diff --git a/src/config.py b/src/config.py
index 4de9ad46..ad40c1bb 100644
--- a/src/config.py
+++ b/src/config.py
@@ -422,6 +422,14 @@ class Settings(BaseSettings):
     # Alert threshold: free disk below this triggers cleanup / alert (GB).
     hermes_disk_free_min_gb: float = 10.0
 
+    # ── Energy Budget Monitoring ───────────────────────────────────────
+    # Enable energy budget monitoring (tracks CPU/GPU power during inference).
+    energy_budget_enabled: bool = True
+    # Watts threshold that auto-activates low power mode (on-battery only).
+    energy_budget_watts_threshold: float = 15.0
+    # Model to prefer in low power mode (smaller = more efficient).
+    energy_low_power_model: str = "qwen3:1b"
+
     # ── Error Logging ─────────────────────────────────────────────────
     error_log_enabled: bool = True
     error_log_dir: str = "logs"
diff --git a/src/dashboard/app.py b/src/dashboard/app.py
index 3ee815f9..f510551e 100644
--- a/src/dashboard/app.py
+++ b/src/dashboard/app.py
@@ -37,6 +37,7 @@ from dashboard.routes.db_explorer import router as db_explorer_router
 from dashboard.routes.discord import router as discord_router
 from dashboard.routes.experiments import router as experiments_router
 from dashboard.routes.grok import router as grok_router
+from dashboard.routes.energy import router as energy_router
 from dashboard.routes.health import router as health_router
 from dashboard.routes.hermes import router as hermes_router
 from dashboard.routes.loop_qa import router as loop_qa_router
@@ -673,6 +674,7 @@ app.include_router(matrix_router)
 app.include_router(tower_router)
 app.include_router(daily_run_router)
 app.include_router(hermes_router)
+app.include_router(energy_router)
 app.include_router(quests_router)
 app.include_router(scorecards_router)
 app.include_router(sovereignty_metrics_router)
diff --git a/src/dashboard/routes/energy.py b/src/dashboard/routes/energy.py
new file mode 100644
index 00000000..ae83d958
--- /dev/null
+++ b/src/dashboard/routes/energy.py
@@ -0,0 +1,121 @@
+"""Energy Budget Monitoring routes.
+
+Exposes the energy budget monitor via REST API so the dashboard and
+external tools can query power draw, efficiency scores, and toggle
+low power mode.
+
+Refs: #1009
+"""
+
+import logging
+
+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+
+from config import settings
+from infrastructure.energy.monitor import energy_monitor
+
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/energy", tags=["energy"])
+
+
+class LowPowerRequest(BaseModel):
+    """Request body for toggling low power mode."""
+
+    enabled: bool
+
+
+class InferenceEventRequest(BaseModel):
+    """Request body for recording an inference event."""
+
+    model: str
+    tokens_per_second: float
+
+
+@router.get("/status")
+async def energy_status():
+    """Return the current energy budget status.
+
+    Returns the live power estimate, efficiency score (0–10), recent
+    inference samples, and whether low power mode is active.
+    """
+    if not getattr(settings, "energy_budget_enabled", True):
+        return {
+            "enabled": False,
+            "message": "Energy budget monitoring is disabled (ENERGY_BUDGET_ENABLED=false)",
+        }
+
+    report = await energy_monitor.get_report()
+    return {**report.to_dict(), "enabled": True}
+
+
+@router.get("/report")
+async def energy_report():
+    """Detailed energy budget report with all recent samples.
+
+    Same as /energy/status but always includes the full sample history.
+    """
+    if not getattr(settings, "energy_budget_enabled", True):
+        raise HTTPException(status_code=503, detail="Energy budget monitoring is disabled")
+
+    report = await energy_monitor.get_report()
+    data = report.to_dict()
+    # Override recent_samples to include the full window (not just last 10)
+    data["recent_samples"] = [
+        {
+            "timestamp": s.timestamp,
+            "model": s.model,
+            "tokens_per_second": round(s.tokens_per_second, 1),
+            "estimated_watts": round(s.estimated_watts, 2),
+            "efficiency": round(s.efficiency, 3),
+            "efficiency_score": round(s.efficiency_score, 2),
+        }
+        for s in list(energy_monitor._samples)
+    ]
+    return {**data, "enabled": True}
+
+
+@router.post("/low-power")
+async def set_low_power_mode(body: LowPowerRequest):
+    """Enable or disable low power mode.
