Pre-render leaderboard rows for the static org-card view (progressive enhancement)

build.py

@@ -3,12 +3,14 @@
 Build leaderboard data for the llm-bench.io HuggingFace Static Space.
 
 Fetches the public benchmarks API, aggregates by model + VRAM tier
-(last 30 days, >= 3 runs, completed quality assessments only), and
-writes a compact data.json consumed by index.html.
+(last 30 days, >= 3 runs, completed quality assessments only), then:
+  1. writes data.json      (enables interactive filter/sort on the live Space)
+  2. renders index.html    (pre-rendered rows so the no-JS org-card view works)
 
 Run:  python3 build.py
 """
 import json
+import urllib.parse
 import urllib.request
 from collections import defaultdict
 from datetime import datetime, timedelta, timezone
@@ -16,6 +18,7 @@ from datetime import datetime, timedelta, timezone
 API = "https://llm-bench.io/api/benchmarks"
 WINDOW_DAYS = 30
 MIN_RUNS = 3
+HERE = "/tmp/space-readme"
 
 SCENARIOS = {
     "coding_agent": "Coding",
@@ -58,6 +61,83 @@ def hardware_label(b):
     return cpu or "CPU"
 
 
+def esc(s):
+    return (
+        str(s)
+        .replace("&", "&")
+        .replace("<", "&lt;")
+        .replace(">", "&gt;")
+        .replace('"', "&quot;")
+        .replace("'", "&#39;")
+    )
+
+
+def qcolor(v):
+    if v is None:
+        return "#f87171"
+    if v >= 85:
+        return "#4ade80"
+    if v >= 75:
+        return "#facc15"
+    return "#f87171"
+
+
+def render_static(out):
+    """Fill template.html placeholders with pre-rendered rows (no-JS view)."""
+    rows = out["rows"]
+    scen_keys = ["coding_agent", "openclaw", "roleplay", "research"]
+
+    trs = []
+    for r in rows:
+        link = "https://llm-bench.io/benchmarks?model=" + urllib.parse.quote(r["model"], safe="")
+        q = r.get("quality")
+        if q is not None:
+            qcell = (
+                '<td class="num"><span class="qbar">'
+                f'<i style="width:{max(0, min(100, q)):.0f}%;background:{qcolor(q)}"></i></span>'
+                f"<b>{q:.1f}</b></td>"
+            )
+        else:
+            qcell = '<td class="num sc dim">–</td>'
+        sc_cells = []
+        for k in scen_keys:
+            v = r.get("scenarios", {}).get(k)
+            sc_cells.append(f'<td class="num sc">{v:.1f}</td>' if v is not None else '<td class="num sc dim">–</td>')
+        speed = r.get("speed")
+        speed_cell = f'<td class="num">{speed:.1f}</td>' if speed is not None else '<td class="num sc dim">–</td>'
+        trs.append(
+            '    <tr style="cursor:pointer" title="Open on llm-bench.io">\n'
+            '      <td><div class="model-cell">'
+            f'<a class="model-name" href="{link}" target="_blank" rel="noopener">{esc(r["model"])}</a>\n'
+            f'        <span class="model-hw">{esc(r["hardware"])}</span></div></td>\n'
+            f'      <td class="num vram">{r["vram"]} GB</td>\n'
+            f'      <td class="num">{r["runs"]}</td>\n'
+            f"      {qcell}\n"
+            f"      {speed_cell}\n"
+            + "      ".join(sc_cells)
+            + f'\n      <td class="num updated">{r["updated"]}</td>\n'
+            "    </tr>"
+        )
+
+    meta = (
+        f'<span><b>{len(rows)}</b> models</span>'
+        f'<span><b>{out["inWindow"]}</b> submissions in window</span>'
+        f'<span>of <b>{out["totalSubmissions"]}</b> total on llm-bench.io</span>'
+    )
+    gen = datetime.fromisoformat(out["generatedAt"]).strftime("%d %b %Y")
+
+    with open(f"{HERE}/template.html") as f:
+        html = f.read()
+    html = html.replace("__ROWS__", "\n".join(trs))
+    html = html.replace("__META__", meta)
+    html = html.replace("__WIN__", str(out["windowDays"]))
+    html = html.replace("__MINR__", str(out["minRuns"]))
+    html = html.replace("__GEN__", gen)
+
+    with open(f"{HERE}/index.html", "w") as f:
+        f.write(html)
+
+
 def main():
     total, records = fetch_all()
     cutoff = datetime.now(timezone.utc) - timedelta(days=WINDOW_DAYS)
@@ -122,11 +202,12 @@ def main():
         "scenarios": SCENARIOS,
         "rows": rows,
     }
-    path = "/tmp/space-readme/data.json"
-    with open(path, "w") as f:
+    with open(f"{HERE}/data.json", "w") as f:
         json.dump(out, f, separators=(",", ":"))
-    size = len(json.dumps(out, separators=(",", ":")))
-    print(f"rows={len(rows)}  window_records={len(in_window)}  total={total}  bytes={size}  -> {path}")
+    print(f"rows={len(rows)}  window_records={len(in_window)}  total={total}  -> {HERE}/data.json")
+
+    render_static(out)
+    print("rendered index.html from template.html")
 
 
 if __name__ == "__main__":

