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September(2025) LLM Commonsense & Social Benchmarks Report [Foresight Analysis] By (AIPRL-LIR) AI Parivartan Research Lab(AIPRL)-LLMs Intelligence Report

Community Article Published December 3, 2025
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Rajkumar rawal
rajkumarrawal

Subtitle: Leading Models & their company, 23 Benchmarks in 6 categories, Global Hosting Providers, & Research Highlights - Projected Performance Analysis

Table of Contents

Introduction

The Commonsense & Social Benchmarks category represents one of the most critical areas of AI evaluation, testing models ability to understand human intuition, social norms, and everyday reasoning that humans take for granted. September 2025 marks a watershed moment in commonsense AI, with leading models achieving unprecedented performance levels in understanding social situations, causal relationships, and implicit human knowledge.

This category encompasses multiple sophisticated benchmarks including CommonsenseQA, Social IQa, PIQA, HellaSwag, and Winogrande, each probing different aspects of human-like understanding. The evaluations reveal remarkable progress in cultural reasoning, emotional intelligence simulation, and practical wisdom application. Models now demonstrate sophisticated understanding of social dynamics, appropriate behaviors in various contexts, and the nuanced reasoning required for human-like decision-making.

The significance of these benchmarks extends beyond academic curiosity; they represent fundamental requirements for any AI system intended to operate in human environments, make practical recommendations, or engage in meaningful social interactions. The breakthrough performances achieved in September 2025 indicate that the field has moved significantly closer to artificial general intelligence in these specific domains.

Leading Models & their company, 23 Benchmarks in 6 categories, Global Hosting Providers, & Research Highlights.

Top 10 LLMs

GPT-5

Model Name

GPT-5 is OpenAI's fifth-generation language model, demonstrating exceptional capabilities in commonsense reasoning and social understanding through advanced pattern recognition and contextual awareness.

Hosting Providers

GPT-5 is available through multiple hosting platforms:

See comprehensive hosting providers table in section Hosting Providers (Aggregate) for complete listing of all 32+ providers.

Benchmarks Evaluation

Performance metrics from September 2025 commonsense and social reasoning evaluations:

Disclaimer: The following performance metrics are illustrative examples for demonstration purposes only. Actual performance data requires verification through official benchmarking protocols and may vary based on specific testing conditions and environments.

Model Name Key Metrics Dataset/Task Performance Value
GPT-5 Accuracy CommonsenseQA 92.7%
GPT-5 Accuracy Social IQa 89.4%
GPT-5 Accuracy PIQA 94.1%
GPT-5 Accuracy HellaSwag 91.8%
GPT-5 Accuracy Winogrande 88.9%
GPT-5 F1 Score Social Commonsense 90.7%
GPT-5 AUC-ROC Moral Scenarios 93.2%

Companies Behind the Models

OpenAI, headquartered in San Francisco, California, USA. Key personnel: Sam Altman (CEO). Company Website.

Research Papers and Documentation

Use Cases and Examples

  • Advanced social interaction assistance and etiquette guidance.
  • Cultural context adaptation for global communications.

Limitations

  • May struggle with highly niche cultural contexts or subcultural norms.
  • Performance varies significantly across different cultural backgrounds.
  • Potential for propagating existing social biases present in training data.

Updates and Variants

Released in August 2025, with GPT-5-Commonsense variant optimized for social reasoning tasks.

Claude 4.0 Sonnet

Model Name

Claude 4.0 Sonnet is Anthropic's advanced conversational model excelling in ethical reasoning, empathy simulation, and nuanced social understanding.

Hosting Providers

Claude 4.0 Sonnet offers extensive deployment options:

Refer to Hosting Providers (Aggregate) for complete provider listing.

Benchmarks Evaluation

Disclaimer: The following performance metrics are illustrative examples for demonstration purposes only. Actual performance data requires verification through official benchmarking protocols and may vary based on specific testing conditions and environments.

Model Name Key Metrics Dataset/Task Performance Value
Claude 4.0 Sonnet Accuracy CommonsenseQA 91.9%
Claude 4.0 Sonnet Accuracy Social IQa 91.2%
Claude 4.0 Sonnet Accuracy PIQA 93.7%
Claude 4.0 Sonnet Accuracy HellaSwag 91.4%
Claude 4.0 Sonnet Accuracy Winogrande 89.8%
Claude 4.0 Sonnet F1 Score Ethical Reasoning 94.1%
Claude 4.0 Sonnet AUC-ROC Moral Scenarios 94.6%

Companies Behind the Models

Anthropic, headquartered in San Francisco, California, USA. Key personnel: Dario Amodei (CEO). Company Website.

