DeepSeek V3 exhibited a contradictory performance in this week's evaluation: significant improvements in multiple capability metrics, with the overall score rising from 52.9 to 66.6, but a cliff-like drop in the stability dimension. This phenomenon of "enhanced capabilities but unstable output" deserves in-depth analysis.
GPT-o3's availability score plummeted from 100 to 69 in just one week, exposing fundamental architectural defects rather than isolated issues—a technical accident that reveals systemic imbalances in AI development.
GPT-o3's long context processing capability collapsed in recent testing, with scores dropping from 62.3 to 28.8 points due to aggressive API rate limiting, exposing serious infrastructure issues at OpenAI.
GPT-4o's catastrophic failure in long-context tests, with 5 questions returning rate limit errors, reveals OpenAI's severe infrastructure problems rather than model capability issues.
Gemini 2.5 Pro's stability score plummeted 22.8 points in one week, exposing a critical lack of engineering judgment despite gains in programming capabilities.
This week's evaluation data reveals Gemini 2.5 Pro's stability score plummeted from 54.0 to 31.2, a 42.2% drop, exposing serious issues in maintaining consistent output quality while other metrics improved.
Wenxin Yiyan 4.0 showed remarkable anomalies in this week's evaluation, with programming capability surging 41.4 points but stability plummeting from 52.1 to 30.0 points, revealing potential deep-seated issues in the model upgrade process.
Claude Opus 4.6's complete failure on security response questions reveals a deeper issue: when AI encounters real emergencies, their "perfect answers" might be the most dangerous.
Weekly testing of 11 top AI models reveals concerning trends: stability has become a luxury, long-context capabilities are collectively declining, and Chinese models are reshaping the competitive landscape.