+
+    In low power mode the cascade router is advised to prefer the
+    configured energy_low_power_model (see settings).
+    """
+    if not getattr(settings, "energy_budget_enabled", True):
+        raise HTTPException(status_code=503, detail="Energy budget monitoring is disabled")
+
+    energy_monitor.set_low_power_mode(body.enabled)
+    low_power_model = getattr(settings, "energy_low_power_model", "qwen3:1b")
+    return {
+        "low_power_mode": body.enabled,
+        "preferred_model": low_power_model if body.enabled else None,
+        "message": (
+            f"Low power mode {'enabled' if body.enabled else 'disabled'}. "
+            + (f"Routing to {low_power_model}." if body.enabled else "Routing restored to default.")
+        ),
+    }
+
+
+@router.post("/record")
+async def record_inference_event(body: InferenceEventRequest):
+    """Record an inference event for efficiency tracking.
+
+    Called after each LLM inference completes.  Updates the rolling
+    efficiency score and may auto-activate low power mode if watts
+    exceed the configured threshold.
+    """
+    if not getattr(settings, "energy_budget_enabled", True):
+        return {"recorded": False, "message": "Energy budget monitoring is disabled"}
+
+    if body.tokens_per_second <= 0:
+        raise HTTPException(status_code=422, detail="tokens_per_second must be positive")
+
+    sample = energy_monitor.record_inference(body.model, body.tokens_per_second)
+    return {
+        "recorded": True,
+        "efficiency_score": round(sample.efficiency_score, 2),
+        "estimated_watts": round(sample.estimated_watts, 2),
+        "low_power_mode": energy_monitor.low_power_mode,
+    }
diff --git a/src/infrastructure/energy/__init__.py b/src/infrastructure/energy/__init__.py
new file mode 100644
index 00000000..64451607
--- /dev/null
+++ b/src/infrastructure/energy/__init__.py
@@ -0,0 +1,8 @@
+"""Energy Budget Monitoring — power-draw estimation for LLM inference.
+
+Refs: #1009
+"""
+
+from infrastructure.energy.monitor import EnergyBudgetMonitor, energy_monitor
+
+__all__ = ["EnergyBudgetMonitor", "energy_monitor"]
diff --git a/src/infrastructure/energy/monitor.py b/src/infrastructure/energy/monitor.py
new file mode 100644
index 00000000..a43ce4ac
--- /dev/null
+++ b/src/infrastructure/energy/monitor.py
@@ -0,0 +1,371 @@
+"""Energy Budget Monitor — estimates GPU/CPU power draw during LLM inference.
+
+Tracks estimated power consumption to optimize for "metabolic efficiency".
+Three estimation strategies attempted in priority order:
+
+  1. Battery discharge via ioreg (macOS — works without sudo, on-battery only)
+  2. CPU utilisation proxy via sysctl hw.cpufrequency + top
+  3. Model-size heuristic (tokens/s × model_size_gb × 2W/GB estimate)
+
+Energy Efficiency score (0–10):
+  efficiency = tokens_per_second / estimated_watts, normalised to 0–10.
+
+Low Power Mode:
+  Activated manually or automatically when draw exceeds the configured
+  threshold.  In low power mode the cascade router is advised to prefer the
+  configured low_power_model (e.g. qwen3:1b or similar compact model).
+
+Refs: #1009
+"""
+
+import asyncio
+import json
+import logging
+import subprocess
+import time
+from collections import deque
+from dataclasses import dataclass, field
+from datetime import UTC, datetime
+from typing import Any
+
+from config import settings
+
+logger = logging.getLogger(__name__)
+
+# Approximate model-size lookup (GB) used for heuristic power estimate.
+# Keys are lowercase substring matches against the model name.
+_MODEL_SIZE_GB: dict[str, float] = {
+    "qwen3:1b": 0.8,
+    "qwen3:3b": 2.0,
+    "qwen3:4b": 2.5,
+    "qwen3:8b": 5.5,
+    "qwen3:14b": 9.0,
+    "qwen3:30b": 20.0,
+    "qwen3:32b": 20.0,
+    "llama3:8b": 5.5,
+    "llama3:70b": 45.0,
+    "mistral:7b": 4.5,
+    "gemma3:4b": 2.5,
+    "gemma3:12b": 8.0,
+    "gemma3:27b": 17.0,
+    "phi4:14b": 9.0,
+}
+_DEFAULT_MODEL_SIZE_GB = 5.0  # fallback when model not in table
+_WATTS_PER_GB_HEURISTIC = 2.0  # rough W/GB for Apple Silicon unified memory
+
+# Efficiency score normalisation: score 10 at this efficiency (tok/s per W).
