diff --git a/.github/workflows/run-sweep.yml b/.github/workflows/run-sweep.yml
index 785ec4dad..c3ff0985c 100644
--- a/.github/workflows/run-sweep.yml
+++ b/.github/workflows/run-sweep.yml
@@ -265,6 +265,36 @@ jobs:
                   name: "run-stats"
                   path: ${{ env.STATS_FILENAME }}.json
 
+    compare-results:
+        needs:
+            [
+                collect-results,
+                setup,
+            ]
+        if: >-
+            always() &&
+            github.event_name == 'pull_request' &&
+            needs.collect-results.result == 'success'
+        runs-on: ubuntu-latest
+
+        env:
+            DATABASE_URL: ${{ secrets.NEON_PROD_RO_URL }}
+
+        steps:
+            - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
+
+            - name: Download results artifacts
+              uses: actions/download-artifact@70fc10c6e5e1ce46ad2ea6f2b72d43f7d47b13c3 # v8.0.0
+              with:
+                  path: results/
+                  pattern: results_bmk
+
+            - name: Install dependencies
+              run: pip install psycopg2-binary tabulate
+
+            - name: Compare results against main
+              run: python3 utils/compare_results.py results/ >> $GITHUB_STEP_SUMMARY
+
     trigger-vercel-deploy:
         needs:
             [
diff --git a/perf-changelog.yaml b/perf-changelog.yaml
index dbb3abc88..51e570373 100644
--- a/perf-changelog.yaml
+++ b/perf-changelog.yaml
@@ -881,3 +881,11 @@
     - "Expanding TP search space"
     - "Adding kv-cache-fp8"
   pr-link: https://github.com/SemiAnalysisAI/InferenceX/pull/865
+
+- config-keys:
+    - gptoss-fp4-b200-vllm
+    - dsr1-fp8-h200-dynamo-trt
+    - dsr1-fp4-mi355x-sglang-disagg
+  description:
+    - "test"
+  pr-link: test
diff --git a/utils/compare_results.py b/utils/compare_results.py
new file mode 100644
index 000000000..6ff345da0
--- /dev/null
+++ b/utils/compare_results.py
@@ -0,0 +1,262 @@
+import json
+import os
+import re
+import sys
+from pathlib import Path
+
+import psycopg2
+from tabulate import tabulate
+
+
+def parse_bool(value):
+    if isinstance(value, bool):
+        return value
+    return str(value).lower() == "true"
+
+
+def extract_hardware(runner):
+    """Strip suffixes like -multinode, -trt, -disagg from runner to get hardware name."""
+    return re.split(r"-(multinode|trt|disagg)$", runner)[0].lower()
+
+
+def build_config_params(result):
+    """Build the DB config lookup parameters from a result JSON."""
+    is_multinode = result.get("is_multinode", False)
+    hw = extract_hardware(result["hw"])
+    model = result["infmax_model_prefix"].lower()
+    framework = result["framework"].lower()
+    precision = result["precision"].lower()
+    spec_method = result.get("spec_decoding", "none").lower()
+    disagg = parse_bool(result.get("disagg", False))
+
+    if is_multinode:
+        return {
+            "hardware": hw,
+            "model": model,
+            "framework": framework,
+            "precision": precision,
+            "spec_method": spec_method,
+            "disagg": disagg,
+            "is_multinode": True,
+            "prefill_tp": int(result["prefill_tp"]),
+            "prefill_ep": int(result["prefill_ep"]),
+            "prefill_dp_attention": parse_bool(result["prefill_dp_attention"]),
+            "decode_tp": int(result["decode_tp"]),
+            "decode_ep": int(result["decode_ep"]),
+            "decode_dp_attention": parse_bool(result["decode_dp_attention"]),
+        }
+    else:
+        tp = int(result["tp"])
+        ep = int(result["ep"])
+        dp_attention = parse_bool(result["dp_attention"])
+        return {
+            "hardware": hw,