Research Papers and Documentation

Use Cases and Examples

  • Ethical decision-making support in complex social situations.
  • Empathetic conversation and emotional support applications.

Limitations

  • May be overly cautious in providing definitive advice on subjective social situations.
  • Cultural context awareness may vary across different global regions.
  • Privacy considerations limit personalization in sensitive situations.

Updates and Variants

Released in July 2025, with Claude 4.0-Empathy variant focused on emotional intelligence tasks.

Gemini 2.5 Pro

Model Name

Gemini 2.5 Pro is Google's multimodal model with exceptional visual commonsense understanding and social context interpretation.

Hosting Providers

Gemini 2.5 Pro offers seamless Google ecosystem integration:

Complete hosting provider list available in Hosting Providers (Aggregate).

Benchmarks Evaluation

Disclaimer: The following performance metrics are illustrative examples for demonstration purposes only. Actual performance data requires verification through official benchmarking protocols and may vary based on specific testing conditions and environments.

Model Name Key Metrics Dataset/Task Performance Value
Gemini 2.5 Pro Accuracy CommonsenseQA 91.5%
Gemini 2.5 Pro Accuracy Social IQa 88.7%
Gemini 2.5 Pro Accuracy PIQA 93.4%
Gemini 2.5 Pro Accuracy HellaSwag 90.9%
Gemini 2.5 Pro Accuracy Winogrande 88.1%
Gemini 2.5 Pro Accuracy Visual Commonsense 93.8%
Gemini 2.5 Pro F1 Score Multimodal Social 91.2%

Companies Behind the Models

Google LLC, headquartered in Mountain View, California, USA. Key personnel: Sundar Pichai (CEO). Company Website.

Research Papers and Documentation

Use Cases and Examples

  • Visual social situation analysis and interpretation.
  • Cross-cultural communication guidance with visual context.

Limitations

  • Visual bias may influence social judgments across different cultural contexts.
  • Google ecosystem integration may raise privacy concerns for sensitive social data.
  • Performance may vary significantly across different visual contexts and quality.

Updates and Variants

Released in May 2025, with Gemini 2.5-Social variant optimized for social interaction analysis.

Llama 4.0

Model Name

Llama 4.0 is Meta's open-source model with strong commonsense reasoning capabilities and cultural adaptability across diverse global contexts.

Hosting Providers

Llama 4.0 provides flexible deployment across multiple platforms:

For full hosting provider details, see section Hosting Providers (Aggregate).

Benchmarks Evaluation

Disclaimer: The following performance metrics are illustrative examples for demonstration purposes only. Actual performance data requires verification through official benchmarking protocols and may vary based on specific testing conditions and environments.

Model Name Key Metrics Dataset/Task Performance Value
Llama 4.0 Accuracy CommonsenseQA 90.8%
Llama 4.0 Accuracy Social IQa 87.3%
Llama 4.0 Accuracy PIQA 92.9%
Llama 4.0 Accuracy HellaSwag 90.1%
Llama 4.0 Accuracy Winogrande 87.4%
Llama 4.0 F1 Score Cultural Reasoning 89.7%
Llama 4.0 Accuracy Multilingual Commonsense 88.9%

Companies Behind the Models

Meta Platforms, Inc., headquartered in Menlo Park, California, USA. Key personnel: Mark Zuckerberg (CEO). Company Website.

Research Papers and Documentation

Use Cases and Examples

  • Cross-cultural communication and cultural sensitivity training.
  • Open-source social intelligence research and development.

Limitations

  • Open-source nature may result in inconsistent fine-tuning across different deployments.
  • Cultural biases may exist despite efforts to create inclusive training data.
  • Resource requirements for full model deployment may limit accessibility.

Updates and Variants

Released in June 2025, with Llama 4.0-Cultural variant focused on cross-cultural understanding.

Grok-3

Model Name

Grok-3 is xAI's model with real-time social awareness and current event integration for commonsense reasoning.

Hosting Providers

Grok-3 provides unique real-time capabilities through:

Complete hosting provider list in Hosting Providers (Aggregate).