data.json

@@ -1 +1 @@
-{"generatedAt":"2026-09-10T20:01:17+00:00","windowDays":30,"minRuns":3,"totalSubmissions":909,"inWindow":640,"scenarios":{"coding_agent":"Coding","openclaw":"Agent","roleplay":"Role-Play","research":"Research"},"rows":[{"vram":128,"model":"mtplx-qwen38-27b-optimized-quality","hardware":"Apple M5 Max","runs":3,"quality":87.2,"speed":35.0,"scenarios":{"coding_agent":78.4,"openclaw":90.6,"roleplay":92.4,"research":87.6},"updated":"2026-09-01"},{"vram":23,"model":"unsloth/Qwen3.8-27B-GGUF:IQ3_S","hardware":"NVIDIA GeForce RTX 4090","runs":4,"quality":86.9,"speed":108.6,"scenarios":{"coding_agent":83.3,"openclaw":86.2,"roleplay":90.3,"research":87.9},"updated":"2026-08-29"},{"vram":64,"model":"mtplx-qwen38-27b-optimized-quality","hardware":"Apple M4 Max","runs":3,"quality":85.7,"speed":35.5,"scenarios":{"coding_agent":77.7,"openclaw":85.2,"roleplay":93.1,"research":86.7},"updated":"2026-09-02"},{"vram":7,"model":"Tiel-Coder-35B-A3B-MTP-UD-Q6_K_XL","hardware":"AMD Radeon RX 5600 OEM/5600 XT / 5700/5700 XT + AMD Radeon RX 9060 XT","runs":3,"quality":85.5,"speed":35.1,"scenarios":{"coding_agent":83.3,"openclaw":90.5,"roleplay":82.6,"research":85.6},"updated":"2026-09-04"},{"vram":23,"model":"unsloth/Qwen3.8-27B-GGUF:Q4_K_M","hardware":"NVIDIA GeForce RTX 4090","runs":6,"quality":85.1,"speed":95.3,"scenarios":{"coding_agent":78.0,"openclaw":85.9,"roleplay":89.0,"research":87.4},"updated":"2026-08-29"},{"vram":128,"model":"Qwen3.8-27B-oQ4e-fp16-mtp","hardware":"Apple M5 Max","runs":4,"quality":85.1,"speed":38.6,"scenarios":{"coding_agent":82.5,"openclaw":85.1,"roleplay":89.6,"research":83.0},"updated":"2026-08-20"},{"vram":128,"model":"mtplx-flash-next-bare-speed","hardware":"Apple M5 Max","runs":3,"quality":85.0,"speed":57.5,"scenarios":{"coding_agent":75.6,"openclaw":84.6,"roleplay":93.3,"research":86.3},"updated":"2026-09-01"},{"vram":23,"model":"qwen3.8-27b-UD-Q4_K_XL","hardware":"AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M","runs":3,"quality":84.9,"speed":50.7,"scenarios":{"coding_agent":80.6,"openclaw":83.7,"roleplay":86.9,"research":88.5},"updated":"2026-09-08"},{"vram":23,"model":"Tiel-Coder-35B-A3B-Q4_K_S","hardware":"AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M","runs":3,"quality":84.6,"speed":165.0,"scenarios":{"coding_agent":76.9,"openclaw":89.3,"roleplay":87.3,"research":84.9},"updated":"2026-09-05"},{"vram":15,"model":"Qwen3.8-27B-UD-Q3_K_XL","hardware":"NVIDIA GeForce RTX 5070 Ti","runs":8,"quality":84.6,"speed":66.3,"scenarios":{"coding_agent":73.3,"openclaw":86.8,"roleplay":92.3,"research":86.2},"updated":"2026-09-04"},{"vram":64,"model":"Qwen3.8-27B-oQ8e-fp16-mtp","hardware":"Apple M5 Max","runs":4,"quality":84.6,"speed":33.7,"scenarios":{"coding_agent":74.1,"openclaw":87.4,"roleplay":91.2,"research":85.6},"updated":"2026-08-28"},{"vram":128,"model":"Qwen3.8-Flash-Next-oQ5e-mtp","hardware":"Apple M4 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+{"generatedAt":"2026-09-10T20:22:48+00:00","windowDays":30,"minRuns":3,"totalSubmissions":909,"inWindow":640,"scenarios":{"coding_agent":"Coding","openclaw":"Agent","roleplay":"Role-Play","research":"Research"},"rows":[{"vram":128,"model":"mtplx-qwen38-27b-optimized-quality","hardware":"Apple M5 Max","runs":3,"quality":87.2,"speed":35.0,"scenarios":{"coding_agent":78.4,"openclaw":90.6,"roleplay":92.4,"research":87.6},"updated":"2026-09-01"},{"vram":23,"model":"unsloth/Qwen3.8-27B-GGUF:IQ3_S","hardware":"NVIDIA GeForce RTX 4090","runs":4,"quality":86.9,"speed":108.6,"scenarios":{"coding_agent":83.3,"openclaw":86.2,"roleplay":90.3,"research":87.9},"updated":"2026-08-29"},{"vram":64,"model":"mtplx-qwen38-27b-optimized-quality","hardware":"Apple M4 Max","runs":3,"quality":85.7,"speed":35.5,"scenarios":{"coding_agent":77.7,"openclaw":85.2,"roleplay":93.1,"research":86.7},"updated":"2026-09-02"},{"vram":7,"model":"Tiel-Coder-35B-A3B-MTP-UD-Q6_K_XL","hardware":"AMD Radeon RX 5600 OEM/5600 XT / 5700/5700 XT + AMD Radeon RX 9060 XT","runs":3,"quality":85.5,"speed":35.1,"scenarios":{"coding_agent":83.3,"openclaw":90.5,"roleplay":82.6,"research":85.6},"updated":"2026-09-04"},{"vram":23,"model":"unsloth/Qwen3.8-27B-GGUF:Q4_K_M","hardware":"NVIDIA GeForce RTX 4090","runs":6,"quality":85.1,"speed":95.3,"scenarios":{"coding_agent":78.0,"openclaw":85.9,"roleplay":89.0,"research":87.4},"updated":"2026-08-29"},{"vram":128,"model":"Qwen3.8-27B-oQ4e-fp16-mtp","hardware":"Apple M5 Max","runs":4,"quality":85.1,"speed":38.6,"scenarios":{"coding_agent":82.5,"openclaw":85.1,"roleplay":89.6,"research":83.0},"updated":"2026-08-20"},{"vram":128,"model":"mtplx-flash-next-bare-speed","hardware":"Apple M5 Max","runs":3,"quality":85.0,"speed":57.5,"scenarios":{"coding_agent":75.6,"openclaw":84.6,"roleplay":93.3,"research":86.3},"updated":"2026-09-01"},{"vram":23,"model":"qwen3.8-27b-UD-Q4_K_XL","hardware":"AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M","runs":3,"quality":84.9,"speed":50.7,"scenarios":{"coding_agent":80.6,"openclaw":83.7,"roleplay":86.9,"research":88.5},"updated":"2026-09-08"},{"vram":23,"model":"Tiel-Coder-35B-A3B-Q4_K_S","hardware":"AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M","runs":3,"quality":84.6,"speed":165.0,"scenarios":{"coding_agent":76.9,"openclaw":89.3,"roleplay":87.3,"research":84.9},"updated":"2026-09-05"},{"vram":15,"model":"Qwen3.8-27B-UD-Q3_K_XL","hardware":"NVIDIA GeForce RTX 5070 Ti","runs":8,"quality":84.6,"speed":66.3,"scenarios":{"coding_agent":73.3,"openclaw":86.8,"roleplay":92.3,"research":86.2},"updated":"2026-09-04"},{"vram":64,"model":"Qwen3.8-27B-oQ8e-fp16-mtp","hardware":"Apple M5 Max","runs":4,"quality":84.6,"speed":33.7,"scenarios":{"coding_agent":74.1,"openclaw":87.4,"roleplay":91.2,"research":85.6},"updated":"2026-08-28"},{"vram":128,"model":"Qwen3.8-Flash-Next-oQ5e-mtp","hardware":"Apple M4 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Max","runs":4,"quality":83.4,"speed":118.4,"scenarios":{"coding_agent":80.7,"openclaw":82.7,"roleplay":85.6,"research":84.4},"updated":"2026-08-25"},{"vram":16,"model":"Qwen3.8-27B-UD-IQ3_S","hardware":"NVIDIA GeForce RTX 4080 SUPER","runs":48,"quality":83.2,"speed":78.8,"scenarios":{"coding_agent":77.8,"openclaw":81.6,"roleplay":87.8,"research":85.7},"updated":"2026-09-05"},{"vram":23,"model":"qwen3.8:27b","hardware":"AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M","runs":5,"quality":82.8,"speed":35.6,"scenarios":{"coding_agent":78.6,"openclaw":82.8,"roleplay":84.8,"research":84.9},"updated":"2026-09-07"},{"vram":23,"model":"qwen3.8:latest","hardware":"AMD Radeon RX 7900 XTX","runs":7,"quality":82.6,"speed":37.4,"scenarios":{"coding_agent":75.9,"openclaw":82.2,"roleplay":85.1,"research":87.1},"updated":"2026-09-07"},{"vram":128,"model":"Qwen3.8-Flash-Next-oQ4e-mtp","hardware":"Apple M4 Max","runs":22,"quality":82.2,"speed":40.7,"scenarios":{"coding_agent":72.6,"openclaw":83.5,"roleplay":87.9,"research":84.9},"updated":"2026-09-07"},{"vram":64,"model":"Tiel-Coder-35B-A3B-MLX-oQ4e-MTP","hardware":"Apple M5 Max","runs":5,"quality":82.1,"speed":123.7,"scenarios":{"coding_agent":76.3,"openclaw":84.4,"roleplay":82.3,"research":85.3},"updated":"2026-09-07"},{"vram":64,"model":"Muse-Glimmer-30B-4bit","hardware":"Apple M5 Max","runs":3,"quality":82.1,"speed":29.9,"scenarios":{"coding_agent":68.6,"openclaw":89.3,"roleplay":89.6,"research":80.9},"updated":"2026-08-13"},{"vram":23,"model":"muse-glimmer:latest","hardware":"AMD Radeon RX 7900 XTX","runs":4,"quality":82.0,"speed":34.5,"scenarios":{"coding_agent":71.5,"openclaw":93.0,"roleplay":79.7,"research":83.6},"updated":"2026-08-25"},{"vram":64,"model":"Qwen3.8-27B-4bit","hardware":"Apple M5 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3080","runs":3,"quality":79.7,"speed":172.4,"scenarios":{"coding_agent":68.0,"openclaw":84.5,"roleplay":83.2,"research":83.1},"updated":"2026-09-07"},{"vram":23,"model":"qwen38-27b","hardware":"AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M","runs":6,"quality":79.6,"speed":72.7,"scenarios":{"coding_agent":67.5,"openclaw":77.3,"roleplay":88.7,"research":85.0},"updated":"2026-09-08"},{"vram":96,"model":"Qwen3.8-27B-UD-Q6_K_XL","hardware":"NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition","runs":4,"quality":79.5,"speed":84.3,"scenarios":{"coding_agent":75.9,"openclaw":76.9,"roleplay":84.4,"research":80.6},"updated":"2026-08-31"},{"vram":16,"model":"peculiar-ragdoll/Tiel-Coder-35B-A3B-GGUF-MTP","hardware":"Advanced Micro Devices, Inc. [AMD/ATI] HawkPoint1","runs":4,"quality":78.9,"speed":26.8,"scenarios":{"coding_agent":77.2,"openclaw":81.8,"roleplay":80.9,"research":75.8},"updated":"2026-09-01"},{"vram":64,"model":"Qwen3.8-27B-oQ8-mtp","hardware":"Apple M5 Max","runs":4,"quality":77.7,"speed":35.6,"scenarios":{"coding_agent":77.6,"openclaw":67.4,"roleplay":84.8,"research":81.0},"updated":"2026-08-17"},{"vram":23,"model":"Qwen3.6-35B-A3B-IQ4_NL","hardware":"AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M","runs":17,"quality":75.9,"speed":164.8,"scenarios":{"coding_agent":67.1,"openclaw":74.7,"roleplay":83.0,"research":78.7},"updated":"2026-09-05"},{"vram":8,"model":"gpt-oss-20b-UD-Q8_K_XL","hardware":"NVIDIA GeForce RTX 5060","runs":3,"quality":75.6,"speed":148.4,"scenarios":{"coding_agent":70.3,"openclaw":83.8,"roleplay":71.6,"research":76.7},"updated":"2026-09-09"},{"vram":23,"model":"gemma-4-26b-a4b-q4kxl","hardware":"AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M","runs":3,"quality":69.5,"speed":169.9,"scenarios":{"coding_agent":67.9,"openclaw":75.2,"roleplay":79.9,"research":55.2},"updated":"2026-09-05"},{"vram":23,"model":"gemma4:26b-a4b-it-qat","hardware":"AMD Radeon RX 7900 XTX","runs":3,"quality":69.2,"speed":116.0,"scenarios":{"coding_agent":60.5,"openclaw":81.7,"roleplay":70.1,"research":64.3},"updated":"2026-08-24"},{"vram":10,"model":"qwen36-35b_VISION","hardware":"NVIDIA GeForce RTX 3080","runs":5,"quality":67.0,"speed":33.1,"scenarios":{"coding_agent":60.7,"openclaw":52.9,"roleplay":81.6,"research":73.0},"updated":"2026-08-26"},{"vram":16,"model":"mudler/Qwen3.6-35B-A3B-APEX-MTP-GGUF","hardware":"Advanced Micro Devices, Inc. [AMD/ATI] HawkPoint1","runs":4,"quality":66.8,"speed":31.6,"scenarios":{"coding_agent":65.7,"openclaw":59.9,"roleplay":68.0,"research":73.8},"updated":"2026-09-01"},{"vram":10,"model":"qwen35_122b","hardware":"NVIDIA GeForce RTX 3080","runs":5,"quality":66.7,"speed":15.2,"scenarios":{"coding_agent":53.7,"openclaw":56.3,"roleplay":80.3,"research":76.3},"updated":"2026-08-25"},{"vram":128,"model":"Ling-3.0-tiny-oQ8e","hardware":"Apple M4 Max","runs":4,"quality":60.1,"speed":124.9,"scenarios":{"coding_agent":47.3,"openclaw":72.3,"roleplay":45.5,"research":75.2},"updated":"2026-09-10"},{"vram":10,"model":"ornith-1.5-9b","hardware":"NVIDIA GeForce RTX 3080","runs":3,"quality":54.3,"speed":93.8,"scenarios":{"coding_agent":11.7,"openclaw":55.2,"roleplay":76.6,"research":73.8},"updated":"2026-08-25"},{"vram":64,"model":"Qwen3.8-27B-oQ2e-mtp","hardware":"Apple M5 Max","runs":3,"quality":10.3,"speed":44.0,"scenarios":{"coding_agent":0.0,"openclaw":28.0,"roleplay":7.7,"research":5.6},"updated":"2026-08-30"}]}

