Generated on: 2025-04-22 16:13:31
Threshold for significant anomalies: 3
LLM model used for analysing: QwQ-32B
Total models analyzed: 0
The model performs moderately, ranking 12th out of 17 overall. While it shows significant strengths in creative and niche domains, it underperforms in technical, analytical, and specialized tasks. The performance is uneven, with clear opportunities for improvement in weaker areas.
The model ranks 15th out of 17, indicating below-average overall performance. Despite this, it exhibits significant strength in niche domains, while lacking broad competence across most tested areas. The absence of notable weaknesses suggests its shortcomings stem from inconsistency rather than critical flaws.
The model demonstrates exceptional performance in the following domains (difference exceeds threshold of 3):
While no explicit weaknesses are flagged, the model’s low overall ranking implies underperformance in unlisted domains. Likely weaknesses include:
The model underperforms overall, ranking 16th out of 17 models. Despite this, it demonstrates significant strengths in specific creative and niche writing/roleplay tasks. The lack of critical weaknesses (no nodes with worse performance) suggests its limitations stem from breadth of competence rather than outright failures in specific areas.
While no catastrophic weaknesses exist, the model’s limited versatility is problematic:
The model ranks 17th out of 17, indicating poor overall performance. However, it exhibits significant strengths in specific domains, suggesting specialized capabilities despite its general weakness.
The model excels in the following domains (differences exceed the threshold of 3):
While no specific weaknesses were flagged (0 worse-performing nodes), the model’s overall rank of 17 implies systemic underperformance across most tasks, particularly in domains not explicitly listed here. This suggests a lack of generalization and broad competency.
QwQ-32B ranks 3rd out of 17 models, indicating strong overall performance. However, it exhibits significant weaknesses in three specific subdomains, with performance drops exceeding the predefined threshold of 3. These anomalies suggest niche domain-specific limitations despite its general capability.
No significant strengths were identified beyond its baseline performance. The model does not outperform competitors in any subdomain, though its average ranking reflects robust generalization across most tasks.
The model performs average overall (ranked 11th out of 17), with notable strengths in logical/mathematical domains and weaknesses in front-end development and creative writing tasks. While its capabilities in abstract reasoning and applied mathematics stand out, it struggles with domain-specific technical and creative skills requiring nuanced expertise.
The model Qwen2.5-72B-Instruct performs average overall, ranking 7th out of 17 models. While it does not exhibit significant strengths in any domain, it shows 41 areas of notable weakness, particularly in technical, mathematical, and creative task categories. These weaknesses suggest gaps in specialized knowledge and nuanced task handling.
No significant strengths were identified. The model does not outperform peers in any evaluated node by more than the 3-rank threshold.
The model ranks 13th out of 17, indicating below-average overall performance. However, it exhibits significant strengths in niche domains and critical weaknesses in creative writing. While its performance is inconsistent across tasks, strategic improvements could elevate its position.
The model performs exceptionally well in technical domains, particularly coding and programming languages, while struggling significantly in creative, emotional, and interpersonal tasks. Its overall ranking of 5/17 suggests a balanced yet uneven proficiency, with notable strengths and weaknesses that require targeted improvement.
DeepSeek-R1-250120 demonstrates strong overall performance, ranking 2nd out of 17 models. However, its performance is uneven, with 26 specialized nodes showing significant weaknesses (difference ≥4). While the model excels in general tasks, it struggles in niche or highly specialized domains, indicating potential gaps in training data or architectural limitations in handling certain knowledge areas.
No areas of significant strength were identified. The model does not outperform others in any specific nodes beyond its overall rank. Its strong overall ranking likely stems from consistent performance across non-specialized tasks.
The model holds the #1 overall ranking among 17 models, indicating strong general performance. However, it exhibits significant weaknesses in 134 specific nodes, particularly in specialized reasoning methods, task types, and roleplay capabilities. While its core functionality is robust, targeted improvements are critical to address these gaps.
The model performs average overall, ranking 10th out of 17. It exhibits significant strengths in mathematical and foundational cognitive tasks but lags in creative, argumentative, and roleplay scenarios. This imbalance suggests a focus on structured, logical reasoning over open-ended or narrative-based tasks.
Note: 68 nodes show improved performance, with differences of -7 to -8 (well beyond the 3-point anomaly threshold).
Note: 39 nodes show degraded performance, with differences of +4 (exceeding the anomaly threshold).
Final Note: While the model’s mathematical strengths are notable, addressing its creative and argumentative gaps could significantly elevate its versatility and overall ranking.
The model gemma-3-27b-it holds an overall ranking of 4 out of 17, indicating solid baseline performance. However, it exhibits 19 significant weaknesses (difference ≥ 4), concentrated across technical domains, specialized knowledge areas, and structured writing tasks. While its rank suggests competitiveness, the large number of underperforming nodes highlights critical gaps that limit its versatility and depth.
No significantly better-performing nodes were identified. The model does not demonstrate exceptional strength in any tested category compared to peers.
Note: The model’s overall rank is respectable, but addressing these weaknesses could elevate its versatility and competitiveness in niche applications.
The model performs moderately well overall, ranking 9th out of 17. While it exhibits significant strengths in creative, emotional, and interactive roleplay scenarios, it struggles notably with coding and data-processing tasks. This suggests a specialization in narrative and reasoning tasks at the expense of technical or structured syntax-based domains.
Key strengths (difference ≤ -7/-6):
Hypothesis: The model may have been trained on extensive narrative or emotionally rich datasets, prioritizing human-like interaction over technical precision.
Major weaknesses (difference ≥ +4):
Hypothesis: Limited exposure to technical datasets or insufficient fine-tuning on code-centric benchmarks.
The model performs above average (ranked 6th out of 17) overall. It exhibits significant strengths in specialized domains but has notable weaknesses in two critical areas. While its versatility is evident across many tasks, targeted improvements in weak areas could elevate its overall ranking.
Strengths noted with a difference of -5 (5 ranks better than overall performance), indicating significant expertise.
Weaknesses exceed the significance threshold (Δ > 3), indicating critical gaps.
Strengths in specialized technical domains, but overall performance lags in broader comparisons.
Outperforms peers in coding tools, specific programming languages, mathematical analysis, and media writing.
No significant weaknesses identified, but overall rank suggests underperformance in non-listed domains.
The model demonstrates average overall performance, ranking 8th out of 17. While it lacks significant strengths, it exhibits notable weaknesses in specific niche areas, particularly in roleplay scenarios, specialized writing tasks, and symbolic reasoning. These weaknesses indicate opportunities for targeted improvements.
No areas of significant strength were identified. The model does not outperform others in any of the evaluated nodes beyond the threshold of statistical significance.