diff --git a/agents/model_compare.py b/agents/model_compare.py
new file mode 100644
index 0000000..1c48041
--- /dev/null
+++ b/agents/model_compare.py
@@ -0,0 +1,247 @@
+"""P2.8 — Model Compare Agent.
+
+Given N candidate models (primary + alternatives collected during P2), score
+each along cheap static metrics plus an LLM-judged ranking, then write a
+consolidated comparison + selected winner back into context_store.
+
+Design notes:
+- This stage never overwrites P2's `primary_model`. It adds a sibling field
+  `comparison_v2` (so the existing P2 comparison stays intact for diffing),
+  plus a top-level `selected_model_id` the downstream stages can opt into.
+- Runs as `on_error="skip"`: if no candidates exist, or the LLM call fails,
+  the pipeline should continue with P2's original choice, not halt.
+- Cheap metrics (equation count, variable count, LaTeX presence) are computed
+  locally so even when the LLM is unreachable we still emit structured output.
+"""
+
+from __future__ import annotations
+
+import json
+import re
+from dataclasses import asdict, dataclass, field
+from pathlib import Path
+from typing import Any
+
+from dotenv import load_dotenv
+
+load_dotenv(Path(__file__).parent.parent / ".env")
+
+from agents.orchestrator import call_model, load_context, save_context
+from agents.utils import parse_json as _parse_json
+
+SYSTEM_RANK = """你是一位数学建模竞赛评委。
+给定 N 个候选模型（每个含 model_name / model_type / equations_latex / variables / assumptions 等），
+从以下维度打分并排名：
+  - rigor       数学严谨性 1-10
+  - feasibility 可实现性   1-10
+  - fit         与题目匹配度 1-10
+  - score_pot   得分潜力     1-10
+
+输出严格 JSON（不含 markdown 代码块）：
+{
+  "ranking": [
+    {"model_id": "候选ID", "rigor": 1-10, "feasibility": 1-10,
+     "fit": 1-10, "score_pot": 1-10, "total": int, "note": "一句话评语"}
+  ],
+  "winner_id": "得分最高的 model_id",
+  "reason": "为什么它最优"
+}
+"""
+
+
+@dataclass(frozen=True)
+class Candidate:
+    model_id: str
+    model: dict
+    metrics: dict
+
+    def to_prompt_dict(self) -> dict:
+        return {"model_id": self.model_id, **_trim(self.model), "metrics": self.metrics}
+
+
+@dataclass
+class CompareResult:
+    candidates: list[dict] = field(default_factory=list)
+    ranking: list[dict] = field(default_factory=list)
+    winner_id: str = ""
+    reason: str = ""
+    method: str = "llm+metrics"   # or "metrics_only" / "single"
+
+    def to_dict(self) -> dict:
+        return asdict(self)
+
+
+def _trim(model: dict, max_str: int = 800) -> dict:
+    """Shrink large string fields so the prompt stays within budget."""
+    out: dict[str, Any] = {}
+    for k, v in model.items():
+        if isinstance(v, str) and len(v) > max_str:
+            out[k] = v[:max_str] + "…"
+        else:
+            out[k] = v
+    return out
+
+
+def _count_latex_equations(text: str) -> int:
+    if not text:
+        return 0
+    count = len(re.findall(r"\\begin\{(equation|align|gather)", text))
+    count += text.count("$$") // 2
+    return count or (1 if text.strip() else 0)
+
+
+def _compute_metrics(model: dict) -> dict:
+    """Cheap static signals — no LLM, always available."""
+    latex = model.get("equations_latex") or model.get("equations") or ""
+    if isinstance(latex, list):
+        latex = "\n".join(str(x) for x in latex)
+    variables = model.get("variables", {})
+    assumptions = model.get("assumptions", [])
+    constraints = model.get("constraints", [])
+
+    return {
+        "equation_count": _count_latex_equations(str(latex)),
+        "variable_count": len(variables) if hasattr(variables, "__len__") else 0,
+        "assumption_count": len(assumptions) if isinstance(assumptions, list) else 0,
+        "constraint_count": len(constraints) if isinstance(constraints, list) else 0,
+        "has_latex": bool(latex),
+        "has_solution_method": bool(model.get("solution_method")),
+    }
+
+
+def _collect_candidates(ctx: dict) -> list[Candidate]:
+    """Gather candidate models from context. Returns empty list if none found."""
