LLM Deceptiveness and Gullibility Benchmark

What it does

This benchmark assesses large language models along two critical dimensions: their capability to generate convincing disinformation and their resilience against misleading information. The evaluation framework uses recent articles outside the models' training data, deriving fact-based questions that probe both deceptive capabilities and resistance to manipulation. Models must craft persuasive but misleading arguments, while also demonstrating their ability to maintain accurate reasoning when faced with deceptive content from other models. Some models often refuse to create disinformation, while others have no problem and always comply when asked. Benchmark evaluating LLMs on their ability…from github.com

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  I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…

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  LocalScore – Local LLM Benchmark2025 · localscore.ai · ▲124

  Hey Folks! I've been building an open source benchmark for measuring local LLM performance on your own hardware. The benchmarking tool is a CLI written on top of Llamafile to allow for portability across different hardware setups and operating systems. The website is a database of results from the benchmark, allowing you to explore the performance of different models and hardware configurations. Please give it a try! Any feedback and contribution is much appreciated. I'd love for this to serve as a helpful resource for the local AI community. For more check out: - Website:…

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