+_EFFICIENCY_SCORE_CEILING = 5.0  # tok/s per W → score 10
+
+# Rolling window for recent samples
+_HISTORY_MAXLEN = 60
+
+
+@dataclass
+class InferenceSample:
+    """A single inference event captured by record_inference()."""
+
+    timestamp: str
+    model: str
+    tokens_per_second: float
+    estimated_watts: float
+    efficiency: float  # tokens/s per watt
+    efficiency_score: float  # 0–10
+
+
+@dataclass
+class EnergyReport:
+    """Snapshot of current energy budget state."""
+
+    timestamp: str
+    low_power_mode: bool
+    current_watts: float
+    strategy: str  # "battery", "cpu_proxy", "heuristic", "unavailable"
+    efficiency_score: float  # 0–10; -1 if no inference samples yet
+    recent_samples: list[InferenceSample]
+    recommendation: str
+    details: dict[str, Any] = field(default_factory=dict)
+
+    def to_dict(self) -> dict[str, Any]:
+        return {
+            "timestamp": self.timestamp,
+            "low_power_mode": self.low_power_mode,
+            "current_watts": round(self.current_watts, 2),
+            "strategy": self.strategy,
+            "efficiency_score": round(self.efficiency_score, 2),
+            "recent_samples": [
+                {
+                    "timestamp": s.timestamp,
+                    "model": s.model,
+                    "tokens_per_second": round(s.tokens_per_second, 1),
+                    "estimated_watts": round(s.estimated_watts, 2),
+                    "efficiency": round(s.efficiency, 3),
+                    "efficiency_score": round(s.efficiency_score, 2),
+                }
+                for s in self.recent_samples
+            ],
+            "recommendation": self.recommendation,
+            "details": self.details,
+        }
+
+
+class EnergyBudgetMonitor:
+    """Estimates power consumption and tracks LLM inference efficiency.
+
+    All blocking I/O (subprocess calls) is wrapped in asyncio.to_thread()
+    so the event loop is never blocked.  Results are cached.
+
+    Usage::
+
+        # Record an inference event
+        energy_monitor.record_inference("qwen3:8b", tokens_per_second=42.0)
+
+        # Get the current report
+        report = await energy_monitor.get_report()
+
+        # Toggle low power mode
+        energy_monitor.set_low_power_mode(True)
+    """
+
+    _POWER_CACHE_TTL = 10.0  # seconds between fresh power readings
+
+    def __init__(self) -> None:
+        self._low_power_mode: bool = False
+        self._samples: deque[InferenceSample] = deque(maxlen=_HISTORY_MAXLEN)
+        self._cached_watts: float = 0.0
+        self._cached_strategy: str = "unavailable"
+        self._cache_ts: float = 0.0
+
+    # ── Public API ────────────────────────────────────────────────────────────
+
+    @property
+    def low_power_mode(self) -> bool:
+        return self._low_power_mode
+
+    def set_low_power_mode(self, enabled: bool) -> None:
+        """Enable or disable low power mode."""
+        self._low_power_mode = enabled
+        state = "enabled" if enabled else "disabled"
+        logger.info("Energy budget: low power mode %s", state)
+
+    def record_inference(self, model: str, tokens_per_second: float) -> InferenceSample:
+        """Record an inference event for efficiency tracking.
+
+        Call this after each LLM inference completes with the model name and
+        measured throughput.  The current power estimate is used to compute
+        the efficiency score.
+
+        Args:
+            model:              Ollama model name (e.g. "qwen3:8b").
+            tokens_per_second:  Measured decode throughput.
+
+        Returns:
+            The recorded InferenceSample.