+    modeling = ctx.get("modeling", {}) if isinstance(ctx, dict) else {}
+    seen_names: set[str] = set()
+    out: list[Candidate] = []
+
+    def _push(model: dict, suffix: str) -> None:
+        if not isinstance(model, dict) or not model:
+            return
+        name = str(model.get("model_name") or model.get("name") or suffix)
+        if name in seen_names:
+            return
+        seen_names.add(name)
+        out.append(Candidate(
+            model_id=f"M{len(out) + 1}:{name}"[:80],
+            model=model,
+            metrics=_compute_metrics(model),
+        ))
+
+    primary = modeling.get("primary_model") or modeling.get("primary")
+    if primary:
+        _push(primary, "primary")
+
+    for i, alt in enumerate(modeling.get("alternative_models", []) or []):
+        _push(alt, f"alt_{i+1}")
+
+    # Optional user-seeded list
+    for i, c in enumerate(modeling.get("candidates", []) or []):
+        _push(c, f"cand_{i+1}")
+
+    return out
+
+
+def _rank_llm(candidates: list[Candidate]) -> dict:
+    """Ask the LLM for a ranked comparison. Returns dict or empty on failure."""
+    payload = [c.to_prompt_dict() for c in candidates]
+    user_prompt = "候选模型列表：\n" + json.dumps(payload, ensure_ascii=False, indent=2)
+    try:
+        raw = call_model(SYSTEM_RANK, user_prompt, task="modeling")
+        return _parse_json(raw) or {}
+    except Exception as exc:
+        print(f"  [P2.8] LLM 排名失败，降级为纯指标打分: {exc}")
+        return {}
+
+
+def _metric_fallback(candidates: list[Candidate]) -> dict:
+    """Deterministic fallback ranking when LLM is unavailable.
+
+    Score = equation_count + variable_count + constraint_count,
+    plus bonuses for has_latex / has_solution_method. Ties broken by
+    original collection order (primary first).
+    """
+    scored = []
+    for idx, c in enumerate(candidates):
+        m = c.metrics
+        total = (
+            m["equation_count"]
+            + m["variable_count"]
+            + m["constraint_count"]
+            + (2 if m["has_latex"] else 0)
+            + (2 if m["has_solution_method"] else 0)
+        )
+        scored.append({
+            "model_id": c.model_id,
+            "rigor": min(10, m["equation_count"] + 2),
+            "feasibility": 5 + (2 if m["has_solution_method"] else 0),
+            "fit": 5,
+            "score_pot": 5 + (2 if m["has_latex"] else 0),
+            "total": total,
+            "note": "指标兜底打分（LLM 不可用）",
+            "_order": idx,
+        })
+    scored.sort(key=lambda r: (-r["total"], r["_order"]))
+    for r in scored:
+        r.pop("_order", None)
+    winner = scored[0] if scored else {}
+    return {
+        "ranking": scored,
+        "winner_id": winner.get("model_id", ""),
+        "reason": "纯指标打分：方程/变量/约束数量加权" if scored else "",
+    }
+
+
+class ModelCompareAgent:
+    """P2.8 — multi-model comparison, adds `comparison_v2` to context."""
