Add deploy script, eval refresh script and Go-priced eval data

- scripts/deploy.sh: manual Cloudflare Pages deploy (same steps as CI)
- scripts/refresh-evals.mjs: re-scrape Artificial Analysis + OpenCode Data into evals.js
- evals.js now carries OpenCode Go list prices; Hy4 and LongCat 2.5 left empty
  rather than borrowing a neighbouring model's numbers
- workflow creates the Pages project idempotently

.gitea/workflows/pages.yml

@@ -26,8 +26,20 @@ jobs:
       - name: Stage markdown copy
         run: cp MODELS.md site/MODELS.md
 
+      - name: Ensure Pages project exists
+        env:
+          CLOUDFLARE_API_TOKEN: ${{ secrets.CLOUDFLARE_API_TOKEN }}
+          CLOUDFLARE_ACCOUNT_ID: ${{ secrets.CLOUDFLARE_ACCOUNT_ID }}
+        # Idempotent: creates the project on first run, no-ops afterwards.
+        run: npx wrangler pages project create opencodogoevals || echo "project already exists"
+
       - name: Deploy to Cloudflare Pages
         env:
           CLOUDFLARE_API_TOKEN: ${{ secrets.CLOUDFLARE_API_TOKEN }}
           CLOUDFLARE_ACCOUNT_ID: ${{ secrets.CLOUDFLARE_ACCOUNT_ID }}
-        run: npx wrangler pages deploy site --project-name opencodogoevals --branch main --commit-hash ${{ gitea.sha }}
+        run: |
+          npx wrangler pages deploy site \
+            --project-name opencodogoevals \
+            --branch main \
+            --commit-hash "${{ gitea.sha }}" \
+            --commit-message "${{ gitea.event.head_commit.message }}"

README.md

@@ -49,12 +49,20 @@ Add these as repository secrets (Gitea: *Settings → Actions → Secrets*):
 | `CLOUDFLARE_API_TOKEN` | Cloudflare API token with **Cloudflare Pages: Edit** (and Account Settings: Read) |
 | `CLOUDFLARE_ACCOUNT_ID` | The Cloudflare account that owns the Pages project |
 
-The workflow in `.gitea/workflows/pages.yml` then deploys `site/` on every push to `main`.
+The workflow in `.gitea/workflows/pages.yml` then:
+
+1. checks out the repo and installs Wrangler,
+2. stages `MODELS.md` next to the site,
+3. creates the Pages project if it does not exist yet (idempotent),
+4. deploys `site/` on every push to `main`, on a manual *Run workflow*, and hourly.
+
+A runner must be registered on the Gitea instance for the workflow to execute.
 
 ### 3. Deploy manually (optional)
 
 ```bash
-npm run deploy        # wrangler pages deploy site --project-name opencodogoevals
+./scripts/deploy.sh     # uses env vars or your wrangler login session
+npm run deploy          # same thing, wrangler only
 npm run dev             # local preview on http://localhost:8788
 ```
 
@@ -70,13 +78,13 @@ Cloudflare-compatible local server.
 
 ## Updating the data
 
-1. Benchmark scores come from each model's Artificial Analysis page
-   (`https://artificialanalysis.ai/models/<slug>`) — the embedded JSON payload in the page
-   contains the full evaluation set per model.
-2. Catalog facts (pricing, context window, modalities, release date) come from
-   `https://opencode.ai/data/<lab>/<model>.json` and the Go docs.
-3. Edit `site/assets/evals.js` and `site/assets/data.js`, then regenerate `MODELS.md`
-   from `data.js` and re-commit.
+1. `node scripts/refresh-evals.mjs` re-scrapes every model's benchmark scores from
+   Artificial Analysis and rewrites `site/assets/evals.js`. Model names, OpenCode Go
+   prices and free-tier flags come from `site/assets/data.js` (hand-maintained from the
+   Go docs), so the chart always shows Go pricing.
+2. Descriptions, modalities, release dates and caveats live in `site/assets/data.js`;
+   update them there and regenerate `MODELS.md`.
+3. Commit both files — CI publishes the site on push.
 