index.html

@@ -68,7 +68,9 @@ footer a:hover{text-decoration:underline}
     <input class="search" id="search" type="search" placeholder="Filter model name…" aria-label="Filter models">
   </div>
 
-  <div class="meta" id="meta"></div>
+  <div class="meta" id="meta">
+    <span><b>50</b> models</span><span><b>640</b> submissions in window</span><span>of <b>909</b> total on llm-bench.io</span>
+  </div>
 
   <div class="card">
     <table>
@@ -86,12 +88,513 @@ footer a:hover{text-decoration:underline}
           <th data-sort="updated" class="num">Updated</th>
         </tr>
       </thead>
-      <tbody id="rows"></tbody>
+      <tbody id="rows">
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=mtplx-qwen38-27b-optimized-quality" target="_blank" rel="noopener">mtplx-qwen38-27b-optimized-quality</a>
+        <span class="model-hw">Apple M5 Max</span></div></td>
+      <td class="num vram">128 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:87%;background:#4ade80"></i></span><b>87.2</b></td>
+      <td class="num">35.0</td>
+<td class="num sc">78.4</td>      <td class="num sc">90.6</td>      <td class="num sc">92.4</td>      <td class="num sc">87.6</td>
+      <td class="num updated">2026-09-01</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=unsloth%2FQwen3.8-27B-GGUF%3AIQ3_S" target="_blank" rel="noopener">unsloth/Qwen3.8-27B-GGUF:IQ3_S</a>
+        <span class="model-hw">NVIDIA GeForce RTX 4090</span></div></td>
+      <td class="num vram">23 GB</td>
+      <td class="num">4</td>
+      <td class="num"><span class="qbar"><i style="width:87%;background:#4ade80"></i></span><b>86.9</b></td>
+      <td class="num">108.6</td>
+<td class="num sc">83.3</td>      <td class="num sc">86.2</td>      <td class="num sc">90.3</td>      <td class="num sc">87.9</td>
+      <td class="num updated">2026-08-29</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=mtplx-qwen38-27b-optimized-quality" target="_blank" rel="noopener">mtplx-qwen38-27b-optimized-quality</a>
+        <span class="model-hw">Apple M4 Max</span></div></td>
+      <td class="num vram">64 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:86%;background:#4ade80"></i></span><b>85.7</b></td>
+      <td class="num">35.5</td>
+<td class="num sc">77.7</td>      <td class="num sc">85.2</td>      <td class="num sc">93.1</td>      <td class="num sc">86.7</td>
+      <td class="num updated">2026-09-02</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Tiel-Coder-35B-A3B-MTP-UD-Q6_K_XL" target="_blank" rel="noopener">Tiel-Coder-35B-A3B-MTP-UD-Q6_K_XL</a>
+        <span class="model-hw">AMD Radeon RX 5600 OEM/5600 XT / 5700/5700 XT + AMD Radeon RX 9060 XT</span></div></td>
+      <td class="num vram">7 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:86%;background:#4ade80"></i></span><b>85.5</b></td>
+      <td class="num">35.1</td>
+<td class="num sc">83.3</td>      <td class="num sc">90.5</td>      <td class="num sc">82.6</td>      <td class="num sc">85.6</td>
+      <td class="num updated">2026-09-04</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=unsloth%2FQwen3.8-27B-GGUF%3AQ4_K_M" target="_blank" rel="noopener">unsloth/Qwen3.8-27B-GGUF:Q4_K_M</a>
+        <span class="model-hw">NVIDIA GeForce RTX 4090</span></div></td>
+      <td class="num vram">23 GB</td>
+      <td class="num">6</td>
+      <td class="num"><span class="qbar"><i style="width:85%;background:#4ade80"></i></span><b>85.1</b></td>
+      <td class="num">95.3</td>
+<td class="num sc">78.0</td>      <td class="num sc">85.9</td>      <td class="num sc">89.0</td>      <td class="num sc">87.4</td>
+      <td class="num updated">2026-08-29</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Qwen3.8-27B-oQ4e-fp16-mtp" target="_blank" rel="noopener">Qwen3.8-27B-oQ4e-fp16-mtp</a>
+        <span class="model-hw">Apple M5 Max</span></div></td>
+      <td class="num vram">128 GB</td>
+      <td class="num">4</td>
+      <td class="num"><span class="qbar"><i style="width:85%;background:#4ade80"></i></span><b>85.1</b></td>
+      <td class="num">38.6</td>
+<td class="num sc">82.5</td>      <td class="num sc">85.1</td>      <td class="num sc">89.6</td>      <td class="num sc">83.0</td>
+      <td class="num updated">2026-08-20</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=mtplx-flash-next-bare-speed" target="_blank" rel="noopener">mtplx-flash-next-bare-speed</a>
+        <span class="model-hw">Apple M5 Max</span></div></td>
+      <td class="num vram">128 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:85%;background:#4ade80"></i></span><b>85.0</b></td>
+      <td class="num">57.5</td>
+<td class="num sc">75.6</td>      <td class="num sc">84.6</td>      <td class="num sc">93.3</td>      <td class="num sc">86.3</td>
+      <td class="num updated">2026-09-01</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=qwen3.8-27b-UD-Q4_K_XL" target="_blank" rel="noopener">qwen3.8-27b-UD-Q4_K_XL</a>
+        <span class="model-hw">AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M</span></div></td>
+      <td class="num vram">23 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:85%;background:#facc15"></i></span><b>84.9</b></td>
+      <td class="num">50.7</td>
+<td class="num sc">80.6</td>      <td class="num sc">83.7</td>      <td class="num sc">86.9</td>      <td class="num sc">88.5</td>
+      <td class="num updated">2026-09-08</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Tiel-Coder-35B-A3B-Q4_K_S" target="_blank" rel="noopener">Tiel-Coder-35B-A3B-Q4_K_S</a>
+        <span class="model-hw">AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M</span></div></td>
+      <td class="num vram">23 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:85%;background:#facc15"></i></span><b>84.6</b></td>
+      <td class="num">165.0</td>
+<td class="num sc">76.9</td>      <td class="num sc">89.3</td>      <td class="num sc">87.3</td>      <td class="num sc">84.9</td>
+      <td class="num updated">2026-09-05</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Qwen3.8-27B-UD-Q3_K_XL" target="_blank" rel="noopener">Qwen3.8-27B-UD-Q3_K_XL</a>
+        <span class="model-hw">NVIDIA GeForce RTX 5070 Ti</span></div></td>
+      <td class="num vram">15 GB</td>
+      <td class="num">8</td>
+      <td class="num"><span class="qbar"><i style="width:85%;background:#facc15"></i></span><b>84.6</b></td>
+      <td class="num">66.3</td>
+<td class="num sc">73.3</td>      <td class="num sc">86.8</td>      <td class="num sc">92.3</td>      <td class="num sc">86.2</td>
+      <td class="num updated">2026-09-04</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Qwen3.8-27B-oQ8e-fp16-mtp" target="_blank" rel="noopener">Qwen3.8-27B-oQ8e-fp16-mtp</a>
+        <span class="model-hw">Apple M5 Max</span></div></td>
+      <td class="num vram">64 GB</td>
+      <td class="num">4</td>
+      <td class="num"><span class="qbar"><i style="width:85%;background:#facc15"></i></span><b>84.6</b></td>
+      <td class="num">33.7</td>
+<td class="num sc">74.1</td>      <td class="num sc">87.4</td>      <td class="num sc">91.2</td>      <td class="num sc">85.6</td>
+      <td class="num updated">2026-08-28</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Qwen3.8-Flash-Next-oQ5e-mtp" target="_blank" rel="noopener">Qwen3.8-Flash-Next-oQ5e-mtp</a>
+        <span class="model-hw">Apple M4 Max</span></div></td>
+      <td class="num vram">128 GB</td>
+      <td class="num">10</td>
+      <td class="num"><span class="qbar"><i style="width:84%;background:#facc15"></i></span><b>84.0</b></td>
+      <td class="num">37.6</td>