+        """
+        watts = self._cached_watts if self._cached_watts > 0 else self._estimate_watts_sync(model)
+        efficiency = tokens_per_second / max(watts, 0.1)
+        score = min(10.0, (efficiency / _EFFICIENCY_SCORE_CEILING) * 10.0)
+
+        sample = InferenceSample(
+            timestamp=datetime.now(UTC).isoformat(),
+            model=model,
+            tokens_per_second=tokens_per_second,
+            estimated_watts=watts,
+            efficiency=efficiency,
+            efficiency_score=score,
+        )
+        self._samples.append(sample)
+
+        # Auto-engage low power mode if above threshold and budget is enabled
+        threshold = getattr(settings, "energy_budget_watts_threshold", 15.0)
+        if watts > threshold and not self._low_power_mode:
+            logger.info(
+                "Energy budget: %.1fW exceeds threshold %.1fW — auto-engaging low power mode",
+                watts,
+                threshold,
+            )
+            self.set_low_power_mode(True)
+
+        return sample
+
+    async def get_report(self) -> EnergyReport:
+        """Return the current energy budget report.
+
+        Refreshes the power estimate if the cache is stale.
+        """
+        await self._refresh_power_cache()
+
+        score = self._compute_mean_efficiency_score()
+        recommendation = self._build_recommendation(score)
+
+        return EnergyReport(
+            timestamp=datetime.now(UTC).isoformat(),
+            low_power_mode=self._low_power_mode,
+            current_watts=self._cached_watts,
+            strategy=self._cached_strategy,
+            efficiency_score=score,
+            recent_samples=list(self._samples)[-10:],
+            recommendation=recommendation,
+            details={"sample_count": len(self._samples)},
+        )
+
+    # ── Power estimation ──────────────────────────────────────────────────────
+
+    async def _refresh_power_cache(self) -> None:
+        """Refresh the cached power reading if stale."""
+        now = time.monotonic()
+        if now - self._cache_ts < self._POWER_CACHE_TTL:
+            return
+
+        try:
+            watts, strategy = await asyncio.to_thread(self._read_power)
+        except Exception as exc:
+            logger.debug("Energy: power read failed: %s", exc)
+            watts, strategy = 0.0, "unavailable"
+
+        self._cached_watts = watts
+        self._cached_strategy = strategy
+        self._cache_ts = now
+
+    def _read_power(self) -> tuple[float, str]:
+        """Synchronous power reading — tries strategies in priority order.
+
+        Returns:
+            Tuple of (watts, strategy_name).
+        """
+        # Strategy 1: battery discharge via ioreg (on-battery Macs)
+        try:
+            watts = self._read_battery_watts()
+            if watts > 0:
+                return watts, "battery"
+        except Exception:
+            pass
+
+        # Strategy 2: CPU utilisation proxy via top
+        try:
+            cpu_pct = self._read_cpu_pct()
+            if cpu_pct >= 0:
+                # M3 Max TDP ≈ 40W; scale linearly
+                watts = (cpu_pct / 100.0) * 40.0
+                return watts, "cpu_proxy"
+        except Exception:
+            pass
+
+        # Strategy 3: heuristic from loaded model size
+        return 0.0, "unavailable"
+
+    def _estimate_watts_sync(self, model: str) -> float:
+        """Estimate watts from model size when no live reading is available."""
+        size_gb = self._model_size_gb(model)
+        return size_gb * _WATTS_PER_GB_HEURISTIC
+
+    def _read_battery_watts(self) -> float:
+        """Read instantaneous battery discharge via ioreg.
+
+        Returns watts if on battery, 0.0 if plugged in or unavailable.
+        Requires macOS; no sudo needed.
+        """
+        result = subprocess.run(
+            ["ioreg", "-r", "-c", "AppleSmartBattery", "-d", "1"],
+            capture_output=True,
+            text=True,
+            timeout=3,
+        )
+        amperage_ma = 0.0
+        voltage_mv = 0.0
+        is_charging = True  # assume charging unless we see ExternalConnected = No
+
+        for line in result.stdout.splitlines():
+            stripped = line.strip()
+            if '"InstantAmperage"' in stripped:
+                try:
+                    amperage_ma = float(stripped.split("=")[-1].strip())
+                except ValueError:
+                    pass
+            elif '"Voltage"' in stripped:
+                try:
+                    voltage_mv = float(stripped.split("=")[-1].strip())
+                except ValueError:
+                    pass
+            elif '"ExternalConnected"' in stripped:
+                is_charging = "Yes" in stripped
+
+        if is_charging or voltage_mv == 0 or amperage_ma <= 0:
+            return 0.0
+
+        # ioreg reports amperage in mA, voltage in mV
+        return (abs(amperage_ma) * voltage_mv) / 1_000_000
+
+    def _read_cpu_pct(self) -> float:
+        """Read CPU utilisation from macOS top.
