DeepEval
8.3#1 in llm evaluation toolsFrom $200 a monthFree planWebAPILinuxmacOS
VisitDeepEval vs SWE-bench
What both publish
| Fact | DeepEval | SWE-bench |
|---|---|---|
| CLI | The `deepeval test run` command is built on Pytest, while `evaluate()` provides a Python-programmatic interface. | SWE-bench CLI can submit model predictions for evaluation, retrieve reports, manage evaluation runs, and track performance in the cloud. |
| Integrations | Listed integrations include LangChain, Pydantic AI, OpenAI Agents, LangGraph, AWS AgentCore, Strands, Google ADK, LlamaIndex, and CrewAI. | The project documents using Hugging Face datasets to load SWE-bench data and Modal for cloud evaluations. |
| Purpose | DeepEval is an open-source LLM evaluation framework for building evaluation pipelines to test AI systems. | SWE-bench evaluates language models on real-world GitHub software issues by asking them to generate patches that resolve the issues. |
DeepEval plans
- DeepEvalFree
- FreeFree
- Starter$200 a month
- Team$2,000 a month
- EnterprisePriced on request
SWE-bench plans
No prices published.
Pick DeepEval if…
DeepEval suits developers building repeatable AI evaluations in Python or CI/CD, especially those testing multiple modalities or agent workflows. Its free OS offering is for pre-production testing rather than production evaluation.
Pick SWE-bench if…
SWE-bench suits people evaluating language models on real software issues and patches. It is also relevant to teams that want to run evaluations locally with Docker or submit them to the cloud.