diff --git a/benchmark/compare_hblt0.py b/benchmark/compare_hblt0.py
new file mode 100644
index 0000000..094ec94
--- /dev/null
+++ b/benchmark/compare_hblt0.py
@@ -0,0 +1,189 @@
+#!/usr/bin/env python3
+"""
+Compare hipBLASLt-on vs hblt0-off benchmark runs.
+
+This script inspects docs/results.json (the same dataset consumed by
+docs/assets/index2.js) and reports, for every backend that was benchmarked
+both with and without the `-hblt0` suffix, which configuration wins.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import statistics
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, Iterable, List, Tuple
+
+
+DEFAULT_RESULTS = Path("docs") / "results.json"
+# Matches the tolerance used in docs/assets/index2.js (MIN_TOL = 0.25)
+DEFAULT_TOLERANCE = 0.25
+
+VariantValues = Dict[str, List[float]]
+BackendMatrix = Dict[Tuple, VariantValues]
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(
+        description=(
+            "Pair benchmark runs with and without '-hblt0' and report which "
+            "configuration is faster per backend."
+        )
+    )
+    parser.add_argument(
+        "--results",
+        type=Path,
+        default=DEFAULT_RESULTS,
+        help="Path to results.json generated by the benchmark pipeline.",
+    )
+    parser.add_argument(
+        "--tolerance",
+        type=float,
+        default=DEFAULT_TOLERANCE,
+        help="Minimum tokens/sec delta to treat as a win (default: 0.25).",
+    )
+    return parser.parse_args()
+
+
+def load_runs(path: Path) -> Iterable[dict]:
+    data = json.loads(path.read_text())
+    runs = data.get("runs")
+    if not isinstance(runs, list):
+        raise ValueError(f"results.json at {path} does not contain a 'runs' array")
+    return runs
+
+
+def measurement_key(run: dict) -> Tuple:
+    """Return a tuple that uniquely identifies a benchmark scenario."""
+    return (
+        (run.get("model_clean") or run.get("model") or "").lower(),
+        run.get("test") or "",
+        run.get("context") or "default",
+        run.get("context_tokens") or 0,
+        (run.get("quant") or "").upper(),
+        run.get("fa"),
+        run.get("rpc"),
+        run.get("ngl"),
+        run.get("backend"),
+    )
+
+
+def pair_runs(runs: Iterable[dict]) -> Tuple[Dict[str, BackendMatrix], Dict[str, Dict[str, int]]]:
+    """
+    Group runs by backend (without / with '-hblt0') and measurement key.
+
+    Returns:
+        pairs: backend -> measurement_key -> {'hipblaslt': [...], 'hblt0': [...]}
+        coverage: backend -> {'hipblaslt': raw_run_count, 'hblt0': raw_run_count}
+    """
+    pairs: Dict[str, BackendMatrix] = defaultdict(lambda: defaultdict(dict))
+    coverage: Dict[str, Dict[str, int]] = defaultdict(lambda: {"hipblaslt": 0, "hblt0": 0})
+
+    for run in runs:
+        env = run.get("env")
+        if not env:
+            continue
+        if run.get("error"):
+            continue
+        tps = run.get("tps_mean")
+        if not isinstance(tps, (int, float)) or math.isnan(tps):
+            continue
+
+        is_hblt0 = env.endswith("-hblt0")
+        base_env = env[:-6] if is_hblt0 else env
+        variant = "hblt0" if is_hblt0 else "hipblaslt"
+
+        key = measurement_key(run)
+        entry = pairs[base_env][key]
+        entry.setdefault(variant, []).append(float(tps))
+        coverage[base_env][variant] += 1
+
+    return pairs, coverage
+
+
+def summarize_backend(
+    backend: str,
+    matrix: BackendMatrix,
+    tolerance: float,
+    coverage: Dict[str, int],
+) -> dict | None:
+    pairs: List[Tuple[float, float]] = []
+
+    for entry in matrix.values():
+        if "hipblaslt" not in entry or "hblt0" not in entry:
+            continue
+        hip = statistics.mean(entry["hipblaslt"])
+        hbl = statistics.mean(entry["hblt0"])
+        pairs.append((hip, hbl))
+
+    if not pairs:
+        return None
+
+    hip_wins = sum(1 for hip, hbl in pairs if (hip - hbl) > tolerance)
+    hbl_wins = sum(1 for hip, hbl in pairs if (hbl - hip) > tolerance)
+    ties = len(pairs) - hip_wins - hbl_wins
+
+    avg_hip = statistics.mean(hip for hip, _ in pairs)
+    avg_hbl = statistics.mean(hbl for _, hbl in pairs)
+    avg_delta = avg_hip - avg_hbl
+    pct_delta = (avg_delta / avg_hbl * 100.0) if avg_hbl else float("inf")
+
+    if avg_delta > tolerance:
+        verdict = "hipBLASLt faster"
+    elif avg_delta < -tolerance:
+        verdict = "hblt0 faster"
+    else:
+        verdict = "too close to call"
+
+    return {
+        "backend": backend,
+        "pairs": len(pairs),
+        "hip_wins": hip_wins,
+        "hbl_wins": hbl_wins,
+        "ties": ties,
+        "avg_hip": avg_hip,
+        "avg_hbl": avg_hbl,
+        "avg_delta": avg_delta,
+        "pct_delta": pct_delta,
+        "verdict": verdict,
+        "coverage": coverage,
+    }
+
+
+def format_summary(summary: dict) -> str:
+    cov = summary["coverage"]
+    hip_runs = cov.get("hipblaslt", 0)
+    hbl_runs = cov.get("hblt0", 0)
+    return (
+        f"{summary['backend']}: {summary['verdict']} "
+        f"(Δ {summary['avg_delta']:+.2f} tps / {summary['pct_delta']:+.2f}% "
+        f"across {summary['pairs']} matched cases; "
+        f"hipBLASLt wins {summary['hip_wins']}, hblt0 wins {summary['hbl_wins']}, "
+        f"ties {summary['ties']}; raw runs hipBLASLt={hip_runs}, hblt0={hbl_runs})"
+    )
+
+
+def main() -> None:
+    args = parse_args()
+    runs = load_runs(args.results)
+    matrices, coverage = pair_runs(runs)
+
+    summaries = []
+    for backend in sorted(matrices):
+        summary = summarize_backend(backend, matrices[backend], args.tolerance, coverage.get(backend, {}))
+        if summary:
+            summaries.append(summary)
+
+    if not summaries:
+        print("No matching hipBLASLt vs hblt0 pairs were found.")
+        return
+
+    for summary in summaries:
+        print(format_summary(summary))
+
+
+if __name__ == "__main__":
+    main()