benchmark_comparison.md

🏆 Code Review OpenEnv - Complete Master Benchmark Trajectory

📉 Final Performance Summary & Evaluation

Throughout the ascending environments, score clamping was mathematically refined from raw score inflation to strict OpenEnv F1 constraints, explicitly limited to 0.999.

🥇 MASTER HISTORICAL BENCHMARK RESULTS

Exact Model ID (No Manual Labels)	Phase	Easy F1	Medium F1	Hard F1	Avg F1	Avg Conf.
deepseek-ai/DeepSeek-V3	🕒 Old Baseline	0.999	0.667	0.800	0.822	96%
qwen/qwen-2.5-72b-instruct	🕒 Old Baseline	0.727	0.824	0.500	0.684	95%
meta-llama/llama-3.3-70b-instruct	🕒 Old Baseline	0.556	0.625	0.375	0.519	94%
deepseek-ai/DeepSeek-V3	🕒 Old Concurrency	0.999	0.667	0.621	0.762	90%
meta-llama/llama-3.1-70b-instruct	🕒 Old Concurrency	0.833	0.636	0.545	0.671	96%
qwen/qwen-2.5-72b-instruct	🕒 Old Concurrency	0.667	0.625	0.500	0.597	99%
openai/gpt-4o-mini	🕒 Old Concurrency	0.667	0.588	0.308	0.521	90%
meta-llama/llama-3.3-70b-instruct	🕒 Live OpenRouter	0.999	0.625	0.545	0.723	95%
deepseek-ai/DeepSeek-V3	🕒 Live OpenRouter	0.600	0.667	0.500	0.589	94%
openai/gpt-4o-mini	🕒 Live OpenRouter	0.600	0.667	0.324	0.530	90%
qwen/qwen-2.5-72b-instruct	🕒 Live OpenRouter	0.500	0.588	0.500	0.529	98%
mistralai/mistral-small-3.1-24b	🕒 Live OpenRouter	0.100	0.333	0.999	0.477	100%

🏆 HUGGING FACE NATIVE SERVERLESS (Final Production Phase)

Native inference parsing successfully verified directly over https://router.huggingface.co/v1.

DeepSeek-AI completely dominated the native test group, surgically identifying every web vulnerability in the medium test environment to achieve a mathematically perfect 0.999 limit ceiling.

Native Model Identifier	Environment	Easy F1	Medium F1	Hard F1	Avg F1	Avg Conf.
deepseek-ai/DeepSeek-V3	✨ HuggingFace	0.667	0.999	0.564	0.743	97%
Qwen/Qwen2.5-72B-Instruct	✨ HuggingFace	0.200	0.588	0.286	0.358	95%
meta-llama/Meta-Llama-3-8B-Instruct	✨ HuggingFace	0.429	0.001	0.001	0.144	96%
meta-llama/Llama-3.3-70B-Instruct	❌ Rate Limited	-	-	-	-	-
mistralai/Mixtral-8x7B-Instruct-v0.1	❌ Model Unsupported	-	-	-	-	-

🌐 POST-SUBMISSION OPENROUTER BENCHMARKS

Final stress test verification leveraging OpenRouter failover.

Native Model Identifier	Environment	Easy F1	Medium F1	Hard F1	Avg F1	Avg Conf.
deepseek-ai/DeepSeek-V3	🚀 OpenRouter	0.750	0.667	0.720	0.712	92%
openai/gpt-4o-mini	🚀 OpenRouter	0.833	0.667	0.581	0.694	90%
meta-llama/llama-3.3-70b-instruct	🚀 OpenRouter	0.500	0.833	0.545	0.626	94%
qwen/qwen-2.5-72b-instruct	🚀 OpenRouter	0.800	0.556	0.500	0.619	97%
mistralai/mistral-small-3.1-24b	🚀 OpenRouter	0.001	0.001	0.999	0.334	100%

🧠 Performance Analysis: Why Models Succeed or Fail

Our deterministic grading environment reveals deep behaviors not captured by standard multiple-choice benchmarks:

• 🥇 DeepSeek-V3 (The Winner): Dominated because of superior confidence calibration and semantic reasoning. Unlike other models, DeepSeek doesn't just guess. When faced with the adversarial "Red Herring" (try...except: pass inside a backoff loop), its confidence drops, allowing it to bypass the trap entirely. It correctly uses multi-step logic to deduce why code is conceptually flawed rather than just syntactically incorrect.
• 🥈 Qwen-2.5-72B: Highly capable at identifying localized syntax and logic errors in the Easy and Medium environments. However, it suffered in the Hard task, demonstrating limitations in long-context, cross-file reasoning. It often failed to correctly track how keys generated in config_loader.py were insecurely consumed in crypto_service.py.
• 🥉 Llama-3.3-70B (The Overconfident Guesser): Suffered mathematically due to overconfidence syndrome. The environment heavily penalizes false positives submitted with >80% confidence. Llama consistently flagged totally secure, verified code blocks as "Critical Vulnerabilities" with 95% confidence, causing its F1 score to crash dynamically. It could not differentiate real bugs from the adversarial comment injections.
• 📉 Smaller/Local Models (Mixtral, Meta-Llama-8B, Gemma): Generally failed either due to JSON parsing collapse (outputting conversational text or reasoning tags instead of strict operation schemas) or by reaching maximum timeout limits when scanning larger codeblocks.