+            "model": model,
+            "framework": framework,
+            "precision": precision,
+            "spec_method": spec_method,
+            "disagg": disagg,
+            "is_multinode": False,
+            "prefill_tp": tp,
+            "prefill_ep": ep,
+            "prefill_dp_attention": dp_attention,
+            "decode_tp": tp,
+            "decode_ep": ep,
+            "decode_dp_attention": dp_attention,
+        }
+
+
+# Use LIKE prefix match on model to handle cases where DB model name
+# differs from model-prefix (e.g. model-prefix "gptoss" -> DB "gptoss120b")
+BASELINE_QUERY = """
+    SELECT br.metrics->>'tput_per_gpu' as tput_per_gpu,
+           c.model as db_model
+    FROM benchmark_results br
+    JOIN configs c ON c.id = br.config_id
+    JOIN workflow_runs wr ON wr.id = br.workflow_run_id
+    WHERE c.hardware = %(hardware)s
+      AND c.framework = %(framework)s
+      AND c.model LIKE %(model)s || '%%'
+      AND c.precision = %(precision)s
+      AND c.spec_method = %(spec_method)s
+      AND c.disagg = %(disagg)s
+      AND c.is_multinode = %(is_multinode)s
+      AND c.prefill_tp = %(prefill_tp)s
+      AND c.prefill_ep = %(prefill_ep)s
+      AND c.prefill_dp_attention = %(prefill_dp_attention)s
+      AND c.decode_tp = %(decode_tp)s
+      AND c.decode_ep = %(decode_ep)s
+      AND c.decode_dp_attention = %(decode_dp_attention)s
+      AND br.isl = %(isl)s
+      AND br.osl = %(osl)s
+      AND br.conc = %(conc)s
+      AND wr.head_branch = 'main'
+      AND br.error IS NULL
+    ORDER BY br.date DESC
+    LIMIT 1
+"""
+
+
+def main():
+    if len(sys.argv) < 2:
+        print("Usage: python compare_results.py <results_dir>")
+        sys.exit(1)
+
+    results_dir = Path(sys.argv[1])
+    database_url = os.environ["DATABASE_URL"]
+
+    # Load all benchmark result JSONs (files may contain a single dict or a list of dicts)
+    results = []
+    for path in results_dir.rglob("*.json"):
+        with open(path) as f:
+            data = json.load(f)
+        if isinstance(data, list):
+            results.extend(data)
+        else:
+            results.append(data)
+
+    print(f"Loaded {len(results)} benchmark results", file=sys.stderr)
+
+    if not results:
+        print("No benchmark results found to compare.")
+        return
+
+    conn = psycopg2.connect(database_url)
+    rows = []
+    matched = 0
+    unmatched = 0
+
+    for r in results:
+        config_params = build_config_params(r)
+        query_params = {
+            **config_params,
+            "isl": int(r["isl"]),
+            "osl": int(r["osl"]),
+            "conc": int(r["conc"]),
+        }
+
+        print(f"\nQuery params: {json.dumps({k: str(v) for k, v in query_params.items()}, indent=2)}", file=sys.stderr)
+
+        with conn.cursor() as cur:
+            cur.execute(BASELINE_QUERY, query_params)
+            row = cur.fetchone()
+
+        if row:
+            matched += 1
+            print(f"  -> Matched DB model={row[1]}, tput={row[0]}", file=sys.stderr)
+        else:
+            unmatched += 1
+            print(f"  -> No baseline found", file=sys.stderr)
+
+        current_tput = float(r["tput_per_gpu"])
+        baseline_tput = float(row[0]) if row else None
+
+        if baseline_tput is not None and baseline_tput > 0:
+            delta = current_tput - baseline_tput
+            pct = (delta / baseline_tput) * 100
+            delta_str = f"{delta:+.2f} ({pct:+.1f}%)"
+        else:
+            delta_str = "N/A (no baseline)"
+
+        is_multinode = r.get("is_multinode", False)
+        if is_multinode:
+            parallelism = (
+                f"P(tp{r['prefill_tp']}/ep{r['prefill_ep']}) "