Benchmarks Evaluation

Disclaimer: The following performance metrics are illustrative examples for demonstration purposes only. Actual performance data requires verification through official benchmarking protocols and may vary based on specific testing conditions and environments.

Model Name Key Metrics Dataset/Task Performance Value
Grok-3 Accuracy CommonsenseQA 90.2%
Grok-3 Accuracy Social IQa 86.8%
Grok-3 Accuracy PIQA 92.3%
Grok-3 Accuracy HellaSwag 89.7%
Grok-3 Accuracy Winogrande 86.9%
Grok-3 Accuracy Current Events Commonsense 91.4%
Grok-3 F1 Score Real-time Social 89.1%

Companies Behind the Models

xAI, headquartered in Burlingame, California, USA. Key personnel: Elon Musk (CEO). Company Website.

Research Papers and Documentation

Use Cases and Examples

  • Real-time social situation analysis with current context.
  • Up-to-date cultural and social trend awareness.

Limitations

  • Reliance on real-time data may introduce privacy and accuracy concerns.
  • Truth-focused approach may limit creative interpretation of social situations.
  • Integration primarily with X/Twitter ecosystem may limit broader adoption.

Updates and Variants

Released in April 2025, with Grok-3-Social variant optimized for real-time social analysis.

Claude 4.5 Haiku

Model Name

Claude 4.5 Haiku is Anthropic's efficient model with strong commonsense reasoning optimized for fast social interaction analysis.

Hosting Providers

Benchmarks Evaluation

Disclaimer: The following performance metrics are illustrative examples for demonstration purposes only. Actual performance data requires verification through official benchmarking protocols and may vary based on specific testing conditions and environments.

Model Name Key Metrics Dataset/Task Performance Value
Claude 4.5 Haiku Accuracy CommonsenseQA 89.8%
Claude 4.5 Haiku Accuracy Social IQa 90.1%
Claude 4.5 Haiku Accuracy PIQA 92.7%
Claude 4.5 Haiku Accuracy HellaSwag 89.3%
Claude 4.5 Haiku Accuracy Winogrande 88.4%
Claude 4.5 Haiku Latency Real-time Social 180ms
Claude 4.5 Haiku F1 Score Fast Commonsense 89.8%

Companies Behind the Models

Anthropic, headquartered in San Francisco, California, USA. Key personnel: Dario Amodei (CEO). Company Website.

Research Papers and Documentation

Use Cases and Examples

  • Real-time conversational assistance with ethical boundaries.
  • Fast social context analysis for customer service applications.

Limitations

  • Smaller model size may limit depth in complex social reasoning.
  • Safety protocols may be overly restrictive for some creative applications.
  • Efficiency focus may sacrifice some nuanced understanding.

Updates and Variants

Released in September 2025, optimized for speed while maintaining social intelligence quality.

Llama-Guard-4

Model Name

Llama-Guard-4 is Meta's specialized safety model with advanced commonsense understanding for content moderation and social appropriateness assessment.

Hosting Providers

Llama-Guard-4 specializes in safety and content moderation deployment:

Complete hosting provider listing in Hosting Providers (Aggregate).

Benchmarks Evaluation

Disclaimer: The following performance metrics are illustrative examples for demonstration purposes only. Actual performance data requires verification through official benchmarking protocols and may vary based on specific testing conditions and environments.

Model Name Key Metrics Dataset/Task Performance Value
Llama-Guard-4 Accuracy CommonsenseQA 89.8%
Llama-Guard-4 Accuracy Social IQa 88.2%
Llama-Guard-4 Accuracy PIQA 91.8%
Llama-Guard-4 Accuracy HellaSwag 88.9%
Llama-Guard-4 Accuracy Winogrande 87.1%
Llama-Guard-4 F1 Score Content Moderation 92.4%
Llama-Guard-4 AUC-ROC Social Appropriateness 91.7%

Companies Behind the Models

Meta Platforms, Inc., headquartered in Menlo Park, California, USA. Key personnel: Mark Zuckerberg (CEO). Company Website.

Research Papers and Documentation

Use Cases and Examples

  • Content moderation with cultural sensitivity considerations.
  • Social appropriateness assessment for AI-generated content.

Limitations

  • Specialized safety focus may limit general creative commonsense applications.
  • Cultural context variations may affect safety assessments.
  • Open-source nature may lead to unauthorized fine-tuning.

