 2026: zembed-1 Tops the Leaderboard

What Makes Code Retrieval Hard

Developers building code search systems quickly discover that the problem has layers:

zembed-1 was designed to handle all of these — and the benchmarks confirm it.

Code Benchmark Performance

The code domain shows the tightest competitive spread across all benchmarked categories, reflecting that this is a domain where top models have invested significant effort. Even here, zembed-1 claims the top position.

Why zembed-1 Excels at Code Retrieval

zELO: Understanding Code Relevance, Not Just Code Similarity

The central challenge in code retrieval is that two snippets can be textually very different and semantically very similar, or textually similar and semantically unrelated. A function that sorts a list using bubble sort and one using mergesort may look different but accomplish the same thing. A function called process_data might be completely irrelevant to what the user is looking for.

32k Token Context for Full-Function Embedding

Code snippets vary enormously in length. A relevant function might be 10 lines or 500 lines. zembed-1’s 32,768-token context window allows full functions, classes, and even small modules to be embedded as coherent units — preserving the structural and logical context that makes code retrieval work.

Models with shorter context windows force chunking strategies that break functions mid-logic, lose the relationship between functions and their docstrings, and separate a method from its class context. zembed-1 can handle full code units.

Multilingual Code, Multilingual Queries

zembed-1 was trained with more than 50% non-English data, making it capable of retrieving code from natural language queries in any major language. A developer working in Japanese can search a Python codebase in Japanese and retrieve relevant results — without a translation layer.

Developer AI Use Cases

Getting Started

zembed-1 is available open-weight on HuggingFace (CC-BY-NC-4.0) and via the ZeroEntropy API for production deployments.

from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
    "zeroentropy/zembed-1",
    trust_remote_code=True,
    model_kwargs={"torch_dtype": "bfloat16"},
)

# Code search: natural language query against code corpus
query_embeddings = model.encode_query(
    "Function that validates an email address using regex"
)

document_embeddings = model.encode_document([
    """def validate_email(email: str) -> bool:
    import re
    pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
    return bool(re.match(pattern, email))""",

    """def check_user_input(data: dict) -> bool:
    # Validates user registration form
    if not data.get('username') or len(data['username']) < 3:
        return False
    return True""",
])

similarities = model.similarity(query_embeddings, document_embeddings)
# zembed-1 correctly ranks the email validation function highest

import ast
import textwrap

def extract_functions(source_code: str) -> list[dict]:
    """Parse Python source and extract individual functions with docstrings."""
    tree = ast.parse(source_code)
    functions = []
    for node in ast.walk(tree):
        if isinstance(node, (ast.FunctionDef, ast.AsyncFunctionDef)):
            docstring = ast.get_docstring(node) or ""
            body = textwrap.dedent(ast.unparse(node))
            functions.append({
                "name": node.name,
                "text": f"Function: {node.name}\n{docstring}\n{body}",
            })
    return functions

# Index your codebase function-by-function
all_functions = []
for filepath in get_python_files("./src"):  # your file walker
    with open(filepath) as f:
        all_functions.extend(extract_functions(f.read()))

func_texts = [fn["text"] for fn in all_functions]
func_embeddings = model.encode_document(func_texts, batch_size=32, show_progress_bar=True)

# Search at query time
def search_codebase(query: str, top_k: int = 5):
    q_emb = model.encode_query(query)
    scores = model.similarity(q_emb, func_embeddings)[0]
    top = scores.argsort(descending=True)[:top_k]
    return [(all_functions[i]["name"], float(scores[i])) for i in top]

results = search_codebase("retry logic with exponential backoff")
# Returns the most semantically relevant functions, even without matching keywords

The Verdict for Engineering Teams

If you’re building developer tooling — code search, AI coding assistants, documentation retrieval, or codebase intelligence platforms — zembed-1 gives you the best available code retrieval performance, period. Its top score on code benchmarks, combined with leadership across all other domains, means you don’t have to choose between a model that’s great for code and one that’s great for everything else.

With zembed-1, you get both.

from zeroentropy import ZeroEntropy
zclient = ZeroEntropy()
response = zclient.models.embed(
model="zembed-1",
input_type="query", # "query" or "document"
input="What is retrieval augmented generation?", # string or list[str]
dimensions=2560, # optional: must be one of [2560, 1280, 640, 320, 160, 80, 40]
encoding_format="float", # "float" or "base64"
latency="fast", # "fast" or "slow"; omit for auto
)