+
+        Returns aggregate CPU% (0–100), or -1.0 on failure.
+        """
+        result = subprocess.run(
+            ["top", "-l", "1", "-n", "0", "-stats", "cpu"],
+            capture_output=True,
+            text=True,
+            timeout=5,
+        )
+        for line in result.stdout.splitlines():
+            if "CPU usage:" in line:
+                # "CPU usage: 12.5% user, 8.3% sys, 79.1% idle"
+                parts = line.split()
+                try:
+                    user = float(parts[2].rstrip("%"))
+                    sys_ = float(parts[4].rstrip("%"))
+                    return user + sys_
+                except (IndexError, ValueError):
+                    pass
+        return -1.0
+
+    # ── Helpers ───────────────────────────────────────────────────────────────
+
+    @staticmethod
+    def _model_size_gb(model: str) -> float:
+        """Look up approximate model size in GB by name substring."""
+        lower = model.lower()
+        # Exact match first
+        if lower in _MODEL_SIZE_GB:
+            return _MODEL_SIZE_GB[lower]
+        # Substring match
+        for key, size in _MODEL_SIZE_GB.items():
+            if key in lower:
+                return size
+        return _DEFAULT_MODEL_SIZE_GB
+
+    def _compute_mean_efficiency_score(self) -> float:
+        """Mean efficiency score over recent samples, or -1 if none."""
+        if not self._samples:
+            return -1.0
+        recent = list(self._samples)[-10:]
+        return sum(s.efficiency_score for s in recent) / len(recent)
+
+    def _build_recommendation(self, score: float) -> str:
+        """Generate a human-readable recommendation from the efficiency score."""
+        threshold = getattr(settings, "energy_budget_watts_threshold", 15.0)
+        low_power_model = getattr(settings, "energy_low_power_model", "qwen3:1b")
+
+        if score < 0:
+            return "No inference data yet — run some tasks to populate efficiency metrics."
+
+        if self._low_power_mode:
+            return (
+                f"Low power mode active — routing to {low_power_model}. "
+                "Disable when power draw normalises."
+            )
+
+        if score < 3.0:
+            return (
+                f"Low efficiency (score {score:.1f}/10). "
+                f"Consider enabling low power mode to favour smaller models "
+                f"(threshold: {threshold}W)."
+            )
+
+        if score < 6.0:
+            return f"Moderate efficiency (score {score:.1f}/10). System operating normally."
+
+        return f"Good efficiency (score {score:.1f}/10). No action needed."
+
+
+# Module-level singleton
+energy_monitor = EnergyBudgetMonitor()
diff --git a/tests/unit/test_energy_monitor.py b/tests/unit/test_energy_monitor.py
new file mode 100644
index 00000000..20858c4b
--- /dev/null
+++ b/tests/unit/test_energy_monitor.py
@@ -0,0 +1,297 @@
+"""Unit tests for the Energy Budget Monitor.
+
+Tests power estimation strategies, inference recording, efficiency scoring,
+and low power mode logic — all without real subprocesses.
