diff --git a/src/veritext/metrics/__init__.py b/src/veritext/metrics/__init__.py
new file mode 100644
index 0000000..9fd2fc9
--- /dev/null
+++ b/src/veritext/metrics/__init__.py
@@ -0,0 +1,16 @@
+"""Metrics module: BLEU, lexical similarity, and batch processing."""
+
+from veritext.metrics.base import AggregateStats, BatchResult, Metric
+from veritext.metrics.bleu import Bleu
+from veritext.metrics.lexical import Lexical
+from veritext.metrics.results import BleuResult, LexicalResult
+
+__all__ = [
+    "AggregateStats",
+    "BatchResult",
+    "Bleu",
+    "BleuResult",
+    "Lexical",
+    "LexicalResult",
+    "Metric",
+]
diff --git a/src/veritext/metrics/bleu.py b/src/veritext/metrics/bleu.py
new file mode 100644
index 0000000..050f72c
--- /dev/null
+++ b/src/veritext/metrics/bleu.py
@@ -0,0 +1,210 @@
+"""BLEU (Bilingual Evaluation Understudy) metric implementation."""
+
+import math
+from collections import Counter
+
+from veritext.core.tokenisation import WordTokeniser
+from veritext.metrics.base import AggregateStats, BatchResult
+from veritext.metrics.results import BleuResult
+
+
+def _get_ngrams(tokens: list[str], n: int) -> Counter[tuple[str, ...]]:
+    """Extract n-grams from a list of tokens."""
+    if n > len(tokens):
+        return Counter()
+    return Counter(tuple(tokens[i : i + n]) for i in range(len(tokens) - n + 1))
+
+
+def _modified_precision(
+    candidate_tokens: list[str],
+    reference_token_lists: list[list[str]],
+    n: int,
+) -> tuple[int, int]:
+    """
+    Compute modified n-gram precision (clipped counts).
+
+    Returns:
+        Tuple of (clipped_count, total_count).
+    """
+    candidate_ngrams = _get_ngrams(candidate_tokens, n)
+    if not candidate_ngrams:
+        return 0, 0
+
+    # Get max count for each n-gram across all references
+    max_ref_counts: Counter[tuple[str, ...]] = Counter()
+    for ref_tokens in reference_token_lists:
+        ref_ngrams = _get_ngrams(ref_tokens, n)
+        for ngram, count in ref_ngrams.items():
+            max_ref_counts[ngram] = max(max_ref_counts[ngram], count)
+
+    # Clip candidate counts to max reference counts
+    clipped_count = 0
+    for ngram, count in candidate_ngrams.items():
+        clipped_count += min(count, max_ref_counts[ngram])
+
+    return clipped_count, sum(candidate_ngrams.values())
+
+
+def _brevity_penalty(candidate_len: int, reference_lens: list[int]) -> float:
+    """
+    Compute brevity penalty.
+
+    Uses the closest reference length to the candidate length.
+    """
+    if candidate_len == 0:
+        return 0.0
+
+    # Find closest reference length
+    closest_ref_len = min(reference_lens, key=lambda r: (abs(r - candidate_len), r))
+
+    if candidate_len >= closest_ref_len:
+        return 1.0
+
+    return math.exp(1 - closest_ref_len / candidate_len)
+
+
+class Bleu:
+    """
+    BLEU metric for measuring translation/generation quality.
+
+    Computes BLEU-1 through BLEU-4 scores using modified n-gram precision
+    with brevity penalty.
+    """
+
+    def __init__(self, tokeniser: WordTokeniser | None = None) -> None:
+        """
+        Initialise the BLEU metric.
+
+        Args:
+            tokeniser: Tokeniser to use. Defaults to WordTokeniser().
+        """
+        self._tokeniser = tokeniser or WordTokeniser()
+
+    @property
+    def name(self) -> str:
+        """Return the name of this metric."""
+        return "bleu"
+
+    @property
+    def requires_reference(self) -> bool:
+        """Return whether this metric requires reference text."""
