Promptfoo vs SWE-bench

Promptfoo helps developers and enterprises evaluate prompts and test AI applications for security risks. It compares prompt outputs across multiple models in a web view. Red-team testing simulates users to look for prompt injections, jailbreaks, data leaks, business rule violations and insecure agent tool use. Its getting-started guide lists more than 60 providers, including OpenAI, Anthropic, Google and local models such as Ollama. Integrations include CI systems such as GitHub Actions, GitLab CI, Jenkins, Azure Pipelines, CircleCI and Bitbucket Pipelines, plus Splunk, Burp Suite, Google Sheets and MCP. Installation is available through npm, npx or Homebrew. Promptfoo requires Node.js 22.22.0 or newer and recommends Node.js 24 LTS. The free Community plan includes all LLM evaluation features and 10k red-team probes per month, and can run locally or be self-hosted. Enterprise includes managed cloud deployment; On-Premise runs on customer infrastructure. Promptfoo says it is based in San Francisco, is now part of OpenAI and remains open source.

SWE-bench evaluates language models on software issues collected from GitHub. A model generates a patch, which the evaluation harness applies in a Docker environment before running the repository tests to check whether the issue is resolved. The original benchmark contains 2,294 task instances from 12 Python repositories. Other listed datasets include SWE-bench Lite, Verified, Multimodal and Multilingual. Verified contains 500 instances engineers checked as solvable; Multilingual has 300 instances across nine languages and 42 repositories. Multimodal includes 100 development instances with screenshots and interface elements, while test evaluation uses the SWE-bench API. Its CLI can submit predictions, retrieve reports, manage evaluation runs and track performance in the cloud. Evaluations can run locally or in the cloud using Modal and sb-cli. Installation requires Python 3.9 or later and Docker. Guides cover macOS, Linux and Windows, with WSL 2 required on Windows. The Docker guide recommends at least 120 GB of free disk space and 16 GB of RAM; caching all Docker images can require about 2,000 GB. SWE-bench is free.