+<td class="num sc">76.0</td>      <td class="num sc">84.7</td>      <td class="num sc">89.8</td>      <td class="num sc">85.5</td>
+      <td class="num updated">2026-09-09</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Qwen3.8-27B-oQ4-mtp" target="_blank" rel="noopener">Qwen3.8-27B-oQ4-mtp</a>
+        <span class="model-hw">Apple M5 Max</span></div></td>
+      <td class="num vram">64 GB</td>
+      <td class="num">4</td>
+      <td class="num"><span class="qbar"><i style="width:84%;background:#facc15"></i></span><b>83.9</b></td>
+      <td class="num">42.8</td>
+<td class="num sc">75.4</td>      <td class="num sc">89.2</td>      <td class="num sc">85.0</td>      <td class="num sc">86.1</td>
+      <td class="num updated">2026-08-28</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Muse-Glimmer-30B-oQ8e" target="_blank" rel="noopener">Muse-Glimmer-30B-oQ8e</a>
+        <span class="model-hw">Apple M5 Max</span></div></td>
+      <td class="num vram">64 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:84%;background:#facc15"></i></span><b>83.9</b></td>
+      <td class="num">17.0</td>
+<td class="num sc">72.3</td>      <td class="num sc">89.4</td>      <td class="num sc">92.0</td>      <td class="num sc">82.1</td>
+      <td class="num updated">2026-08-29</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Qwen3.8-27B-GSQ-RCO-IQ3_S" target="_blank" rel="noopener">Qwen3.8-27B-GSQ-RCO-IQ3_S</a>
+        <span class="model-hw">NVIDIA GeForce RTX 4080 SUPER</span></div></td>
+      <td class="num vram">16 GB</td>
+      <td class="num">4</td>
+      <td class="num"><span class="qbar"><i style="width:84%;background:#facc15"></i></span><b>83.8</b></td>
+      <td class="num">73.6</td>
+<td class="num sc">75.3</td>      <td class="num sc">84.7</td>      <td class="num sc">88.8</td>      <td class="num sc">86.3</td>
+      <td class="num updated">2026-09-04</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Ornith-1.5-35B-A3B-oQ8e-mtp" target="_blank" rel="noopener">Ornith-1.5-35B-A3B-oQ8e-mtp</a>
+        <span class="model-hw">Apple M5 Max</span></div></td>
+      <td class="num vram">64 GB</td>
+      <td class="num">5</td>
+      <td class="num"><span class="qbar"><i style="width:84%;background:#facc15"></i></span><b>83.7</b></td>
+      <td class="num">94.6</td>
+<td class="num sc">77.5</td>      <td class="num sc">82.0</td>      <td class="num sc">88.5</td>      <td class="num sc">86.8</td>
+      <td class="num updated">2026-09-07</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Tiel-Coder-35B-A3B-MTP-UD-IQ3_XXS" target="_blank" rel="noopener">Tiel-Coder-35B-A3B-MTP-UD-IQ3_XXS</a>
+        <span class="model-hw">NVIDIA GeForce RTX 5060</span></div></td>
+      <td class="num vram">8 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:84%;background:#facc15"></i></span><b>83.7</b></td>
+      <td class="num">90.0</td>
+<td class="num sc">78.4</td>      <td class="num sc">86.7</td>      <td class="num sc">85.4</td>      <td class="num sc">84.3</td>
+      <td class="num updated">2026-09-09</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Qwen3.8-27B-oQ4e-mtp" target="_blank" rel="noopener">Qwen3.8-27B-oQ4e-mtp</a>
+        <span class="model-hw">Apple M5 Max</span></div></td>
+      <td class="num vram">64 GB</td>
+      <td class="num">26</td>
+      <td class="num"><span class="qbar"><i style="width:84%;background:#facc15"></i></span><b>83.5</b></td>
+      <td class="num">45.3</td>
+<td class="num sc">74.2</td>      <td class="num sc">83.4</td>      <td class="num sc">90.5</td>      <td class="num sc">86.0</td>
+      <td class="num updated">2026-09-09</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=qwen3.8%3A27b-mtp-q4_K_M" target="_blank" rel="noopener">qwen3.8:27b-mtp-q4_K_M</a>
+        <span class="model-hw">AMD Radeon RX 7900 XTX</span></div></td>
+      <td class="num vram">23 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:84%;background:#facc15"></i></span><b>83.5</b></td>
+      <td class="num">40.8</td>
+<td class="num sc">71.2</td>      <td class="num sc">88.2</td>      <td class="num sc">88.1</td>      <td class="num sc">86.7</td>
+      <td class="num updated">2026-08-16</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Tiel-Coder-35B-A3B-MLX-oQ4e" target="_blank" rel="noopener">Tiel-Coder-35B-A3B-MLX-oQ4e</a>
+        <span class="model-hw">Apple M5 Max</span></div></td>
+      <td class="num vram">64 GB</td>
+      <td class="num">4</td>
+      <td class="num"><span class="qbar"><i style="width:83%;background:#facc15"></i></span><b>83.4</b></td>
+      <td class="num">118.4</td>
+<td class="num sc">80.7</td>      <td class="num sc">82.7</td>      <td class="num sc">85.6</td>      <td class="num sc">84.4</td>
+      <td class="num updated">2026-08-25</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Qwen3.8-27B-UD-IQ3_S" target="_blank" rel="noopener">Qwen3.8-27B-UD-IQ3_S</a>
+        <span class="model-hw">NVIDIA GeForce RTX 4080 SUPER</span></div></td>
+      <td class="num vram">16 GB</td>
+      <td class="num">48</td>
+      <td class="num"><span class="qbar"><i style="width:83%;background:#facc15"></i></span><b>83.2</b></td>
+      <td class="num">78.8</td>
+<td class="num sc">77.8</td>      <td class="num sc">81.6</td>      <td class="num sc">87.8</td>      <td class="num sc">85.7</td>
+      <td class="num updated">2026-09-05</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=qwen3.8%3A27b" target="_blank" rel="noopener">qwen3.8:27b</a>
+        <span class="model-hw">AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M</span></div></td>
+      <td class="num vram">23 GB</td>
+      <td class="num">5</td>
+      <td class="num"><span class="qbar"><i style="width:83%;background:#facc15"></i></span><b>82.8</b></td>
+      <td class="num">35.6</td>
+<td class="num sc">78.6</td>      <td class="num sc">82.8</td>      <td class="num sc">84.8</td>      <td class="num sc">84.9</td>
+      <td class="num updated">2026-09-07</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=qwen3.8%3Alatest" target="_blank" rel="noopener">qwen3.8:latest</a>
+        <span class="model-hw">AMD Radeon RX 7900 XTX</span></div></td>
+      <td class="num vram">23 GB</td>
+      <td class="num">7</td>
+      <td class="num"><span class="qbar"><i style="width:83%;background:#facc15"></i></span><b>82.6</b></td>
+      <td class="num">37.4</td>
+<td class="num sc">75.9</td>      <td class="num sc">82.2</td>      <td class="num sc">85.1</td>      <td class="num sc">87.1</td>
+      <td class="num updated">2026-09-07</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Qwen3.8-Flash-Next-oQ4e-mtp" target="_blank" rel="noopener">Qwen3.8-Flash-Next-oQ4e-mtp</a>
+        <span class="model-hw">Apple M4 Max</span></div></td>
+      <td class="num vram">128 GB</td>
+      <td class="num">22</td>
+      <td class="num"><span class="qbar"><i style="width:82%;background:#facc15"></i></span><b>82.2</b></td>
+      <td class="num">40.7</td>
+<td class="num sc">72.6</td>      <td class="num sc">83.5</td>      <td class="num sc">87.9</td>      <td class="num sc">84.9</td>
+      <td class="num updated">2026-09-07</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Tiel-Coder-35B-A3B-MLX-oQ4e-MTP" target="_blank" rel="noopener">Tiel-Coder-35B-A3B-MLX-oQ4e-MTP</a>
+        <span class="model-hw">Apple M5 Max</span></div></td>
+      <td class="num vram">64 GB</td>
+      <td class="num">5</td>
+      <td class="num"><span class="qbar"><i style="width:82%;background:#facc15"></i></span><b>82.1</b></td>
