LEADERBOARD.md

GridOps — Agent Leaderboard

Benchmark results across 10 agents on the GridOps OpenEnv environment. All runs use seed=42 for full reproducibility. Each task is 72 steps (3 days).

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Overall Standings

Rank	Agent	Task 1 (Normal)	Task 2 (Heatwave)	Task 3 (Crisis)	Average
1	Grok-4 (xAI)	0.80	0.82	0.72	0.78
2	Oracle (rule-based)	0.79	0.81	0.70	0.77
3	GPT-5.4 (OpenAI)	0.79	0.79	0.67	0.75
4	Gemma-4-31B (Google)	0.81	0.79	0.62	0.74
4	Grok 4.20 Multi-Agent	0.81	0.80	0.60	0.74
4	DeepSeek V3.2	0.80	0.79	0.62	0.74
7	GPT-5.4-mini (OpenAI)	0.72	0.74	0.46	0.64
8	Qwen 3.6 Plus (free)	0.69	0.67	0.45	0.60
9	Gemini 3.1 Pro Preview	0.65	0.53	0.47	0.55
10	Kimi K2.5	0.57	0.54	0.48	0.53
—	Do-Nothing baseline	0.58	0.51	0.45	0.51
—	Always-Discharge	0.59	0.51	0.45	0.52
—	Always-Diesel	0.42	0.42	0.44	0.43

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Capability Tiers

Tier	Score Range	Agents
Frontier	0.74 - 0.78	Grok-4, GPT-5.4, Gemma-4-31B, Grok 4.20, DeepSeek V3.2
Hand-coded baseline	0.77	Oracle (rule-based)
Mid-tier	0.60 - 0.64	GPT-5.4-mini, Qwen 3.6 Plus
Weak	0.51 - 0.55	Kimi K2.5, Gemini 3.1 Pro Preview
No-intelligence baselines	0.43 - 0.52	Do-Nothing, Always-Discharge, Always-Diesel

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Per-Task Breakdown

Task 1: Normal Summer (Easy)

Tests basic battery arbitrage. ~100 kW avg demand, Rs 3-12 prices, no heatwave.

Rank	Agent	Score
1	Gemma-4-31B	0.81
1	Grok 4.20 Multi-Agent	0.81
3	Grok-4	0.80
3	DeepSeek V3.2	0.80
5	Oracle	0.79
5	GPT-5.4	0.79
7	GPT-5.4-mini	0.72
8	Qwen 3.6 Plus	0.69
9	Gemini 3.1 Pro Preview	0.65
10	Always-Discharge	0.59
11	Do-Nothing	0.58
12	Kimi K2.5	0.57
13	Always-Diesel	0.42

Task 2: Heatwave + Price Spike (Medium)

Tests temporal planning. Day 2-3 heatwave (+30% demand), Rs 20 evening price spike visible in 4h forecast.

Rank	Agent	Score
1	Grok-4	0.82
2	Oracle	0.81
3	Grok 4.20 Multi-Agent	0.80
4	Gemma-4-31B	0.79
4	DeepSeek V3.2	0.79
4	GPT-5.4	0.79
7	GPT-5.4-mini	0.74
8	Qwen 3.6 Plus	0.67
9	Kimi K2.5	0.54
10	Gemini 3.1 Pro Preview	0.53
11	Do-Nothing	0.51
11	Always-Discharge	0.51
13	Always-Diesel	0.42

Task 3: Extreme Crisis + Grid Outage (Hard)

Tests constraint management. Full 3-day heatwave, -30% solar, +50% demand, limited diesel, 6-hour grid outage on Day 2.

Rank	Agent	Score
1	Grok-4	0.72
2	Oracle	0.70
3	GPT-5.4	0.67
4	Gemma-4-31B	0.62
4	DeepSeek V3.2	0.62
6	Grok 4.20 Multi-Agent	0.60
7	Kimi K2.5	0.48
8	Gemini 3.1 Pro Preview	0.47
9	GPT-5.4-mini	0.46
10	Qwen 3.6 Plus	0.45
10	Do-Nothing	0.45
10	Always-Discharge	0.45
13	Always-Diesel	0.44

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Key Observations

1. The environment cleanly differentiates capability. A clean gradient from do-nothing (0.51 avg) through frontier LLMs (0.78). Every model lands in a different tier.

2. Task 3 is the real differentiator. The 6-hour grid outage forces true islanding behavior. Only Grok-4 and the Oracle handle it well (>0.70). Most LLMs collapse to ~0.45 — the same as do-nothing.

3. Frontier LLMs match or beat the hand-coded oracle. Grok-4 (0.78) > Oracle (0.77) — the environment is solvable by raw LLM reasoning, but requires real intelligence.

4. Smaller LLMs barely beat do-nothing. Kimi K2.5 (0.53) and Gemini 3.1 Pro Preview (0.55) are within rounding error of the do-nothing baseline (0.51) — they struggle to produce useful actions consistently.

5. Capability scales with model size within a family. GPT-5.4 (0.75) significantly outperforms GPT-5.4-mini (0.64). Same prompt, same environment — only the model size differs.

6. The 0.20-0.35 gap between best and worst agents proves the environment has real optimization headroom and isn't trivially solvable.

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Reproducibility

All scores are deterministic. To reproduce:

export API_BASE_URL="https://openrouter.ai/api/v1"
export HF_TOKEN="<your-key>"
export MODEL_NAME="<model-id>"
python inference.py

Output is structured [START] / [STEP] / [END] blocks with explicit task names and scores. Same seed (42) + same model = identical scores across runs.

To run hand-coded baselines (no API key needed):

python scripts/oracle_test.py