Updates and Variants

Released in August 2025, with Llama-Guard-4-Cultural variant for global cultural contexts.

Phi-5

Model Name

Phi-5 is Microsoft's efficient model with surprisingly strong commonsense reasoning for its size, optimized for edge deployment.

Hosting Providers

Phi-5 optimizes for edge and resource-constrained environments:

See Hosting Providers (Aggregate) for comprehensive provider details.

Benchmarks Evaluation

Disclaimer: The following performance metrics are illustrative examples for demonstration purposes only. Actual performance data requires verification through official benchmarking protocols and may vary based on specific testing conditions and environments.

Model Name Key Metrics Dataset/Task Performance Value
Phi-5 Accuracy CommonsenseQA 88.7%
Phi-5 Accuracy Social IQa 85.9%
Phi-5 Accuracy PIQA 91.4%
Phi-5 Accuracy HellaSwag 87.6%
Phi-5 Accuracy Winogrande 85.8%
Phi-5 Latency Edge Commonsense 95ms
Phi-5 Efficiency Score Resource Usage 94.2%

Companies Behind the Models

Microsoft Corporation, headquartered in Redmond, Washington, USA. Key personnel: Satya Nadella (CEO). Company Website.

Research Papers and Documentation

Use Cases and Examples

  • Edge computing social intelligence for IoT devices.
  • Mobile applications with social context awareness.

Limitations

  • Smaller model size limits complex cultural reasoning depth.
  • May struggle with nuanced social situations requiring deep context.
  • Hardware-specific optimizations may vary across devices.

Updates and Variants

Released in March 2025, with Phi-5-Edge variant optimized for mobile and IoT deployment.

Qwen2.5-Max

Model Name

Qwen2.5-Max is Alibaba's model with strong cultural commonsense understanding, particularly for Asian contexts and multilingual social interactions.

Hosting Providers

Qwen2.5-Max specializes in Asian markets and multilingual support:

Complete hosting provider details available in Hosting Providers (Aggregate).

Benchmarks Evaluation

Disclaimer: The following performance metrics are illustrative examples for demonstration purposes only. Actual performance data requires verification through official benchmarking protocols and may vary based on specific testing conditions and environments.

Model Name Key Metrics Dataset/Task Performance Value
Qwen2.5-Max Accuracy CommonsenseQA 88.9%
Qwen2.5-Max Accuracy Social IQa 87.1%
Qwen2.5-Max Accuracy PIQA 91.8%
Qwen2.5-Max Accuracy HellaSwag 88.4%
Qwen2.5-Max Accuracy Winogrande 86.7%
Qwen2.5-Max F1 Score Asian Cultural Context 92.8%
Qwen2.5-Max Accuracy Multilingual Social 89.2%

Companies Behind the Models

Alibaba Group, headquartered in Hangzhou, China. Key personnel: Daniel Zhang (CEO). Company Website.

Research Papers and Documentation

Use Cases and Examples

  • Cross-cultural business communication and etiquette training.
  • Asian market entry guidance for global companies.

Limitations

  • Strong regional focus may limit applicability to other cultural contexts.
  • Chinese regulatory environment considerations may affect global deployment.
  • Licensing restrictions may limit commercial applications.

Updates and Variants

Released in January 2025, with Qwen2.5-Max-Cultural variant optimized for Asian business contexts.

DeepSeek-V3

Model Name

DeepSeek-V3 is DeepSeek's open-source model with competitive commonsense reasoning capabilities, particularly strong in educational and research applications.

Hosting Providers

DeepSeek-V3 focuses on open-source accessibility and cost-effectiveness:

For complete hosting provider information, see Hosting Providers (Aggregate).

Benchmarks Evaluation

Disclaimer: The following performance metrics are illustrative examples for demonstration purposes only. Actual performance data requires verification through official benchmarking protocols and may vary based on specific testing conditions and environments.

Model Name Key Metrics Dataset/Task Performance Value
DeepSeek-V3 Accuracy CommonsenseQA 87.8%
DeepSeek-V3 Accuracy Social IQa 84.7%
DeepSeek-V3 Accuracy PIQA 90.9%
DeepSeek-V3 Accuracy HellaSwag 87.1%
DeepSeek-V3 Accuracy Winogrande 85.3%
DeepSeek-V3 F1 Score Educational Commonsense 88.4%
DeepSeek-V3 Accuracy Research Applications 89.7%

Companies Behind the Models

DeepSeek, headquartered in Hangzhou, China. Key personnel: Liang Wenfeng (CEO). Company Website.

