diff --git a/agent-evolution/index.html b/agent-evolution/index.html
index f0d8fde..02f54fd 100644
--- a/agent-evolution/index.html
+++ b/agent-evolution/index.html
@@ -1015,6 +1015,25 @@ const INLINE_RECOMMENDATIONS = [
     { agent: "memory-manager", current_model: "ollama-cloud/qwen3.6-plus", impact: "low", score_before: 87, score_after: 87, score_delta: 0, rationale: "memory-manager on qwen3.6-plus (87) is the best fit. 1M context critical." }
 ];
 
+// Inline benchmark data (fallback when embedded data doesn't have model_benchmarks)
+const MODEL_BENCHMARKS = {
+    "qwen3.5-122b": { "if_score": 92, "swe_bench": null, "context_window": 128 },
+    "qwen3-coder-480b": { "if_score": 88, "swe_bench": 66.5, "context_window": 1000 },
+    "deepseek-v4-pro-max": { "if_score": 89, "swe_bench": 80.6, "context_window": 1000 },
+    "deepseek-v4-flash": { "if_score": 86, "swe_bench": 79, "context_window": 1000 },
+    "kimi-k2.6": { "if_score": 91, "swe_bench": 80.2, "context_window": 1000 },
+    "kimi-k2.5": { "if_score": 90, "swe_bench": 78, "context_window": 256 },
+    "minimax-m2.5": { "if_score": 82, "swe_bench": 80.2, "context_window": 128 },
+    "minimax-m2.7": { "if_score": 80, "swe_bench": 78, "context_window": 128 },
+    "glm-5.1": { "if_score": 90, "swe_bench": null, "context_window": 128 },
+    "glm-5": { "if_score": 90, "swe_bench": null, "context_window": 128 },
+    "nemotron-3-super": { "if_score": 78, "swe_bench": 60.5, "context_window": 1000 },
+    "nemotron-3-nano": { "if_score": 68, "swe_bench": null, "context_window": 128 },
+    "gemma4-27b": { "if_score": 85, "swe_bench": null, "context_window": 128 },
+    "devstral-2": { "if_score": 80, "swe_bench": null, "context_window": 128 },
+    "devstral-small-2": { "if_score": 75, "swe_bench": null, "context_window": 128 }
+};
+
 // Default embedded data (minimal - updated by sync script)
 const EMBEDDED_DATA = {
     "$schema": "./data/agent-versions.schema.json",
@@ -1714,17 +1733,26 @@ function renderModelsTab(agent) {
 
 // Compute score for any model name using benchmark lookup + fallback
 function computeAgentScore(modelName) {
-    const bm = (agentData.model_benchmarks || {});
+    const bm = Object.keys(agentData.model_benchmarks || {}).length > 0
+        ? agentData.model_benchmarks
+        : MODEL_BENCHMARKS;
     const key = Object.keys(bm).find(k => modelName.includes(k)) || '';
     if (bm[key]) {
         const m = bm[key];
-        let score = (m.if_score || 70) * 0.6 + (m.swe_bench || 0) * 0.3;
+        let score;
+        if (m.swe_bench && m.swe_bench > 0) {
+            score = (m.if_score || 70) * 0.5 + (m.swe_bench) * 0.3;
+        } else {
+            // No SWE: weight IF heavily (reasoning-only models)
+            score = (m.if_score || 70) * 0.85;
+        }
         const ctx = m.context_window || 128;
         score += ctx >= 1000 ? 15 : ctx >= 256 ? 8 : 4;
-        return Math.round(score);
+        return Math.round(Math.min(100, score));
     }
+    // Fallback: deterministic but reasonable
     const hash = modelName.split('').reduce((a, c) => a + c.charCodeAt(0), 0);
-    return 50 + (hash % 35);
+    return 55 + (hash % 25);
 }
 
 // Chart 1: Agent Score Bar Chart