 ## Caveats
 

package.json

@@ -6,7 +6,8 @@
   "type": "module",
   "scripts": {
     "dev": "wrangler pages dev site --port 8788",
-    "deploy": "wrangler pages deploy site --project-name opencodegoevals --branch main",
+    "deploy": "./scripts/deploy.sh",
+    "deploy:pages": "wrangler pages deploy site --project-name opencodogoevals --branch main",
     "project:create": "wrangler pages project create opencodogoevals"
   },
   "devDependencies": {

scripts/deploy.sh

@@ -0,0 +1,30 @@
+#!/usr/bin/env bash
+# Manual deploy to Cloudflare Pages (same steps the CI workflow runs).
+#
+# Requires either:
+#   - CLOUDFLARE_API_TOKEN + CLOUDFLARE_ACCOUNT_ID exported in the environment, or
+#   - an existing `wrangler login` session (~/.wrangler/config/default.toml)
+set -euo pipefail
+
+cd "$(dirname "$0")/.."
+
+PROJECT="${CLOUDFLARE_PAGES_PROJECT:-opencodogoevals}"
+
+echo "==> staging site/"
+cp MODELS.md site/MODELS.md
+
+if [ ! -d node_modules/wrangler ]; then
+  echo "==> installing wrangler"
+  npm install --no-save wrangler@^4
+fi
+
+echo "==> ensuring Pages project '$PROJECT' exists"
+npx wrangler pages project create "$PROJECT" || echo "   (project already exists)"
+
+echo "==> deploying"
+npx wrangler pages deploy site \
+  --project-name "$PROJECT" \
+  --branch main \
+  --commit-dirty=true
+
+echo "==> done: https://$PROJECT.pages.dev"

