Side by side
DecodingTrust vs SWE-bench
Both stand on llm evaluation tools. Which one you can start free with, which one lets you read the code, and what each costs once you pay.
Paid APISelf-hosted
DecodingTrust is a focused toolkit for structured LLM trustworthiness evaluations. Its broad assessment areas and reproducibility-oriented model versions are useful, but setup and content warnings matter.
Visit DecodingTrust Free plan APILinuxMac+3
SWE-bench provides datasets and an evaluation harness for checking model-generated patches against repository tests. Docker is central to consistent evaluations, and its storage requirements can be substantial.
Visit SWE-benchThe facts
| DecodingTrust | SWE-bench | |
|---|---|---|
| Free plan | Not published | Yes |
| Open source | Not published | Not published |
| Runs on | API, Self-hosted | API, Linux, Mac, Self-hosted, Web, Windows |
| Lowest paid plan | Not published | Not published |
| Plans published | None | None |
| Maker | Not published | Not published |
| Website | decodingtrust.github.io | swebench.com |
| On llm evaluation tools | #25 of 29 | #21 of 29 |
DecodingTrust: what it does well
- Covers eight trustworthiness assessment areas
- Provides dataset and evaluation scripts
- Supports GPT-3.5, GPT-4, and causal LLMs
- Documents Docker and HPC-oriented containers
- Free under CC BY-SA 4.0
What to know first
- Main benchmark focus is older GPT model versions
- Data may contain offensive model outputs
- Installation includes PyTorch and CUDA compatibility concerns
SWE-bench: what it does well
- Free to use.
- Original dataset draws from 12 Python repositories.
- Offers Verified, Multimodal, and Multilingual datasets.
- CLI submits predictions and retrieves reports.
- Cloud evaluations can use Modal and sb-cli.
What to know first
- Docker is required for consistent evaluation.
- Installation requires Python 3.9 or later and Docker.
- Guide recommends at least 120 GB free disk space.
- Caching all Docker images can require about 2,000 GB.
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