+      <td class="num">123.7</td>
+<td class="num sc">76.3</td>      <td class="num sc">84.4</td>      <td class="num sc">82.3</td>      <td class="num sc">85.3</td>
+      <td class="num updated">2026-09-07</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Muse-Glimmer-30B-4bit" target="_blank" rel="noopener">Muse-Glimmer-30B-4bit</a>
+        <span class="model-hw">Apple M5 Max</span></div></td>
+      <td class="num vram">64 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:82%;background:#facc15"></i></span><b>82.1</b></td>
+      <td class="num">29.9</td>
+<td class="num sc">68.6</td>      <td class="num sc">89.3</td>      <td class="num sc">89.6</td>      <td class="num sc">80.9</td>
+      <td class="num updated">2026-08-13</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=muse-glimmer%3Alatest" target="_blank" rel="noopener">muse-glimmer:latest</a>
+        <span class="model-hw">AMD Radeon RX 7900 XTX</span></div></td>
+      <td class="num vram">23 GB</td>
+      <td class="num">4</td>
+      <td class="num"><span class="qbar"><i style="width:82%;background:#facc15"></i></span><b>82.0</b></td>
+      <td class="num">34.5</td>
+<td class="num sc">71.5</td>      <td class="num sc">93.0</td>      <td class="num sc">79.7</td>      <td class="num sc">83.6</td>
+      <td class="num updated">2026-08-25</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Qwen3.8-27B-4bit" target="_blank" rel="noopener">Qwen3.8-27B-4bit</a>
+        <span class="model-hw">Apple M5 Max</span></div></td>
+      <td class="num vram">64 GB</td>
+      <td class="num">9</td>
+      <td class="num"><span class="qbar"><i style="width:82%;background:#facc15"></i></span><b>81.9</b></td>
+      <td class="num">30.6</td>
+<td class="num sc">76.8</td>      <td class="num sc">78.0</td>      <td class="num sc">89.2</td>      <td class="num sc">83.4</td>
+      <td class="num updated">2026-08-22</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Cyber-Tiel-Coder-35B-A3B-MTP-UD-IQ3_XXS" target="_blank" rel="noopener">Cyber-Tiel-Coder-35B-A3B-MTP-UD-IQ3_XXS</a>
+        <span class="model-hw">NVIDIA GeForce RTX 5060</span></div></td>
+      <td class="num vram">8 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:82%;background:#facc15"></i></span><b>81.5</b></td>
+      <td class="num">115.7</td>
+<td class="num sc">73.8</td>      <td class="num sc">85.3</td>      <td class="num sc">82.4</td>      <td class="num sc">84.7</td>
+      <td class="num updated">2026-09-10</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Qwen3.8-27B-Q4_K_XL" target="_blank" rel="noopener">Qwen3.8-27B-Q4_K_XL</a>
+        <span class="model-hw">AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M</span></div></td>
+      <td class="num vram">23 GB</td>
+      <td class="num">15</td>
+      <td class="num"><span class="qbar"><i style="width:82%;background:#facc15"></i></span><b>81.5</b></td>
+      <td class="num">72.4</td>
+<td class="num sc">78.7</td>      <td class="num sc">76.5</td>      <td class="num sc">85.7</td>      <td class="num sc">85.0</td>
+      <td class="num updated">2026-09-05</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Qwen3.8-27B-oQ8e-mtp" target="_blank" rel="noopener">Qwen3.8-27B-oQ8e-mtp</a>
+        <span class="model-hw">Apple M5 Max</span></div></td>
+      <td class="num vram">64 GB</td>
+      <td class="num">26</td>
+      <td class="num"><span class="qbar"><i style="width:81%;background:#facc15"></i></span><b>81.3</b></td>
+      <td class="num">33.4</td>
+<td class="num sc">70.7</td>      <td class="num sc">82.2</td>      <td class="num sc">88.1</td>      <td class="num sc">84.4</td>
+      <td class="num updated">2026-09-10</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=unsloth%2FQwen3.8-27B-GGUF%3AUD-Q8_K_XL" target="_blank" rel="noopener">unsloth/Qwen3.8-27B-GGUF:UD-Q8_K_XL</a>
+        <span class="model-hw">Apple M5 Max</span></div></td>
+      <td class="num vram">64 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:81%;background:#facc15"></i></span><b>80.9</b></td>
+      <td class="num">24.4</td>
+<td class="num sc">68.1</td>      <td class="num sc">80.7</td>      <td class="num sc">89.3</td>      <td class="num sc">85.4</td>
+      <td class="num updated">2026-08-21</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Ornith-1.5-35B-A3B-IQ4_XS" target="_blank" rel="noopener">Ornith-1.5-35B-A3B-IQ4_XS</a>
+        <span class="model-hw">AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M</span></div></td>
+      <td class="num vram">23 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:81%;background:#facc15"></i></span><b>80.7</b></td>
+      <td class="num">139.5</td>
+<td class="num sc">78.7</td>      <td class="num sc">81.4</td>      <td class="num sc">82.4</td>      <td class="num sc">80.2</td>
+      <td class="num updated">2026-09-05</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Qwen3.8-27B-oQ8e-fp16-mtp" target="_blank" rel="noopener">Qwen3.8-27B-oQ8e-fp16-mtp</a>
+        <span class="model-hw">Apple M2 Ultra</span></div></td>
+      <td class="num vram">128 GB</td>
+      <td class="num">13</td>
+      <td class="num"><span class="qbar"><i style="width:81%;background:#facc15"></i></span><b>80.7</b></td>
+      <td class="num">34.1</td>
+<td class="num sc">72.7</td>      <td class="num sc">77.3</td>      <td class="num sc">88.2</td>      <td class="num sc">84.6</td>
+      <td class="num updated">2026-09-04</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=qwen3_coder_next" target="_blank" rel="noopener">qwen3_coder_next</a>
+        <span class="model-hw">NVIDIA GeForce RTX 3080</span></div></td>
+      <td class="num vram">10 GB</td>
+      <td class="num">4</td>
+      <td class="num"><span class="qbar"><i style="width:80%;background:#facc15"></i></span><b>80.1</b></td>
+      <td class="num">31.2</td>
+<td class="num sc">72.5</td>      <td class="num sc">87.3</td>      <td class="num sc">85.3</td>      <td class="num sc">75.4</td>
+      <td class="num updated">2026-08-25</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=unsloth%2FNVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF%3AUD-Q4_K_XL" target="_blank" rel="noopener">unsloth/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:UD-Q4_K_XL</a>
+        <span class="model-hw">NVIDIA GeForce RTX 3080 Ti + NVIDIA GeForce RTX 3080</span></div></td>
+      <td class="num vram">20 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:80%;background:#facc15"></i></span><b>79.7</b></td>
+      <td class="num">172.4</td>
+<td class="num sc">68.0</td>      <td class="num sc">84.5</td>      <td class="num sc">83.2</td>      <td class="num sc">83.1</td>
+      <td class="num updated">2026-09-07</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=qwen38-27b" target="_blank" rel="noopener">qwen38-27b</a>
+        <span class="model-hw">AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M</span></div></td>
+      <td class="num vram">23 GB</td>
+      <td class="num">6</td>
+      <td class="num"><span class="qbar"><i style="width:80%;background:#facc15"></i></span><b>79.6</b></td>
+      <td class="num">72.7</td>
+<td class="num sc">67.5</td>      <td class="num sc">77.3</td>      <td class="num sc">88.7</td>      <td class="num sc">85.0</td>
+      <td class="num updated">2026-09-08</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Qwen3.8-27B-UD-Q6_K_XL" target="_blank" rel="noopener">Qwen3.8-27B-UD-Q6_K_XL</a>
+        <span class="model-hw">NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition</span></div></td>