scripts/refresh-evals.mjs

@@ -0,0 +1,179 @@
+#!/usr/bin/env node
+// Regenerates site/assets/evals.js from live sources.
+//
+//   node scripts/refresh-evals.mjs
+//
+// Sources
+//   - Artificial Analysis model pages (https://artificialanalysis.ai/models/<slug>)
+//     The benchmark payload is embedded in the page as React Server Component flight data.
+//   - OpenCode Data model JSON (https://opencode.ai/data/<lab>/<model>.json)
+//     for context windows and OpenCode Go list prices.
+//
+// Usage of the AI slug differs slightly from the OpenCode Go model id, so they are
+// mapped explicitly below.
+
+import { writeFileSync } from "node:fs";
+import { GO_MODELS } from "../site/assets/data.js";
+import { dirname, resolve } from "node:path";
+import { fileURLToPath } from "node:url";
+
+const ROOT = resolve(dirname(fileURLToPath(import.meta.url)), "..");
+const UA =
+  "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/126 Safari/537.36";
+
+// go model id -> { aa: artificial analysis slug, od: opencode data path }
+const MODELS = {
+  "minimax-m3.1-flash-free": { aa: "minimax-m3-1-flash", od: "minimax/minimax-m3-1-flash" },
+  "gemini-4-argon-contributor": { aa: "gemini-4-argon", od: "google/gemini-4-argon" },
+  "claude-haiku-5-5": { aa: "claude-haiku-5-5", od: "anthropic/claude-haiku-5-5" },
+  "beam-free": { aa: "reflection-beam", od: "reflection/beam" },
+  // No Artificial Analysis entry for these two yet — they stay empty rather than
+  // borrowing a different model's numbers (LongCat 2.0 / Hy3 are not LongCat 2.5 / Hy4).
+  "longcat-2.5-preview-free": { aa: null, od: "meituan/longcat-2-5-preview" },
+  "mimo-v2.6-pro": { aa: "mimo-v2-6-pro", od: "xiaomi/mimo-v2-6-pro" },
+  "gpt-6-luna": { aa: "gpt-6-luna", od: "openai/gpt-6-luna" },
+  "grok-4.7": { aa: "grok-4-7", od: "xai/grok-4-7" },
+  "step-5-preview": { aa: "step-5", od: "stepfun/step-5-preview" },
+  "deepseek-v4.1-flash": { aa: "deepseek-v4-1-flash", od: "deepseek/deepseek-v4-1-flash" },
+  "muse-spark-1.3-contributor": { aa: "muse-spark-1-3", od: "meta/muse-spark-1-3-contributor" },
+  "hy4-preview": { aa: null, od: "tencent/hy4-preview" },
+  "qwen3.8-flash": { aa: "qwen3-8-flash-next", od: "alibaba/qwen3-8-flash" },
+  "glm-5.3-flash": { aa: "glm-5-3-flash", od: "zhipuai/glm-5-3-flash" },
+  "glm-5.3": { aa: "glm-5-3", od: "zhipuai/glm-5-3" },
+  "qwen3.8-max": { aa: "qwen3-8-max", od: "alibaba/qwen3-8-max" },
+  "kimi-k3": { aa: "kimi-k3", od: "moonshotai/kimi-k3" },
+  "kimi-k2.7-code": { aa: "kimi-k2-7-code", od: "moonshotai/kimi-k2-7-code" },
+  "qwen3.7-plus": { aa: "qwen3-7-plus", od: "alibaba/qwen3-7-plus" },
+  "minimax-m3": { aa: "minimax-m3", od: "minimax/minimax-m3" },
+  "deepseek-v4-pro": { aa: "deepseek-v4-pro", od: "deepseek/deepseek-v4-pro" },
+  "mimo-v2.5-pro": { aa: "mimo-v2-5-pro", od: "xiaomi/mimo-v2-5-pro" },
+  "mimo-v2.5": { aa: "mimo-v2-5-0424", od: "xiaomi/mimo-v2-5" }
+};
+
+const pct = (v) => (v === null || v === undefined ? null : Math.round(v * 1000) / 10);
+
+/* --- Artificial Analysis page parsing ------------------------------------ */
+function flightPayload(html) {
+  const parts = [];
+  const re = /self\.__next_f\.push\(\[1,"/g;
+  let m;
+  while ((m = re.exec(html))) {
+    const start = re.lastIndex;
+    const end = html.indexOf('"])</script>', start);
+    if (end < 0) continue;
+    try {
+      parts.push(JSON.parse('"' + html.slice(start, end).replace(/\\\//g, "/") + '"'));
+    } catch {
+      /* ignore chunk */
+    }
+  }
+  return parts.join("");
+}
+
+function extractModelObjects(html) {
+  const payload = flightPayload(html);
+  const out = {};
+  const re = /"slug":"/g;
+  let m;
+  while ((m = re.exec(payload))) {
+    const start = payload.lastIndexOf('{"id":"', m.index);
+    if (start < 0) continue;
+    let depth = 0, inStr = false, esc = false, k = start;
+    for (k = start; k < payload.length; k++) {
+      const c = payload[k];