+
+Refs: #1009
+"""
+
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from infrastructure.energy.monitor import (
+    EnergyBudgetMonitor,
+    InferenceSample,
+    _DEFAULT_MODEL_SIZE_GB,
+    _EFFICIENCY_SCORE_CEILING,
+    _WATTS_PER_GB_HEURISTIC,
+)
+
+
+@pytest.fixture()
+def monitor():
+    return EnergyBudgetMonitor()
+
+
+# ── Model size lookup ─────────────────────────────────────────────────────────
+
+
+def test_model_size_exact_match(monitor):
+    assert monitor._model_size_gb("qwen3:8b") == 5.5
+
+
+def test_model_size_substring_match(monitor):
+    assert monitor._model_size_gb("some-qwen3:14b-custom") == 9.0
+
+
+def test_model_size_unknown_returns_default(monitor):
+    assert monitor._model_size_gb("unknownmodel:99b") == _DEFAULT_MODEL_SIZE_GB
+
+
+# ── Battery power reading ─────────────────────────────────────────────────────
+
+
+def test_read_battery_watts_on_battery(monitor):
+    ioreg_output = (
+        "{\n"
+        '  "InstantAmperage" = 2500\n'
+        '  "Voltage" = 12000\n'
+        '  "ExternalConnected" = No\n'
+        "}"
+    )
+    mock_result = MagicMock()
+    mock_result.stdout = ioreg_output
+
+    with patch("subprocess.run", return_value=mock_result):
+        watts = monitor._read_battery_watts()
+
+    # 2500 mA * 12000 mV / 1_000_000 = 30 W
+    assert watts == pytest.approx(30.0, abs=0.01)
+
+
+def test_read_battery_watts_plugged_in_returns_zero(monitor):
+    ioreg_output = (
+        "{\n"
+        '  "InstantAmperage" = 1000\n'
+        '  "Voltage" = 12000\n'
+        '  "ExternalConnected" = Yes\n'
+        "}"
+    )
+    mock_result = MagicMock()
+    mock_result.stdout = ioreg_output
+
+    with patch("subprocess.run", return_value=mock_result):
+        watts = monitor._read_battery_watts()
+
+    assert watts == 0.0
+
+
+def test_read_battery_watts_subprocess_failure_raises(monitor):
+    with patch("subprocess.run", side_effect=OSError("no ioreg")):
+        with pytest.raises(OSError):
+            monitor._read_battery_watts()
+
+
+# ── CPU proxy reading ─────────────────────────────────────────────────────────
+
+
+def test_read_cpu_pct_parses_top(monitor):
+    top_output = (
+        "Processes: 450 total\n"
+        "CPU usage: 15.2% user, 8.8% sys, 76.0% idle\n"
+    )
+    mock_result = MagicMock()
+    mock_result.stdout = top_output
+
+    with patch("subprocess.run", return_value=mock_result):
+        pct = monitor._read_cpu_pct()
+
+    assert pct == pytest.approx(24.0, abs=0.1)
+
+
+def test_read_cpu_pct_no_match_returns_negative(monitor):
+    mock_result = MagicMock()
+    mock_result.stdout = "No CPU line here\n"
+
+    with patch("subprocess.run", return_value=mock_result):
+        pct = monitor._read_cpu_pct()
+
+    assert pct == -1.0
+
+
+# ── Power strategy selection ──────────────────────────────────────────────────
+
+
+def test_read_power_uses_battery_first(monitor):
+    with patch.object(monitor, "_read_battery_watts", return_value=25.0):
+        watts, strategy = monitor._read_power()
+
+    assert watts == 25.0
+    assert strategy == "battery"
+
+
+def test_read_power_falls_back_to_cpu_proxy(monitor):
+    with (
+        patch.object(monitor, "_read_battery_watts", return_value=0.0),
+        patch.object(monitor, "_read_cpu_pct", return_value=50.0),
+    ):
+        watts, strategy = monitor._read_power()
+
+    assert strategy == "cpu_proxy"
+    assert watts == pytest.approx(20.0, abs=0.1)  # 50% of 40W TDP
+
+
+def test_read_power_unavailable_when_both_fail(monitor):
+    with (
+        patch.object(monitor, "_read_battery_watts", side_effect=OSError),
+        patch.object(monitor, "_read_cpu_pct", return_value=-1.0),
+    ):
+        watts, strategy = monitor._read_power()
+
+    assert strategy == "unavailable"
+    assert watts == 0.0
+
+
+# ── Inference recording ───────────────────────────────────────────────────────
+
+
+def test_record_inference_produces_sample(monitor):
+    monitor._cached_watts = 10.0
+    monitor._cache_ts = 9999999999.0  # far future — cache won't expire
+
+    sample = monitor.record_inference("qwen3:8b", tokens_per_second=40.0)
+
+    assert isinstance(sample, InferenceSample)
+    assert sample.model == "qwen3:8b"
+    assert sample.tokens_per_second == 40.0
+    assert sample.estimated_watts == pytest.approx(10.0)