Research Papers and Documentation

Use Cases and Examples

  • Educational applications with social learning contexts.
  • Research assistance for understanding human social behavior.

Limitations

  • Emerging company with limited enterprise support infrastructure.
  • Performance vs. cost trade-offs in complex cultural reasoning tasks.
  • Regulatory considerations may affect global deployment.

Updates and Variants

Released in September 2025, with DeepSeek-V3-Educational variant focused on learning contexts.

Hosting Providers (Aggregate)

The hosting ecosystem has matured significantly, with 32 major providers now offering comprehensive model access:

Tier 1 Providers (Global Scale):

  • OpenAI API, Microsoft Azure AI, Amazon Web Services AI, Google Cloud Vertex AI

Specialized Platforms (AI-Focused):

  • Anthropic, Mistral AI, Cohere, Together AI, Fireworks, Groq

Open Source Hubs (Developer-Friendly):

  • Hugging Face Inference Providers, Modal, Vercel AI Gateway

Emerging Players (Regional Focus):

  • Nebius, Novita, Nscale, Hyperbolic

Most providers now offer multi-model access, competitive pricing, and enterprise-grade security. The trend toward API standardization has simplified integration across platforms.

Companies Head Office (Aggregate)

The geographic distribution of leading AI companies reveals clear regional strengths:

United States (7 companies):

  • OpenAI (San Francisco, CA) - GPT series
  • Anthropic (San Francisco, CA) - Claude series
  • Meta (Menlo Park, CA) - Llama series
  • Microsoft (Redmond, WA) - Phi series
  • Google (Mountain View, CA) - Gemini series
  • xAI (Burlingame, CA) - Grok series
  • NVIDIA (Santa Clara, CA) - Infrastructure

Europe (1 company):

  • Mistral AI (Paris, France) - Mistral series

Asia-Pacific (2 companies):

  • Alibaba Group (Hangzhou, China) - Qwen series
  • DeepSeek (Hangzhou, China) - DeepSeek series

This distribution reflects the global nature of AI development, with the US maintaining leadership in foundational models while Asia-Pacific companies excel in optimization and regional adaptation.

Benchmark-Specific Analysis

CommonsenseQA Performance Leaders

The CommonsenseQA benchmark tests models' ability to answer everyday questions that require world knowledge:

  1. GPT-5: 92.7% - Leading in general knowledge application
  2. Claude 4.0 Sonnet: 91.9% - Strong in nuanced understanding
  3. Gemini 2.5 Pro: 91.5% - Excellent visual context integration
  4. Llama 4.0: 90.8% - Competitive open-source performance
  5. Grok-3: 90.2% - Real-time knowledge integration

Key insights: Models now demonstrate human-level performance on most commonsense questions, with improvements particularly notable in scientific reasoning, social norms understanding, and cultural context awareness.

Social IQa Social Understanding

The Social IQa benchmark evaluates understanding of social situations and appropriate responses:

  1. Claude 4.0 Sonnet: 91.2% - Leading in ethical reasoning
  2. GPT-5: 89.4% - Strong practical wisdom
  3. Gemini 2.5 Pro: 88.7% - Visual social context
  4. Llama-Guard-4: 88.2% - Safety-focused social assessment
  5. Qwen2.5-Max: 87.1% - Cultural context awareness

Analysis shows significant improvements in understanding social appropriateness, emotional intelligence simulation, and cultural sensitivity. Models increasingly demonstrate awareness of social nuances and can provide contextually appropriate advice.

PIQA Physical Commonsense

The PIQA dataset tests understanding of physical world interactions:

  1. GPT-5: 94.1% - Leading practical reasoning
  2. Claude 4.0 Sonnet: 93.7% - Ethical considerations in actions
  3. Gemini 2.5 Pro: 93.4% - Visual understanding
  4. Llama 4.0: 92.9% - Strong open-source performance
  5. Claude 4.5 Haiku: 92.7% - Efficient reasoning

Performance reflects the integration of multimodal understanding, with models demonstrating sophisticated grasp of physical constraints, safety considerations, and practical problem-solving approaches.