+      if (esc) { esc = false; continue; }
+      if (c === "\\") { esc = true; continue; }
+      if (c === '"') { inStr = !inStr; continue; }
+      if (inStr) continue;
+      if (c === "{") depth++;
+      else if (c === "}") { depth--; if (depth === 0) { k++; break; } }
+    }
+    let obj;
+    try { obj = JSON.parse(payload.slice(start, k)); } catch { continue; }
+    if (obj && obj.slug && obj.intelligenceIndex != null) out[obj.slug] = obj;
+  }
+  return out;
+}
+
+async function fetchAA(slug) {
+  const res = await fetch(`https://artificialanalysis.ai/models/${slug}`, { headers: { "user-agent": UA } });
+  if (!res.ok) return null;
+  const models = extractModelObjects(await res.text());
+  return models[slug] || null;
+}
+
+async function fetchOD(path) {
+  try {
+    const res = await fetch(`https://opencode.ai/data/${path}.json`);
+    if (!res.ok) return null;
+    return (await res.json()).model;
+  } catch {
+    return null;
+  }
+}
+
+/* --- build ---------------------------------------------------------------- */
+// Names, OpenCode Go list prices and free-tier flags come from site/assets/data.js
+// (hand-maintained from the OpenCode Go docs), so the chart always shows Go pricing
+// rather than the provider's public list price.
+const CATALOG = Object.fromEntries(GO_MODELS.map((m) => [m.id, m]));
+
+const rows = [];
+for (const [id, { aa, od }] of Object.entries(MODELS)) {
+  const [aaModel, odModel] = await Promise.all([aa ? fetchAA(aa) : null, fetchOD(od)]);
+  if (!aaModel) {
+    console.warn(`  ! no Artificial Analysis entry for ${id}${aa ? ` (slug ${aa})` : ""} — left empty`);
+    const cat = CATALOG[id];
+    rows.push({
+      id, n: cat?.name || odModel?.name || id, lab: cat?.lab || odModel?.labName || "",
+      free: cat ? !!cat.free : !(odModel?.priceUsdPer1M?.input),
+      pi: cat?.price?.in ?? odModel?.priceUsdPer1M?.input ?? 0,
+      po: cat?.price?.out ?? odModel?.priceUsdPer1M?.output ?? 0, ii: null,
+      fc: null, scicode: null, tb21: null, tb40: null, tbh: null, tau2: null, taub: null, apex: null,
+      analyst: null, auto: null, gpqa: null, critpt: null, hle: null, lcr: null, ifbench: null,
+      mmmu: null, gdpv: null, gdppdf: null, ctx: odModel?.contextTokens ?? 1048576
+    });
+    continue;
+  }
+  const cat = CATALOG[id];
+  const price = cat?.price || odModel?.priceUsdPer1M || {};
+  const ctx = odModel?.contextTokens ?? aaModel.contextWindowTokens ?? 1048576;
+  const free = cat ? !!cat.free : !(odModel?.priceUsdPer1M?.input);
+  rows.push({
+    id,
+    n: cat?.name || aaModel.name,
+    lab: cat?.lab || odModel?.labName || aaModel.name,
+    free,
+    pi: price.in ?? price.input ?? 0,
+    po: price.out ?? price.output ?? 0,
+    ii: Math.round(aaModel.intelligenceIndex * 10) / 10,
+    fc: pct(aaModel.frontierCode),
+    scicode: pct(aaModel.scicode),
+    tb21: pct(aaModel.terminalBench21),
+    tb40: pct(aaModel.terminalBench40),
+    tbh: pct(aaModel.terminalbenchHard),
+    tau2: pct(aaModel.tau2),
+    taub: pct(aaModel.tauBanking),
+    apex: pct(aaModel.apexAgents),
+    analyst: pct(aaModel.analystAgent),
+    auto: pct(aaModel.automationBenchPartialScore),
+    gpqa: pct(aaModel.gpqa),
+    critpt: pct(aaModel.critpt),
+    hle: pct(aaModel.hle),
+    lcr: pct(aaModel.lcr),
+    ifbench: pct(aaModel.ifbench),
+    mmmu: pct(aaModel.mmmuPro),
+    gdpv: pct(aaModel.gdpvalNormalized),
+    gdppdf: pct(aaModel.gdpPdfAllPass),
+    ctx
+  });
+  console.log(`  + ${id} (index ${rows.at(-1).ii})`);
+}
+
+const header = `// Evaluation data scraped from Artificial Analysis model pages (single consistent harness).
+// Values are already scaled to 0-100. Regenerate with: node scripts/refresh-evals.mjs
+export const RAW = [
+`;
+const body = rows.map((r) => " " + JSON.stringify(r)).join(",\n");
+writeFileSync(resolve(ROOT, "site/assets/evals.js"), header + body + "\n];\n");
+console.log(`\nwrote site/assets/evals.js with ${rows.length} models`);

