BENCHMARK AUDIT: TRACK 04 • CANONICAL BLIND HUMANEVAL (164 PROBLEMS) • AUTHOR: GANESH NALLASIVAM

Canonical Blind HumanEval Benchmark

Full 164-problem blind execution benchmark evaluating Qwen2.5-Coder-14B-Instruct (4-bit) running on Apple Silicon via AtacamaODR (Port 8088). Every test case was executed in an isolated sandboxed subprocess with strict 5.0-second timeouts against official OpenAI canonical assertions. Zero synthetic mock data. Zero prompt overfitting regexes. 100% physically executed code.

Pass@1 Accuracy

86.0%

141 of 164 passed.

Total Problems Evaluated

164 / 164

100% completed suite.

Average Turnaround

13.8 s

Total runtime: 37.7 min.

Execution Failures

19 assertion, 4 syntax errors.

Industry Standard Python Coding Accuracy (HumanEval Pass@1)

Grounded Empirical Comparison

Model Architecture	Parameters / Quant	Host Environment	Pass@1 Accuracy	Status
Qwen2.5-Coder + AtacamaODR	14B (4-bit MLX)	Apple Silicon (24GB UMA)	86.0% (141/164)	Physically Verified
Claude 3.5 Sonnet (Frontier Reference)	Unknown (Cloud)	Anthropic Cloud API	93.7%	Published Lab Metric
GPT-4o (Frontier Reference)	Unknown (Cloud)	OpenAI Cloud API	90.2%	Published Lab Metric
Llama 3.1 70B Instruct	70B (16-bit / 8-bit)	Enterprise Cloud Host	80.5%	Published Lab Metric
Qwen2.5-Coder-7B-Instruct	7B (4-bit)	Local Apple Silicon	79.9%	Published Model Card

Audit Methodology & Error Breakdown

Empirical Execution Telemetry

All 164 problems were piped via HTTP to http://127.0.0.1:8088/v1/chat/completions, parsed for Python function blocks, concatenated with canonical test cases, and executed in temporary isolated files via python3 subprocesses with 5.0-second timeouts.

Passed Unit Tests:

141 Problems

Exit Code 0 (Pass@1)

Logical / Assertion Errors:

19 Problems

Canonical assertion mismatch

Syntax / Parsing Errors:

4 Problems

Unterminated strings / syntax

Dataset: OpenAI Canonical HumanEval.jsonl (164 tasks) Artifact: humaneval_execution_results.json