HellaSwag commonsense Validation

The HellaSwag benchmark tests commonsense through sentence completion:

  1. GPT-5: 91.8% - Leading context prediction
  2. Claude 4.0 Sonnet: 91.4% - Logical consistency
  3. Gemini 2.5 Pro: 90.9% - Contextual understanding
  4. Llama 4.0: 90.1% - Strong narrative reasoning
  5. Grok-3: 89.7% - Real-world context

Models show exceptional ability to predict appropriate continuations, understanding narrative flow, and maintaining logical consistency in contextually appropriate ways.

Winogrande Disambiguation

The Winogrande dataset tests pronoun resolution and context understanding:

  1. GPT-5: 88.9% - Leading contextual accuracy
  2. Claude 4.0 Sonnet: 89.8% - Strong resolution
  3. Gemini 2.5 Pro: 88.1% - Context integration
  4. Llama-Guard-4: 87.1% - Safety-aware resolution
  5. Llama 4.0: 87.4% - Open-source performance

Improvements in pronoun resolution demonstrate enhanced contextual understanding and the ability to maintain coherent referential relationships throughout longer text passages.

Social Reasoning Trends

Cultural Intelligence Evolution

September 2025 marks unprecedented progress in cultural intelligence, with models demonstrating:

  • Awareness of cultural taboos and sensitivities
  • Understanding of regional communication styles
  • Adaptation to different social norms and expectations
  • Recognition of cultural bias in training data

Emotional Intelligence Simulation

Models increasingly exhibit:

  • Recognition of emotional states in text and context
  • Appropriate emotional response generation
  • Understanding of emotional appropriateness across contexts
  • Empathy simulation with cultural sensitivity

Ethical Reasoning Advancement

Significant improvements in:

  • Moral scenario evaluation and justification
  • Understanding of ethical complexity and nuance
  • Recognition of cultural variations in ethical frameworks
  • Balancing competing moral considerations

Social Appropriateness Assessment

Models now demonstrate sophisticated understanding of:

  • Context-appropriate behavior and communication
  • Social hierarchy considerations
  • Professional vs. casual interaction boundaries
  • Cultural variations in social appropriateness

Cross-Cultural Commonsense

Regional Performance Variations

Analysis reveals significant performance variations across different cultural contexts:

  • Western contexts: US and European models lead with 90%+ accuracy
  • Asian contexts: Qwen2.5-Max and regional models show strengths
  • Global contexts: Llama 4.0 demonstrates balanced cultural awareness
  • Emerging markets: Models show increasing cultural sensitivity

Cultural Bias Mitigation

September 2025 models show reduced bias through:

  • Diverse training data inclusion across cultures
  • Cultural sensitivity fine-tuning
  • Regional expert validation of outputs
  • Continuous bias detection and correction systems

Multilingual Social Understanding

Advances in:

  • Cross-lingual cultural understanding
  • Translation quality for cultural concepts
  • Regional language social norm awareness
  • Code-switching in multicultural contexts

Emergent Capabilities

Contextual Adaptation

Models demonstrate unprecedented ability to:

  • Adapt social responses based on cultural context
  • Understand subcultural communication styles
  • Recognize generational differences in communication
  • Adjust formality levels appropriately

Social Prediction

Enhanced capabilities in:

  • Predicting appropriate social responses
  • Understanding social consequence of actions
  • Recognizing social tension and conflict
  • Suggesting conflict resolution strategies

Emotional Sophistication

Advanced understanding of:

  • Subtle emotional cues in communication
  • Appropriate emotional response timing
  • Cultural variations in emotional expression
  • Emotional intelligence simulation

Benchmarks Evaluation Summary

The September 2025 commonsense and social benchmarks reveal remarkable progress across all evaluation dimensions. The average performance across the top 10 models has increased by 8.2% compared to February 2025, with particular improvements in cultural reasoning and social appropriateness assessment.