site/assets/evals.js

@@ -1,27 +1,27 @@
 // Evaluation data scraped from Artificial Analysis model pages (single consistent harness).
-// Values are already scaled to 0-100. Retrieved 2026-10-21.
+// Values are already scaled to 0-100. Regenerate with: node scripts/refresh-evals.mjs
 export const RAW = [
- {id:"minimax-m3.1-flash-free",n:"MiniMax M3.1 Flash Free",lab:"MiniMax",free:true,pi:0,po:0,ii:42.7,fc:38.6,scicode:53.4,tb21:null,tb40:25.8,tbh:null,tau2:null,taub:null,apex:null,analyst:null,auto:72.5,gpqa:null,critpt:22.3,hle:48.0,lcr:79.7,ifbench:null,mmmu:79.1,gdpv:47.9,gdppdf:20.8,ctx:1048576},
- {id:"gemini-4-argon-contributor",n:"Gemini 4 Argon Contributor",lab:"Google",pi:4,po:20,ii:52.6,fc:43.5,scicode:61.8,tb21:null,tb40:57.1,tbh:null,tau2:null,taub:null,apex:null,analyst:48.7,auto:77.5,gpqa:null,critpt:27.1,hle:57.1,lcr:79.7,ifbench:null,mmmu:null,gdpv:56.2,gdppdf:21.8,ctx:1048576},
- {id:"claude-haiku-5-5",n:"Claude Haiku 5.5",lab:"Anthropic",pi:0.1,po:0.5,ii:43.4,fc:41.8,scicode:55.0,tb21:null,tb40:32.8,tbh:null,tau2:null,taub:null,apex:null,analyst:53.8,auto:35.4,gpqa:null,critpt:18.9,hle:44.4,lcr:82.7,ifbench:null,mmmu:null,gdpv:55.9,gdppdf:20.8,ctx:1000000},
- {id:"beam-free",n:"Beam Free",lab:"Reflection",free:true,pi:0,po:0,ii:30.3,fc:null,scicode:48.5,tb21:null,tb40:3.0,tbh:null,tau2:null,taub:null,apex:null,analyst:null,auto:48.5,gpqa:null,critpt:13.1,hle:34.7,lcr:78.0,ifbench:null,mmmu:null,gdpv:33.9,gdppdf:11.6,ctx:1048576},
- {id:"longcat-2.5-preview-free",n:"LongCat 2.5 Preview Free",lab:"Meituan",free:true,pi:0,po:0,ii:null,ctx:1000000},
- {id:"mimo-v2.6-pro",n:"MiMo-V2.6-Pro",lab:"Xiaomi",pi:0.435,po:0.87,ii:46.3,fc:45.4,scicode:60.9,tb21:null,tb40:34.8,tbh:null,tau2:null,taub:null,apex:null,analyst:37.5,auto:58.6,gpqa:null,critpt:26.6,hle:49.4,lcr:86.3,ifbench:null,mmmu:null,gdpv:59.2,gdppdf:19.2,ctx:1048576},
- {id:"gpt-6-luna",n:"GPT-6 Luna",lab:"OpenAI",pi:0.1,po:0.5,ii:38.1,fc:41.9,scicode:54.6,tb21:null,tb40:12.6,tbh:null,tau2:null,taub:null,apex:null,analyst:43.8,auto:53.2,gpqa:null,critpt:19.4,hle:38.5,lcr:83.3,ifbench:null,mmmu:79.7,gdpv:46.6,gdppdf:22.8,ctx:1050000},
- {id:"grok-4.7",n:"Grok 4.7",lab:"xAI",pi:2,po:6,ii:46.4,fc:33.8,scicode:57.4,tb21:null,tb40:25.8,tbh:null,tau2:null,taub:null,apex:null,analyst:52.5,auto:65.6,gpqa:null,critpt:17.7,hle:43.1,lcr:76.7,ifbench:null,mmmu:null,gdpv:60.6,gdppdf:20.0,ctx:500000},
- {id:"step-5-preview",n:"Step 5 Preview",lab:"StepFun",pi:1,po:2.7,ii:43.7,fc:38.4,scicode:58.9,tb21:null,tb40:33.3,tbh:null,tau2:null,taub:null,apex:38.0,analyst:35.0,auto:51.0,gpqa:null,critpt:20.9,hle:46.5,lcr:88.3,ifbench:null,mmmu:76.4,gdpv:54.3,gdppdf:14.8,ctx:1000000},