+        return True
+
+    def score(
+        self, candidate: str, reference: str | list[str] | None = None
+    ) -> BleuResult:
+        """
+        Compute BLEU scores for a candidate text.
+
+        Args:
+            candidate: The text to score.
+            reference: Reference text(s) for comparison.
+
+        Returns:
+            BleuResult with BLEU-1 through BLEU-4 and brevity penalty.
+
+        Raises:
+            ValueError: If reference is None.
+        """
+        if reference is None:
+            raise ValueError("BLEU requires reference text")
+
+        # Normalise reference to list
+        references = [reference] if isinstance(reference, str) else reference
+
+        # Tokenise
+        candidate_tokens = self._tokeniser.tokenise(candidate)
+        reference_token_lists = [self._tokeniser.tokenise(r) for r in references]
+
+        # Handle empty candidate
+        if not candidate_tokens:
+            return BleuResult(
+                bleu1=0.0, bleu2=0.0, bleu3=0.0, bleu4=0.0, brevity_penalty=0.0
+            )
+
+        # Handle empty references
+        if all(not ref for ref in reference_token_lists):
+            raise ValueError("Reference text cannot be empty")
+
+        # Compute modified precisions for n=1,2,3,4
+        precisions = []
+        for n in range(1, 5):
+            clipped, total = _modified_precision(
+                candidate_tokens, reference_token_lists, n
+            )
+            if total == 0:
+                precisions.append(0.0)
+            else:
+                precisions.append(clipped / total)
+
+        # Compute BLEU scores using geometric mean
+        bp = _brevity_penalty(
+            len(candidate_tokens),
+            [len(ref) for ref in reference_token_lists],
+        )
+
+        def geometric_mean(p_list: list[float]) -> float:
+            """Compute geometric mean with smoothing for zeros."""
+            if any(p == 0.0 for p in p_list):
+                return 0.0
+            log_sum = sum(math.log(p) for p in p_list)
+            return math.exp(log_sum / len(p_list))
+
+        bleu_scores = []
+        for n in range(1, 5):
+            # BLEU-n uses precisions 1 through n
+            bleu_n = bp * geometric_mean(precisions[:n])
+            bleu_scores.append(bleu_n)
+
+        return BleuResult(
+            bleu1=bleu_scores[0],
+            bleu2=bleu_scores[1],
+            bleu3=bleu_scores[2],
+            bleu4=bleu_scores[3],
+            brevity_penalty=bp,
+        )
+
+    def batch_score(
+        self,
+        candidates: list[str],
+        references: list[str] | list[list[str]] | None = None,
+    ) -> BatchResult[BleuResult]:
+        """
+        Compute BLEU scores for a batch of candidates.
+
+        Args:
+            candidates: List of texts to score.
+            references: Reference text(s) for each candidate.
+
+        Returns:
+            BatchResult containing individual results and aggregate statistics.
+
+        Raises:
+            ValueError: If references is None or length mismatch.
+        """
+        if references is None:
+            raise ValueError("BLEU requires reference texts")
+
+        if len(candidates) != len(references):
+            raise ValueError(
+                f"Number of candidates ({len(candidates)}) must match "
+                f"number of references ({len(references)})"
+            )
+
+        results: list[BleuResult] = []
+        for i, cand in enumerate(candidates):
+            ref: str | list[str] = references[i]
+            results.append(self.score(cand, ref))
+
+        # Compute aggregate statistics for each score type
+        stats = {
+            "bleu1": AggregateStats.from_values([r.bleu1 for r in results]),
+            "bleu2": AggregateStats.from_values([r.bleu2 for r in results]),
+            "bleu3": AggregateStats.from_values([r.bleu3 for r in results]),
+            "bleu4": AggregateStats.from_values([r.bleu4 for r in results]),
+            "brevity_penalty": AggregateStats.from_values(
+                [r.brevity_penalty for r in results]
+            ),
+        }
+
+        return BatchResult(results=results, count=len(results), stats=stats)