+      <td class="num vram">96 GB</td>
+      <td class="num">4</td>
+      <td class="num"><span class="qbar"><i style="width:80%;background:#facc15"></i></span><b>79.5</b></td>
+      <td class="num">84.3</td>
+<td class="num sc">75.9</td>      <td class="num sc">76.9</td>      <td class="num sc">84.4</td>      <td class="num sc">80.6</td>
+      <td class="num updated">2026-08-31</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=peculiar-ragdoll%2FTiel-Coder-35B-A3B-GGUF-MTP" target="_blank" rel="noopener">peculiar-ragdoll/Tiel-Coder-35B-A3B-GGUF-MTP</a>
+        <span class="model-hw">Advanced Micro Devices, Inc. [AMD/ATI] HawkPoint1</span></div></td>
+      <td class="num vram">16 GB</td>
+      <td class="num">4</td>
+      <td class="num"><span class="qbar"><i style="width:79%;background:#facc15"></i></span><b>78.9</b></td>
+      <td class="num">26.8</td>
+<td class="num sc">77.2</td>      <td class="num sc">81.8</td>      <td class="num sc">80.9</td>      <td class="num sc">75.8</td>
+      <td class="num updated">2026-09-01</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Qwen3.8-27B-oQ8-mtp" target="_blank" rel="noopener">Qwen3.8-27B-oQ8-mtp</a>
+        <span class="model-hw">Apple M5 Max</span></div></td>
+      <td class="num vram">64 GB</td>
+      <td class="num">4</td>
+      <td class="num"><span class="qbar"><i style="width:78%;background:#facc15"></i></span><b>77.7</b></td>
+      <td class="num">35.6</td>
+<td class="num sc">77.6</td>      <td class="num sc">67.4</td>      <td class="num sc">84.8</td>      <td class="num sc">81.0</td>
+      <td class="num updated">2026-08-17</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Qwen3.6-35B-A3B-IQ4_NL" target="_blank" rel="noopener">Qwen3.6-35B-A3B-IQ4_NL</a>
+        <span class="model-hw">AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M</span></div></td>
+      <td class="num vram">23 GB</td>
+      <td class="num">17</td>
+      <td class="num"><span class="qbar"><i style="width:76%;background:#facc15"></i></span><b>75.9</b></td>
+      <td class="num">164.8</td>
+<td class="num sc">67.1</td>      <td class="num sc">74.7</td>      <td class="num sc">83.0</td>      <td class="num sc">78.7</td>
+      <td class="num updated">2026-09-05</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=gpt-oss-20b-UD-Q8_K_XL" target="_blank" rel="noopener">gpt-oss-20b-UD-Q8_K_XL</a>
+        <span class="model-hw">NVIDIA GeForce RTX 5060</span></div></td>
+      <td class="num vram">8 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:76%;background:#facc15"></i></span><b>75.6</b></td>
+      <td class="num">148.4</td>
+<td class="num sc">70.3</td>      <td class="num sc">83.8</td>      <td class="num sc">71.6</td>      <td class="num sc">76.7</td>
+      <td class="num updated">2026-09-09</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=gemma-4-26b-a4b-q4kxl" target="_blank" rel="noopener">gemma-4-26b-a4b-q4kxl</a>
+        <span class="model-hw">AMD Radeon RX 7900 XT/7900 XTX/7900 GRE/7900M</span></div></td>
+      <td class="num vram">23 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:70%;background:#f87171"></i></span><b>69.5</b></td>
+      <td class="num">169.9</td>
+<td class="num sc">67.9</td>      <td class="num sc">75.2</td>      <td class="num sc">79.9</td>      <td class="num sc">55.2</td>
+      <td class="num updated">2026-09-05</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=gemma4%3A26b-a4b-it-qat" target="_blank" rel="noopener">gemma4:26b-a4b-it-qat</a>
+        <span class="model-hw">AMD Radeon RX 7900 XTX</span></div></td>
+      <td class="num vram">23 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:69%;background:#f87171"></i></span><b>69.2</b></td>
+      <td class="num">116.0</td>
+<td class="num sc">60.5</td>      <td class="num sc">81.7</td>      <td class="num sc">70.1</td>      <td class="num sc">64.3</td>
+      <td class="num updated">2026-08-24</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=qwen36-35b_VISION" target="_blank" rel="noopener">qwen36-35b_VISION</a>
+        <span class="model-hw">NVIDIA GeForce RTX 3080</span></div></td>
+      <td class="num vram">10 GB</td>
+      <td class="num">5</td>
+      <td class="num"><span class="qbar"><i style="width:67%;background:#f87171"></i></span><b>67.0</b></td>
+      <td class="num">33.1</td>
+<td class="num sc">60.7</td>      <td class="num sc">52.9</td>      <td class="num sc">81.6</td>      <td class="num sc">73.0</td>
+      <td class="num updated">2026-08-26</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=mudler%2FQwen3.6-35B-A3B-APEX-MTP-GGUF" target="_blank" rel="noopener">mudler/Qwen3.6-35B-A3B-APEX-MTP-GGUF</a>
+        <span class="model-hw">Advanced Micro Devices, Inc. [AMD/ATI] HawkPoint1</span></div></td>
+      <td class="num vram">16 GB</td>
+      <td class="num">4</td>
+      <td class="num"><span class="qbar"><i style="width:67%;background:#f87171"></i></span><b>66.8</b></td>
+      <td class="num">31.6</td>
+<td class="num sc">65.7</td>      <td class="num sc">59.9</td>      <td class="num sc">68.0</td>      <td class="num sc">73.8</td>
+      <td class="num updated">2026-09-01</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=qwen35_122b" target="_blank" rel="noopener">qwen35_122b</a>
+        <span class="model-hw">NVIDIA GeForce RTX 3080</span></div></td>
+      <td class="num vram">10 GB</td>
+      <td class="num">5</td>
+      <td class="num"><span class="qbar"><i style="width:67%;background:#f87171"></i></span><b>66.7</b></td>
+      <td class="num">15.2</td>
+<td class="num sc">53.7</td>      <td class="num sc">56.3</td>      <td class="num sc">80.3</td>      <td class="num sc">76.3</td>
+      <td class="num updated">2026-08-25</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Ling-3.0-tiny-oQ8e" target="_blank" rel="noopener">Ling-3.0-tiny-oQ8e</a>
+        <span class="model-hw">Apple M4 Max</span></div></td>
+      <td class="num vram">128 GB</td>
+      <td class="num">4</td>
+      <td class="num"><span class="qbar"><i style="width:60%;background:#f87171"></i></span><b>60.1</b></td>
+      <td class="num">124.9</td>
+<td class="num sc">47.3</td>      <td class="num sc">72.3</td>      <td class="num sc">45.5</td>      <td class="num sc">75.2</td>
+      <td class="num updated">2026-09-10</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=ornith-1.5-9b" target="_blank" rel="noopener">ornith-1.5-9b</a>
+        <span class="model-hw">NVIDIA GeForce RTX 3080</span></div></td>
+      <td class="num vram">10 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:54%;background:#f87171"></i></span><b>54.3</b></td>
+      <td class="num">93.8</td>
+<td class="num sc">11.7</td>      <td class="num sc">55.2</td>      <td class="num sc">76.6</td>      <td class="num sc">73.8</td>
+      <td class="num updated">2026-08-25</td>
+    </tr>
+    <tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="https://llm-bench.io/benchmarks?model=Qwen3.8-27B-oQ2e-mtp" target="_blank" rel="noopener">Qwen3.8-27B-oQ2e-mtp</a>
+        <span class="model-hw">Apple M5 Max</span></div></td>
+      <td class="num vram">64 GB</td>
+      <td class="num">3</td>
+      <td class="num"><span class="qbar"><i style="width:10%;background:#f87171"></i></span><b>10.3</b></td>
+      <td class="num">44.0</td>
+<td class="num sc">0.0</td>      <td class="num sc">28.0</td>      <td class="num sc">7.7</td>      <td class="num sc">5.6</td>
+      <td class="num updated">2026-08-30</td>
+    </tr>
+    </tbody>
     </table>
   </div>
 