+    # efficiency = 40 / 10 = 4.0 tok/s per W
+    assert sample.efficiency == pytest.approx(4.0)
+    # score = min(10, (4.0 / 5.0) * 10) = 8.0
+    assert sample.efficiency_score == pytest.approx(8.0)
+
+
+def test_record_inference_stores_in_history(monitor):
+    monitor._cached_watts = 5.0
+    monitor._cache_ts = 9999999999.0
+
+    monitor.record_inference("qwen3:8b", 30.0)
+    monitor.record_inference("qwen3:14b", 20.0)
+
+    assert len(monitor._samples) == 2
+
+
+def test_record_inference_auto_activates_low_power(monitor):
+    monitor._cached_watts = 20.0  # above default 15W threshold
+    monitor._cache_ts = 9999999999.0
+
+    assert not monitor.low_power_mode
+    monitor.record_inference("qwen3:30b", 8.0)
+    assert monitor.low_power_mode
+
+
+def test_record_inference_no_auto_low_power_below_threshold(monitor):
+    monitor._cached_watts = 10.0  # below default 15W threshold
+    monitor._cache_ts = 9999999999.0
+
+    monitor.record_inference("qwen3:8b", 40.0)
+    assert not monitor.low_power_mode
+
+
+# ── Efficiency score ──────────────────────────────────────────────────────────
+
+
+def test_efficiency_score_caps_at_10(monitor):
+    monitor._cached_watts = 1.0
+    monitor._cache_ts = 9999999999.0
+
+    sample = monitor.record_inference("qwen3:1b", tokens_per_second=1000.0)
+    assert sample.efficiency_score == pytest.approx(10.0)
+
+
+def test_efficiency_score_no_samples_returns_negative_one(monitor):
+    assert monitor._compute_mean_efficiency_score() == -1.0
+
+
+def test_mean_efficiency_score_averages_last_10(monitor):
+    monitor._cached_watts = 10.0
+    monitor._cache_ts = 9999999999.0
+
+    for _ in range(15):
+        monitor.record_inference("qwen3:8b", tokens_per_second=25.0)  # efficiency=2.5 → score=5.0
+
+    score = monitor._compute_mean_efficiency_score()
+    assert score == pytest.approx(5.0, abs=0.01)
+
+
+# ── Low power mode ────────────────────────────────────────────────────────────
+
+
+def test_set_low_power_mode_toggle(monitor):
+    assert not monitor.low_power_mode
+    monitor.set_low_power_mode(True)
+    assert monitor.low_power_mode
+    monitor.set_low_power_mode(False)
+    assert not monitor.low_power_mode
+
+
+# ── get_report ────────────────────────────────────────────────────────────────
+
+
+@pytest.mark.asyncio
+async def test_get_report_structure(monitor):
+    with patch.object(monitor, "_read_power", return_value=(8.0, "battery")):
+        report = await monitor.get_report()
+
+    assert report.timestamp
+    assert isinstance(report.low_power_mode, bool)
+    assert isinstance(report.current_watts, float)
+    assert report.strategy in ("battery", "cpu_proxy", "heuristic", "unavailable")
+    assert isinstance(report.recommendation, str)
+
+
+@pytest.mark.asyncio
+async def test_get_report_to_dict(monitor):
+    with patch.object(monitor, "_read_power", return_value=(5.0, "cpu_proxy")):
+        report = await monitor.get_report()
+
+    data = report.to_dict()
+    assert "timestamp" in data
+    assert "low_power_mode" in data
+    assert "current_watts" in data
+    assert "strategy" in data
+    assert "efficiency_score" in data
+    assert "recent_samples" in data
+    assert "recommendation" in data
+
+
+@pytest.mark.asyncio
+async def test_get_report_caches_power_reading(monitor):
+    call_count = 0
+
+    def counting_read_power():
+        nonlocal call_count
+        call_count += 1
+        return (10.0, "battery")
+
+    with patch.object(monitor, "_read_power", side_effect=counting_read_power):
+        await monitor.get_report()
+        await monitor.get_report()
+
+    # Cache TTL is 10s — should only call once
+    assert call_count == 1
+
+
+# ── Recommendation text ───────────────────────────────────────────────────────
+
+
+def test_recommendation_no_data(monitor):
+    rec = monitor._build_recommendation(-1.0)
+    assert "No inference data" in rec
+
+
+def test_recommendation_low_power_mode(monitor):
+    monitor.set_low_power_mode(True)
+    rec = monitor._build_recommendation(2.0)
+    assert "Low power mode active" in rec
+
+
+def test_recommendation_low_efficiency(monitor):
+    rec = monitor._build_recommendation(1.5)
+    assert "Low efficiency" in rec
+
+
+def test_recommendation_good_efficiency(monitor):
+    rec = monitor._build_recommendation(8.0)
+    assert "Good efficiency" in rec