LM Evaluation Harness vs Promptfoo
Both are eval frameworks tools. Here is how they actually differ on price, billing model and deployment.
LM Evaluation Harness
EleutherAI's academic benchmarking framework and the backend behind the HuggingFace Open LLM Leaderboard. 60+ standard benchmarks, cited in hundreds of papers - and structurally unable to run multiple-choice tasks against chat-only APIs.
Promptfoo
The de-facto open-source CLI for LLM eval and red-teaming, driven by declarative YAML. MIT-licensed, ~23.5k stars, and acquired by OpenAI in March 2026 - still open source, now part of OpenAI Frontier.
| LM Evaluation Harness | Promptfoo | |
|---|---|---|
| Category | Eval Frameworks | Eval Frameworks |
| Our rating | 4/5 | 4/5 |
| Starting price | $0 (open source) | $0 |
| Billing meter | No usage metering | No usage metering |
| Free plan | Yes | Yes |
| Free self-hosting | Yes, free | Yes, free |
| Best for | Anyone benchmarking base models, comparing fine-tunes against published baselines, or producing numbers that need to line up with academic literature and the Open LLM Leaderboard. | Security and CI teams who want config-driven LLM eval plus serious red-teaming, from an OSS tool with no seat cost |
Our verdict on LM Evaluation Harness
LM Evaluation Harness is the standard for academic LLM benchmarking and should be your default whenever the question is how good is this model rather than how good is my application. It covers 60+ standard benchmarks with hundreds of subtasks, it is the backend behind the HuggingFace Open LLM Leaderboard, and it is cited in hundreds of papers - which means your numbers are directly comparable with published results instead of being your own private metric. It is actively maintained, with a 2026 release adding a proper subcommand CLI with YAML configs and modular installs. The critical limitation to understand before you plan around it is that loglikelihood is not supported for chat completions, because OpenAI does not expose prompt logprobs. That means multiple-choice benchmarks - a large share of the standard suite - cannot run against chat-only APIs at all. If you are evaluating open-weight models you serve yourself, this is a non-issue. If you intended to benchmark GPT-class endpoints on MMLU-style tasks, it is a wall.
Full LM Evaluation Harness review →Our verdict on Promptfoo
The clearest pick if red-teaming and security testing are part of your eval story, and its YAML-in-git approach fits CI cleanly. The OpenAI acquisition in March 2026 is the thing to weigh - the commitment to stay MIT is on the record, but you are now betting on OpenAI's stewardship of the project. For the free tier alone it is still an easy yes.
Full Promptfoo review →Frequently Asked Questions
What is the main difference between LM Evaluation Harness and Promptfoo?
LM Evaluation Harness: Anyone benchmarking base models, comparing fine-tunes against published baselines, or producing numbers that need to line up with academic literature and the Open LLM Leaderboard. Promptfoo: Security and CI teams who want config-driven LLM eval plus serious red-teaming, from an OSS tool with no seat cost Both sit in Eval Frameworks, so the decision usually comes down to billing model and deployment rather than raw capability.
Which is cheaper, LM Evaluation Harness or Promptfoo?
It depends entirely on your workload shape, because they meter differently - LM Evaluation Harness bills on no usage metering and Promptfoo bills on no usage metering. Published starting prices are $0 (open source) and $0 respectively, but those numbers are not comparable until you apply them to the same traffic. Use our cost calculator to model both against your own request volume and span count.
Can I self-host LM Evaluation Harness or Promptfoo?
LM Evaluation Harness: Yes, free. Promptfoo: Yes, free. Free self-hosting means no licence fee, not no cost - you still own the infrastructure, upgrades and on-call.