   <footer>
-    <span>Data: last <b id="win">–</b> days, ≥ <b id="minr">–</b> runs per model · generated <span id="gen">–</span></span>
+    <span>Data: last <b id="win">30</b> days, ≥ <b id="minr">3</b> runs per model · generated <span id="gen">10 Sep 2026</span></span>
     <span>·</span>
     <span>Methodology: <a href="https://llm-bench.io/benchmark-methodology" target="_blank" rel="noopener">how we measure</a></span>
     <span>·</span>
@@ -103,16 +606,13 @@ footer a:hover{text-decoration:underline}
 let DATA=null, sortKey="quality", sortDir=-1, vramFilter=null, query="";
 
 async function init(){
+  // Table rows + meta are pre-rendered at build time (static view for the
+  // no-JS org card / crawlers). data.json enables interactive
+  // filtering + sorting on the live Space page.
   try{
     DATA = await (await fetch("data.json")).json();
-  }catch(e){
-    document.getElementById("rows").innerHTML =
-      '<tr><td colspan="10" class="empty">Could not load data.json — try refreshing.</td></tr>';
-    return;
-  }
-  document.getElementById("gen").textContent = new Date(DATA.generatedAt).toLocaleDateString("en-GB",{day:"2-digit",month:"short",year:"numeric"});
-  document.getElementById("win").textContent = DATA.windowDays;
-  document.getElementById("minr").textContent = DATA.minRuns;
+  }catch(e){ DATA=null; }
+  if(!DATA) return;
   renderChips(); bindHeader(); bindSearch(); render();
 }
 