+                f"D(tp{r['decode_tp']}/ep{r['decode_ep']})"
+            )
+        else:
+            parallelism = f"tp{r['tp']}/ep{r['ep']}"
+
+        row_data = {
+            "model": r["infmax_model_prefix"],
+            "served_model": r["model"],
+            "hw": extract_hardware(r["hw"]).upper(),
+            "framework": r["framework"].upper(),
+            "precision": r["precision"].upper(),
+            "parallelism": parallelism,
+            "isl": int(r["isl"]),
+            "osl": int(r["osl"]),
+            "conc": int(r["conc"]),
+            "current": current_tput,
+            "baseline": baseline_tput,
+            "delta_str": delta_str,
+        }
+        if not is_multinode:
+            row_data["dp_attention"] = r.get("dp_attention", False)
+        rows.append(row_data)
+
+    conn.close()
+
+    print(f"\nSummary: {matched} matched, {unmatched} unmatched out of {len(results)} results", file=sys.stderr)
+
+    rows.sort(key=lambda x: (x["model"], x["hw"], x["framework"], x["isl"], x["osl"], x["conc"]))
+
+    single_node = [r for r in rows if "P(" not in r["parallelism"]]
+    multi_node = [r for r in rows if "P(" in r["parallelism"]]
+
+    if single_node:
+        headers = [
+            "Model", "Served Model", "Hardware", "Framework", "Precision",
+            "ISL", "OSL", "TP", "EP", "DP Attention", "Conc",
+            "TPUT per GPU", "Baseline TPUT per GPU", "Delta",
+        ]
+        table_rows = []
+        for row in single_node:
+            parts = row["parallelism"]  # "tp1/ep1"
+            tp_val = parts.split("/")[0].replace("tp", "")
+            ep_val = parts.split("/")[1].replace("ep", "")
+            table_rows.append([
+                row["model"],
+                row["served_model"],
+                row["hw"],
+                row["framework"],
+                row["precision"],
+                row["isl"],
+                row["osl"],
+                tp_val,
+                ep_val,
+                row.get("dp_attention", False),
+                row["conc"],
+                f"{row['current']:.4f}",
+                f"{row['baseline']:.4f}" if row["baseline"] is not None else "N/A",
+                row["delta_str"],
+            ])
+
+        print("## Single-Node Throughput Comparison vs. Most Recent\n")
+        print(tabulate(table_rows, headers=headers, tablefmt="github"))
+        print()
+
+    if multi_node:
+        headers = [
+            "Model", "Served Model", "Hardware", "Framework", "Precision",
+            "ISL", "OSL", "Prefill TP", "Prefill EP", "Decode TP", "Decode EP",
+            "Conc", "TPUT per GPU", "Baseline TPUT per GPU", "Delta",
+        ]
+        table_rows = []
+        for row in multi_node:
+            # Parse P(tp4/ep4) D(tp8/ep8)
+            m = re.match(r"P\(tp(\d+)/ep(\d+)\) D\(tp(\d+)/ep(\d+)\)", row["parallelism"])
+            table_rows.append([
+                row["model"],
+                row["served_model"],
+                row["hw"],
+                row["framework"],
+                row["precision"],
+                row["isl"],
+                row["osl"],
+                m.group(1) if m else "",
+                m.group(2) if m else "",
+                m.group(3) if m else "",
+                m.group(4) if m else "",
+                row["conc"],
+                f"{row['current']:.4f}",
+                f"{row['baseline']:.4f}" if row["baseline"] is not None else "N/A",
+                row["delta_str"],
+            ])
+
+        print("## Multi-Node Throughput Comparison vs. Most Recent\n")
+        print(tabulate(table_rows, headers=headers, tablefmt="github"))
+
+
+if __name__ == "__main__":
+    main()