Mercury
Python code-synthesis benchmark that rewards both functional correctness and runtime efficiency against real solution distributions.
Mercury is a static 256-task public LeetCode-derived evaluation with no official maintained leaderboard or refreshed test set. Beyond remains useful for efficiency research, but the 2024 launch snapshot is no longer a frontier comparison.
Performance Timeline
Longitudinal progression of model scores against human baselines.Performance & Historical Trajectory
Empirical score progression across model release dates and evaluation rounds.
| Model | Score | Date | Source Type | Provenance |
|---|---|---|---|---|
| CodeQwen1.5-7B base (Mercury-eval Overall Beyond, 5 samples) | 47.78% | 2024-04-18 | independent | Source ↗ |
| StarCoder2-15B base (Mercury-eval Overall Beyond, 5 samples) | 49.17% | 2024-02-28 | independent | Source ↗ |
| DeepSeek-Coder-33B base (Mercury-eval Overall Beyond, 5 samples) | 48.53% | 2023-11-02 | independent | Source ↗ |
| CodeLlama-34B base (Mercury-eval Overall Beyond, 5 samples) | 42.4% | 2023-08-24 | independent | Source ↗ |
Human Baseline & Difficulty Horizon
Calibrated human reference points, specialist benchmarks, and ceiling thresholds.Human distribution baseline across Beyond@1 computational efficiency and correctness benchmarks.
Metric & Scoring Methodology
Verification protocols, aggregation formulas, and specialized metric variants.Beyond with 5 sampled solutions per task (%)Dataset & Compute Cost
Evaluation volume, public availability, API pricing, and local hardware requirements.$5 – $20 USD for full benchmark evaluation run on frontier APIs.
1x NVIDIA RTX 4090 (24GB) or A100 (40GB/80GB) via vLLM / SGLang
How to Run & Reproduce
Standardized evaluation protocols, CLI commands, and reproducible runner templates.lm_eval --model hf --model_args pretrained=<model_path> --tasks mercury --batch_size autoopencompass --datasets mercury --models <model_config># Standard API Evaluation Loop
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": prompt}],
temperature=0.0,
)When publishing results for Mercury, always report the exact prompt template, few-shot exemplar ordering, sampling temperature (temperature=0), maximum reasoning budget tokens, and the precise timestamped model snapshot ID.
Contamination & Memorization Analysis
Audit of pretraining exposure risks, memorization vectors, and refresh policies.Static fixed snapshot
Public on web / HuggingFace