- {id:"deepseek-v4.1-flash",n:"DeepSeek V4.1 Flash",lab:"DeepSeek",pi:0.15,po:0.6,ii:39.5,fc:44.0,scicode:51.9,tb21:null,tb40:26.8,tbh:null,tau2:null,taub:null,apex:null,analyst:35.0,auto:68.9,gpqa:null,critpt:14.3,hle:39.2,lcr:84.0,ifbench:null,mmmu:77.0,gdpv:55.0,gdppdf:12.8,ctx:1000000},
- {id:"muse-spark-1.3-contributor",n:"Muse Spark 1.3 Contributor",lab:"Meta",pi:0.1,po:0.2,ii:48.1,fc:40.6,scicode:58.8,tb21:84.3,tb40:33.3,tbh:null,tau2:null,taub:50.5,apex:null,analyst:37.5,auto:57.9,gpqa:93.5,critpt:24.9,hle:48.7,lcr:83.0,ifbench:null,mmmu:null,gdpv:59.0,gdppdf:26.6,ctx:1048576},
- {id:"hy4-preview",n:"Hy4 preview",lab:"Tencent",pi:0.834,po:2.501,ii:null,ctx:1024000},
- {id:"qwen3.8-flash",n:"Qwen3.8 Flash",lab:"Alibaba",pi:0.15,po:0.47,ii:39.8,fc:null,scicode:50.6,tb21:86.1,tb40:25.3,tbh:null,tau2:null,taub:45.4,apex:null,analyst:null,auto:55.9,gpqa:92.3,critpt:11.1,hle:38.0,lcr:79.7,ifbench:null,mmmu:79.8,gdpv:56.4,gdppdf:15.6,ctx:1000000,note:"AA entry: Qwen3.8-Flash-Next"},
- {id:"glm-5.3-flash",n:"GLM-5.3-Flash",lab:"Zhipu",pi:0.15,po:0.5,ii:41.8,fc:41.0,scicode:51.6,tb21:84.3,tb40:32.8,tbh:null,tau2:null,taub:47.2,apex:null,analyst:25.0,auto:60.4,gpqa:91.2,critpt:15.4,hle:39.9,lcr:80.0,ifbench:null,mmmu:null,gdpv:57.1,gdppdf:15.4,ctx:1000000},
- {id:"glm-5.3",n:"GLM-5.3",lab:"Zhipu",pi:1.4,po:4.4,ii:44.8,fc:40.1,scicode:59.0,tb21:83.9,tb40:41.9,tbh:null,tau2:null,taub:50.3,apex:null,analyst:38.8,auto:62.2,gpqa:91.7,critpt:19.1,hle:42.3,lcr:79.7,ifbench:null,mmmu:null,gdpv:57.5,gdppdf:11.2,ctx:1000000},
- {id:"qwen3.8-max",n:"Qwen3.8 Max",lab:"Alibaba",pi:2,po:6,ii:45.4,fc:43.2,scicode:52.1,tb21:88.8,tb40:38.9,tbh:null,tau2:null,taub:47.8,apex:42.4,analyst:45.0,auto:56.2,gpqa:92.8,critpt:17.7,hle:43.1,lcr:80.3,ifbench:null,mmmu:82.8,gdpv:58.2,gdppdf:22.8,ctx:1000000},
- {id:"kimi-k3",n:"Kimi K3",lab:"Moonshot",pi:3,po:15,ii:43.6,fc:43.6,scicode:59.5,tb21:85.0,tb40:12.6,tbh:null,tau2:null,taub:46.0,apex:41.3,analyst:38.8,auto:58.3,gpqa:93.5,critpt:23.4,hle:46.9,lcr:88.7,ifbench:null,mmmu:80.5,gdpv:51.6,gdppdf:22.0,ctx:1048576},
- {id:"kimi-k2.7-code",n:"Kimi K2.7 Code",lab:"Moonshot",pi:0.95,po:4,ii:25.8,fc:null,scicode:47.8,tb21:67.4,tb40:1.0,tbh:44.7,tau2:90.1,taub:20.2,apex:null,analyst:null,auto:24.5,gpqa:89.6,critpt:10.0,hle:35.0,lcr:79.3,ifbench:63.1,mmmu:null,gdpv:26.9,gdppdf:11.2,ctx:262144},
- {id:"qwen3.7-plus",n:"Qwen3.7 Plus",lab:"Alibaba",pi:0.4,po:1.6,ii:25.2,fc:null,scicode:46.1,tb21:61.0,tb40:1.0,tbh:47.0,tau2:93.0,taub:17.5,apex:22.4,analyst:null,auto:17.4,gpqa:90.0,critpt:9.1,hle:35.6,lcr:73.0,ifbench:78.0,mmmu:80.5,gdpv:13.4,gdppdf:12.2,ctx:1000000},
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