+
+    def run(self) -> dict:
+        ctx = load_context()
+        candidates = _collect_candidates(ctx)
+
+        result = CompareResult(candidates=[c.to_prompt_dict() for c in candidates])
+
+        if not candidates:
+            result.method = "skipped"
+            result.reason = "无候选模型（modeling.primary_model / alternative_models 均为空）"
+            print("  [P2.8] 未发现候选模型，跳过对比")
+            return self._write(ctx, result)
+
+        if len(candidates) == 1:
+            result.method = "single"
+            result.winner_id = candidates[0].model_id
+            result.reason = "只有一个候选模型"
+            result.ranking = [{
+                "model_id": candidates[0].model_id, "total": 0,
+                "note": "唯一候选，无需对比",
+            }]
+            print(f"  [P2.8] 仅 1 个候选（{candidates[0].model_id}），直接选定")
+            return self._write(ctx, result)
+
+        llm = _rank_llm(candidates)
+        if llm.get("ranking"):
+            result.ranking = llm["ranking"]
+            result.winner_id = llm.get("winner_id") or (
+                llm["ranking"][0].get("model_id", "") if llm["ranking"] else ""
+            )
+            result.reason = llm.get("reason", "")
+            result.method = "llm+metrics"
+        else:
+            fb = _metric_fallback(candidates)
+            result.ranking = fb["ranking"]
+            result.winner_id = fb["winner_id"]
+            result.reason = fb["reason"]
+            result.method = "metrics_only"
+
+        print(f"  [P2.8] 对比 {len(candidates)} 个候选，winner={result.winner_id} ({result.method})")
+        return self._write(ctx, result)
+
+    @staticmethod
+    def _write(ctx: dict, result: CompareResult) -> dict:
+        modeling = ctx.setdefault("modeling", {})
+        modeling["comparison_v2"] = result.to_dict()
+        if result.winner_id:
+            modeling["selected_model_id"] = result.winner_id
+        ctx["phase"] = "P2.8_complete"
+        save_context(ctx)
+        return ctx
diff --git a/main.py b/main.py
index 0153818..cd82e6f 100644
--- a/main.py
+++ b/main.py
@@ -11,6 +11,7 @@
 from agents.question_extractor import QuestionExtractor
 from agents.data_cleaning_agent import DataCleaningAgent
 from agents.modeling_agent import ModelingAgent
+from agents.model_compare import ModelCompareAgent
 from agents.matlab_viz import MatlabVizAgent
 from agents.viz3d import Viz3DAgent
 from agents.code_agent import CodeAgent
@@ -94,6 +95,15 @@ def p2(ctx: dict) -> PhaseOutcome:
         )
         return PhaseOutcome(ctx=new_ctx, note=note)
 
+    def p2_8(ctx: dict) -> PhaseOutcome:
+        new_ctx = ModelCompareAgent().run()
+        cmp = new_ctx.get("modeling", {}).get("comparison_v2", {})
+        n = len(cmp.get("candidates", []))
+        winner = cmp.get("winner_id", "?")
+        method = cmp.get("method", "?")
+        note = f"候选 {n} 个, winner={winner} ({method})" if n else "无候选模型，跳过"
+        return PhaseOutcome(ctx=new_ctx, note=note)
+
     def p2_5(ctx: dict) -> PhaseOutcome:
         viz_result = MatlabVizAgent().run(ctx=ctx)
         n_viz = len(viz_result.get("figures", []))
@@ -161,6 +171,7 @@ def p5_5(ctx: dict) -> PhaseOutcome:
         PhaseSpec(name="P1", run=p1, record_experience=True, description="题目解析 + 三手分发"),
         PhaseSpec(name="P1.5", run=p1_5, record_experience=True, description="数据清洗 + EDA"),
         PhaseSpec(name="P2", run=p2, record_experience=True, description="数学建模"),
+        PhaseSpec(name="P2.8", run=p2_8, on_error="skip", description="多模型对比（LLM + 指标）"),
         PhaseSpec(name="P2.5", run=p2_5, on_error="skip", description="MATLAB 风格可视化"),
         PhaseSpec(name="P2.7", run=p2_7, on_error="skip", description="3D 建模（PyVista + Plotly + Octave）"),
         PhaseSpec(name="P3", run=p3, record_experience=True, description="代码求解"),
@@ -246,7 +257,7 @@ def run_pipeline(start_phase: str = "P0b", selected_problem: str | None = None)
     parser.add_argument(
         "--start",
         default="P0b",
-        choices=["P0b", "P1", "P1.5", "P2", "P2.5", "P2.7", "P3", "P3.5", "P4", "P4.5", "P5", "P5.5"],
+        choices=["P0b", "P1", "P1.5", "P2", "P2.8", "P2.5", "P2.7", "P3", "P3.5", "P4", "P4.5", "P5", "P5.5"],
         help="起始阶段，默认 P0b",
     )
     parser.add_argument(