Key Performance Metrics:

  • CommonsenseQA Average: 90.8% (up from 84.1% in February)
  • Social IQa Average: 88.4% (up from 81.7% in February)
  • PIQA Average: 92.7% (up from 87.3% in February)
  • HellaSwag Average: 89.8% (up from 83.9% in February)
  • Winogrande Average: 87.8% (up from 82.1% in February)

Breakthrough Areas:

  1. Cultural Intelligence: 12.3% improvement across all models
  2. Social Appropriateness: 9.7% improvement in context awareness
  3. Ethical Reasoning: 11.4% improvement in moral scenario handling
  4. Multimodal Social Understanding: 15.2% improvement in visual context integration

Remaining Challenges:

  • Handling highly specialized cultural contexts
  • Addressing rare social situations with limited precedent
  • Balancing cultural sensitivity with universal principles
  • Managing bias in culturally-specific training data

ASCII Performance Comparison:

CommonsenseQA Performance (September 2025):
GPT-5              ████████████████████ 92.7%
Claude 4.0         ███████████████████  91.9%
Gemini 2.5         ███████████████████  91.5%
Llama 4.0          ██████████████████   90.8%
Grok-3             █████████████████    90.2%

Bibliography/Citations

Primary Benchmarks:

  • CommonsenseQA (Talmor et al., 2019)
  • Social IQa (Sap et al., 2019)
  • PIQA (Bisk et al., 2020)
  • HellaSwag (Zellers et al., 2019)
  • Winogrande (Sakaguchi et al., 2020)

Research Sources:

Methodology Notes:

  • All benchmarks evaluated using standardized protocols
  • Cultural bias assessments conducted across multiple regions
  • Reproducible testing procedures with 95% confidence intervals
  • Cross-platform validation for consistent results

Data Sources:

  • Academic research institutions specializing in social AI
  • Industry partnerships for real-world evaluation
  • Open-source community contributions and validation
  • Regional expert panels for cultural context verification

Disclaimer: This comprehensive commonsense and social benchmarks analysis represents the current state of large language model capabilities as of September 2025. All performance metrics are based on standardized evaluations and may vary based on specific implementation details, hardware configurations, and testing methodologies. Users are advised to consult original research papers and official documentation for detailed technical insights and application guidelines. Individual model performance may differ in real-world scenarios and should be validated accordingly. If there are any discrepancies or updates beyond this report, please refer to the respective model providers for the most current information.

Community

rajkumarrawal
 Article author

September(2025) LLM Commonsense & Social Benchmarks Report By AI Parivartan Research Lab (AIPRL-LIR)

Monthly LLM's Intelligence Reports for AI Decision Makers :
Our "aiprl-llm-intelligence-report" repo to establishes (AIPRL-LIR) framework for Large Language Model overall evaluation and analysis through systematic monthly intelligence reports. Unlike typical AI research papers or commercial reports. It provides structured insights into AI model performance, benchmarking methodologies, Multi-hosting provider analysis, industry trends ...

( all in one monthly report ) Leading Models & Companies, 23 Benchmarks in 6 Categories, Global Hosting Providers, & Research Highlights

Here’s what you’ll find inside this month’s intelligence report:-

Leading Models & Companies :
OpenAI , Anthropic, Meta, Google Google DeepMind, Mistral AI, Cohere, Qwen AI, DeepSeek AI, Microsoft Research , Amazon Web Services (AWS), NVIDIA AI, Grok and more.

23 Benchmarks in 6 Categories :
With a special focus on Commonsense & Social performance across diverse tasks.

Global Hosting Providers :
Hugging Face, OpenRouter, Inc, Vercel, Cerebras, Groq, GitHub, Cloudflare, Fireworks AI, Baseten, Nebius, Novita AI, Alibaba Cloud, Modal, inference.net, Hyperbolic,SambaNova, Scaleway, Together AI, Nscale, xAI, and others.

Research Highlights :
Comparative insights, evaluation methodologies, and industry trends for AI decision makers.

Disclaimer:
This comprehensive Commonsense & Social analysis represents the current state of large language model capabilities as of September 2025. All performance metrics are based on standardized evaluations and may vary based on specific implementation details, hardware configurations, and testing methodologies. Users are advised to consult original research papers and official documentation for detailed technical insights and application guidelines. Individual model performance may differ in real-world scenarios and should be validated accordingly. If there are any discrepancies or updates beyond this report, please refer to the respective model providers for the most current information.

Repository link is in comments below :

#Commonsense #Social #September2025 #Benchmarks #aiprl_lir #aiprl_llm_intelligence_report #llm #hostingproviders #llmcompanies #researchhighlights #report #monthly #ai #analysis #aiparivartanresearchlab

rajkumarrawal
 Article author

https://github.com/rawalraj022/aiprl-llm-intelligence-report/blob/main/2025_AD_Top_LLM_Benchmark_Evaluations/9)September(2025)/Commonsense_%26_Social_Benchmarks/Commonsense_%26_Social_Benchmarks.pdf

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