DeepEval vs EvalPlus
DeepEval
deepeval.comEvalPlus
github.comSide by side
| Figure | DeepEval | EvalPlus |
|---|---|---|
| Price from | $200/mo | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | Yes | — |
| Web | Yes | — |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 5 | 0 |
| Spec score · LLM Evaluation Tools | 9.3 (#1) | 7.3 (#20) |
| License | The repository and package identify DeepEval as Apache Software License 2.0. | The repository provides the software under the Apache License 2.0, with files under evalplus/eval additionally complying with the MIT License. |
| Purpose | DeepEval is an open-source LLM evaluation framework for building evaluation pipelines to test AI systems. | EvalPlus is a framework for rigorously evaluating large language models on code related tasks. |
Plans
DeepEval
- DeepEvalFree
- FreeFree
- Starter$200 a month
- Team$2,000 a month
- EnterprisePriced on request
In their own words
DeepEval
DeepEval is a free, open-source framework for building evaluation pipelines to test AI systems. Its Pytest-native evaluations run in CI/CD or as Python scripts, and the framework lists more than 50 research-backed metrics, including checks for hallucination, faithfulness, answer relevancy, summarization, toxicity and bias. It evaluates text, images and audio, including conversational and voice interactions, using methods such as G-Eval, DAG, QAG and JevEval. DeepEval can generate synthetic goldens from a knowledge base and model conversations across user personas. It also traces agent steps so they can be graded and inspected in a terminal or test runner. Integrations cover agent frameworks including LangChain, Pydantic AI, OpenAI Agents, LangGraph, LlamaIndex and CrewAI, alongside a range of model providers. By default, it sends basic telemetry to PostHog, excluding personally identifiable information and stored results; users can opt out with an environment variable. The maker describes DeepEval OS as for pre-production testing, with results in local files and an engineer-owned test runner. Enterprise capabilities are offered through Confident AI Evals, with self-hosted or maker-cloud deployment options.