template.html

@@ -0,0 +1,200 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>llm-bench.io — Local LLM Leaderboard</title>
+<meta name="description" content="Live leaderboard of local LLMs benchmarked on real hardware — quality, speed, and VRAM requirements. Powered by llm-bench.io.">
+<style>
+:root{
+  --bg:#0b0e14; --panel:#11151f; --panel2:#161b28; --border:#232a3b;
+  --text:#e8ecf4; --muted:#8b94a7; --accent:#5b8cff; --accent2:#7c5bff;
+  --good:#4ade80; --mid:#facc15; --bad:#f87171;
+}
+*{box-sizing:border-box;margin:0;padding:0}
+body{background:var(--bg);color:var(--text);font-family:-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,Helvetica,Arial,sans-serif;line-height:1.5;padding:24px 16px 60px}
+.wrap{max-width:1180px;margin:0 auto}
+header{display:flex;align-items:baseline;gap:12px;flex-wrap:wrap;margin-bottom:6px}
+h1{font-size:24px;font-weight:700}
+h1 .dot{color:var(--accent)}
+.sub{color:var(--muted);font-size:13px;margin-bottom:18px}
+.sub a{color:var(--accent);text-decoration:none}
+.sub a:hover{text-decoration:underline}
+.toolbar{display:flex;gap:10px;flex-wrap:wrap;align-items:center;margin-bottom:14px}
+.chips{display:flex;gap:6px;flex-wrap:wrap}
+.chip{background:var(--panel);border:1px solid var(--border);color:var(--muted);border-radius:999px;padding:5px 12px;font-size:12.5px;cursor:pointer;user-select:none;transition:all .15s}
+.chip:hover{color:var(--text);border-color:#3a445c}
+.chip.active{background:var(--accent);border-color:var(--accent);color:#fff}
+.search{margin-left:auto;background:var(--panel);border:1px solid var(--border);border-radius:8px;color:var(--text);padding:7px 12px;font-size:13px;min-width:200px}
+.search::placeholder{color:var(--muted)}
+.meta{color:var(--muted);font-size:12px;margin-bottom:10px;display:flex;gap:16px;flex-wrap:wrap}
+.meta b{color:var(--text);font-weight:600}
+.card{background:var(--panel);border:1px solid var(--border);border-radius:12px;overflow:auto}
+table{width:100%;border-collapse:collapse;font-size:13px;min-width:900px}
+th,td{padding:9px 12px;text-align:left;border-bottom:1px solid var(--border);white-space:nowrap}
+th{position:sticky;top:0;background:var(--panel2);color:var(--muted);font-size:11.5px;text-transform:uppercase;letter-spacing:.04em;cursor:pointer;user-select:none;z-index:1}
+th:hover{color:var(--text)}
+th .arrow{font-size:10px;margin-left:3px;opacity:.7}
+td.num,th.num{text-align:right;font-variant-numeric:tabular-nums}
+tr:hover td{background:rgba(91,140,255,.05)}
+.model-cell{display:flex;flex-direction:column;gap:1px;max-width:340px}
+.model-name{font-weight:600;color:var(--text);overflow:hidden;text-overflow:ellipsis}
+.model-hw{color:var(--muted);font-size:11.5px;overflow:hidden;text-overflow:ellipsis}
+.vram{color:var(--accent2);font-weight:600}
+.qbar{display:inline-block;width:52px;height:5px;border-radius:3px;background:var(--border);vertical-align:middle;margin-right:7px;overflow:hidden}
+.qbar i{display:block;height:100%;border-radius:3px}
+.sc{color:var(--text)}
+.sc.dim{color:var(--muted)}
+.updated{color:var(--muted);font-size:11.5px}
+footer{margin-top:22px;color:var(--muted);font-size:12px;display:flex;gap:8px;flex-wrap:wrap;align-items:center}
+footer a{color:var(--accent);text-decoration:none}
+footer a:hover{text-decoration:underline}
+.empty{padding:40px;text-align:center;color:var(--muted)}
+@media(max-width:700px){ .search{margin-left:0;width:100%} }
+</style>
+</head>
+<body>
+<div class="wrap">
+  <header>
+    <h1>llm-bench<span class="dot">.io</span> — Local LLM Leaderboard</h1>
+  </header>
+  <div class="sub">
+    Real-world benchmarks from actual users — LLM-judged <b>quality</b> and <b>speed</b> across coding, agent, role-play &amp; research tasks, grouped by VRAM.
+    <a href="https://llm-bench.io" target="_blank" rel="noopener">Run your own test →</a>
+  </div>
+
+  <div class="toolbar">
+    <div class="chips" id="vramChips"></div>
+    <input class="search" id="search" type="search" placeholder="Filter model name…" aria-label="Filter models">
+  </div>
+
+  <div class="meta" id="meta">
+    __META__
+  </div>
+
+  <div class="card">
+    <table>
+      <thead>
+        <tr>
+          <th data-sort="model">Model</th>
+          <th data-sort="vram" class="num">VRAM</th>
+          <th data-sort="runs" class="num">Runs</th>
+          <th data-sort="quality" class="num">Quality</th>
+          <th data-sort="speed" class="num">Speed (tok/s)</th>
+          <th data-sort="coding_agent" class="num">Coding</th>
+          <th data-sort="openclaw" class="num">Agent</th>
+          <th data-sort="roleplay" class="num">Role-Play</th>
+          <th data-sort="research" class="num">Research</th>
+          <th data-sort="updated" class="num">Updated</th>
+        </tr>
+      </thead>
+      <tbody id="rows">
+__ROWS__
+    </tbody>
+    </table>
+  </div>
+
+  <footer>
+    <span>Data: last <b id="win">__WIN__</b> days, ≥ <b id="minr">__MINR__</b> runs per model · generated <span id="gen">__GEN__</span></span>
+    <span>·</span>
+    <span>Methodology: <a href="https://llm-bench.io/benchmark-methodology" target="_blank" rel="noopener">how we measure</a></span>
+    <span>·</span>
+    <span>Dataset on Hugging Face: <a href="https://huggingface.co/datasets/llmbenchio/benchmarks-by-vram" target="_blank" rel="noopener">llmbenchio/benchmarks-by-vram</a></span>
+  </footer>
+</div>
+
+<script>
+let DATA=null, sortKey="quality", sortDir=-1, vramFilter=null, query="";
+
+async function init(){
+  // Table rows + meta are pre-rendered at build time (static view for the
+  // no-JS org card / crawlers). data.json enables interactive
+  // filtering + sorting on the live Space page.
+  try{
+    DATA = await (await fetch("data.json")).json();
+  }catch(e){ DATA=null; }
+  if(!DATA) return;
+  renderChips(); bindHeader(); bindSearch(); render();
+}
+
+function renderChips(){
+  const vals=[...new Set(DATA.rows.map(r=>r.vram))].sort((a,b)=>a-b);
+  const box=document.getElementById("vramChips");
+  const all=document.createElement("div");
+  all.className="chip active"; all.textContent="All VRAM";
+  all.onclick=()=>{vramFilter=null;[...box.children].forEach(c=>c.classList.remove("active"));all.classList.add("active");render();};
+  box.appendChild(all);
+  for(const v of vals){
+    const c=document.createElement("div");
+    c.className="chip"; c.textContent=v+" GB";
+    c.onclick=()=>{vramFilter=v;[...box.children].forEach(x=>x.classList.remove("active"));c.classList.add("active");render();};
+    box.appendChild(c);
+  }
+}
+
+function bindHeader(){
+  document.querySelectorAll("th[data-sort]").forEach(th=>{
+    th.addEventListener("click",()=>{
+      const k=th.dataset.sort;
+      if(sortKey===k) sortDir*=-1; else {sortKey=k;sortDir=(k==="model"?1:-1);}
+      render();
+    });
+  });
+}
+function bindSearch(){
+  document.getElementById("search").addEventListener("input",e=>{query=e.target.value.toLowerCase();render();});
+}
+
+function qColor(v){ if(v==null)return "var(--bad)"; if(v>=85)return "var(--good)"; if(v>=75)return "var(--mid)"; return "var(--bad)"; }
+function esc(s){ return String(s).replace(/[&<>"']/g,c=>({"&":"&amp;","<":"&lt;",">":"&gt;",'"':"&quot;","'":"&#39;"}[c])); }
+
+function render(){
+  let rows=DATA.rows.slice();
+  if(vramFilter!=null) rows=rows.filter(r=>r.vram===vramFilter);
+  if(query) rows=rows.filter(r=>r.model.toLowerCase().includes(query));
+  rows.sort((a,b)=>{
+    let x=a[sortKey],y=b[sortKey];
+    if(x==null)x=-1; if(y==null)y=-1;
+    if(typeof x==="string") return sortDir*x.localeCompare(y);
+    return sortDir*(x-y);
+  });
+
+  const head=document.querySelector("thead tr");
+  [...head.children].forEach(th=>{
+    const t=th.querySelector(".arrow"); if(t)t.remove();
+    if(th.dataset.sort===sortKey){const a=document.createElement("span");a.className="arrow";a.textContent=sortDir<0?"▼":"▲";th.appendChild(a);}
+  });
+
+  const tb=document.getElementById("rows");
+  if(!rows.length){ tb.innerHTML='<tr><td colspan="10" class="empty">No models match this filter.</td></tr>'; return; }
+
+  tb.innerHTML=rows.map(r=>{
+    const link=`https://llm-bench.io/benchmarks?model=${encodeURIComponent(r.model)}`;
+    const sc=k=>r.scenarios[k]!=null
+      ? `<td class="num sc">${r.scenarios[k].toFixed(1)}</td>`
+      : `<td class="num sc dim">–</td>`;
+    const q=r.quality;
+    const qCell=q!=null
+      ? `<td class="num"><span class="qbar"><i style="width:${Math.max(0,Math.min(100,q))}%;background:${qColor(q)}"></i></span><b>${q.toFixed(1)}</b></td>`
+      : `<td class="num sc dim">–</td>`;
+    return `<tr style="cursor:pointer" title="Open on llm-bench.io">
+      <td><div class="model-cell"><a class="model-name" href="${link}" target="_blank" rel="noopener" onclick="event.stopPropagation()">${esc(r.model)}</a><span class="model-hw">${esc(r.hardware)}</span></div></td>
+      <td class="num vram">${r.vram} GB</td>
+      <td class="num">${r.runs}</td>
+      ${qCell}
+      <td class="num">${r.speed!=null?r.speed.toFixed(1):"–"}</td>
+      ${sc("coding_agent")}${sc("openclaw")}${sc("roleplay")}${sc("research")}
+      <td class="num updated">${r.updated}</td>
+    </tr>`;
+  }).join("");
+
+  document.getElementById("meta").innerHTML=
+    `<span><b>${rows.length}</b> models</span>`+
+    `<span><b>${DATA.inWindow}</b> submissions in window</span>`+
+    `<span>of <b>${DATA.totalSubmissions}</b> total on llm-bench.io</span>`;
+}
+
+init();
+</script>
+</body>
+</html>