rajkumarrawal (Rajkumar rawal)

nvidia ,

grokgpt-org and more.

23 Benchmarks in 6 Categories :
With a special focus on Core Knowledge & Reasoning performance across diverse tasks.

Repository link is in comments below :

https://huggingface.co/blog/rajkumarrawal/september-2025-aiprl-lir-core-knowledge-reasoning#6935ab96da60512619b7cf1f

3 replies

·

posted an update 4 days ago

Post

2776

September(2025) LLM Core Knowledge & Reasoning Benchmarks Report By

AiParivartanResearchLab (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-llama ,

deepmind

google ,

mistralai ,

Cohere ,

Qwen ,

deepseek-ai ,

nvidia ,

grokgpt-org and more.

23 Benchmarks in 6 Categories :
With a special focus on Core Knowledge & Reasoning performance across diverse tasks.

Repository link is in comments below :

https://huggingface.co/blog/rajkumarrawal/september-2025-aiprl-lir-core-knowledge-reasoning#6935ab96da60512619b7cf1f

3 replies

·

reacted to their post with 👍 8 days ago

Post

1113

September(2025) LLM Commonsense & Social Benchmarks Report By

AiParivartanResearchLab (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-llama ,

google

deepmind ,

mistralai ,

Cohere ,

Qwen ,

deepseek-ai ,

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

nvidia ,

grokgpt-org and more.

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

Repository link is in comments below :

https://huggingface.co/blog/rajkumarrawal/september-2025-aiprl-lir-commonsense-social

2 replies

·

replied to their post 8 days ago

replied to their post 8 days ago

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.

posted an update 8 days ago

Post

1113

September(2025) LLM Commonsense & Social Benchmarks Report By

AiParivartanResearchLab (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-llama ,

google

deepmind ,

mistralai ,

Cohere ,

Qwen ,

deepseek-ai ,

nvidia ,

grokgpt-org and more.

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

Repository link is in comments below :

https://huggingface.co/blog/rajkumarrawal/september-2025-aiprl-lir-commonsense-social

2 replies

·

reacted to their post with 👍 9 days ago

Post

2479

September(2025) LLM Safety & Reliability 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 :

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

Global Hosting Providers :

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

Disclaimer:
This comprehensive Safety & Reliability 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 :

https://huggingface.co/blog/rajkumarrawal/september-2025-aiprl-lir-safety-reliability

1 reply

·

reacted to their post with 👍 10 days ago

Post

296

The best way to predict the future is to evaluate it systematically.

reacted to their post with 👍 10 days ago

Post

831

Annual AI Investment ROI Calculator:

Current Manual Process Cost: $X
AI Implementation Cost: $Y
Expected Efficiency Gain: Z%

Annual Savings = X × Z% = A
Annual AI Cost = Y
Net Annual Benefit = A - Y = B

ROI = (B ÷ Y) × 100%
Payback Period = (Y ÷ B) * 12 months

Monthly LLM's Intelligence Reports for AI Decision Makers :

Repository link is in comments below :

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

1 reply

·

replied to their post 10 days ago

posted an update 10 days ago

Post

2479

September(2025) LLM Safety & Reliability 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 :

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

Global Hosting Providers :

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

Disclaimer:
This comprehensive Safety & Reliability 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 :

https://huggingface.co/blog/rajkumarrawal/september-2025-aiprl-lir-safety-reliability

https://github.com/rawalraj022/aiprl-llm-intelligence-report

1 reply

·

replied to their post 21 days ago

posted an update 21 days ago

Post

831

Annual AI Investment ROI Calculator:

Current Manual Process Cost: $X
AI Implementation Cost: $Y
Expected Efficiency Gain: Z%

Annual Savings = X × Z% = A
Annual AI Cost = Y
Net Annual Benefit = A - Y = B

ROI = (B ÷ Y) × 100%
Payback Period = (Y ÷ B) * 12 months

Monthly LLM's Intelligence Reports for AI Decision Makers :

Repository link is in comments below :

1 reply

·

posted an update 24 days ago

Post

296

The best way to predict the future is to evaluate it systematically.