garak vs OpenCompass

garak is a command-line scanner for probing language models and systems that use them for security weaknesses. Its checks cover issues such as hallucination, data leakage, prompt injection, misinformation, toxicity generation, and jailbreaks. Probes try to elicit failures, while detectors inspect responses for patterns such as unsafe content or a failure to refuse. A standard scan can run without supervision and produce a report, with scan settings available for customization. Integrations include OpenAI, Hugging Face, Cohere, Replicate, AWS Bedrock, LiteLLM, REST-accessible models, and custom Python integrations. Hugging Face models can run locally or be accessed through an inference API or private endpoint. Output includes screen results, a detailed report log, a hit log, and a debug log. garak assesses text output only. It is developed in Linux and OSX and can be installed with pip or from source; listed platforms include Linux, macOS, Windows, and self-hosted use. It is free, with a free plan and no free trial. Listed capabilities include custom metrics, LLM-as-a-judge, and safety evaluations.

OpenCompass is a free, open-source Python platform for evaluating large language models across knowledge, reasoning, coding, science, language, long-context and safety datasets. It describes support for models from OpenAI, Anthropic, Gemini, Qwen, GLM, DeepSeek and other providers, along with more than 100 datasets. Evaluations can use zero-shot, few-shot or chain-of-thought methods with standard or dialogue prompt templates. The system supports task division, distributed evaluation and data-parallel work across multiple GPUs. Configuration files record experiments, and results can be reported in real time. Its modular design can be extended with models, datasets, task-division strategies and cluster-management systems. OpenCompass evaluates API models as well as open-source models; installation documentation lists LMDeploy and vLLM inference backends and an API option. A VLMEvalKit integration supports multimodal dataset loading, OpenAI-compatible inference and VLMEvalKit metrics. It is intended for self-hosted use under the Apache-2.0 license. The README recommends Python 3.12, while pyext-backed code-evaluation datasets require Python 3.10. Some datasets and agent evaluations need extra dependencies that may conflict.