MMLU-Pro alternatives: 6 tools compared (October 2026)

C-Eval is an evaluation dataset of Chinese multiple-choice exam questions. Companies serving Chinese-speaking customers use it to compare how well language models handle Chinese knowledge and reasoning.

Key functions

• Tests Chinese-language knowledge and reasoning of language models
• Few-shot examples with explanations per subject

Compared with MMLU-Pro

• Strongest elsewhere: Hong Kong, China.

Check before buying: The CC BY-NC-SA 4.0 license excludes commercial use; check with legal whether internal model selection in a company counts as commercial before using it. Results only describe Chinese-language performance. Public questions may have leaked into training data. For German or English use cases, other benchmarks fit better.

GSM8K is a widely used evaluation dataset of 8,500 English math word problems at grade school level. It checks whether a language model can reason through several calculation steps.

Key functions

• Problems need two to eight reasoning steps with basic arithmetic
• Natural-language solutions show each intermediate step

Compared with MMLU-Pro

• Strongest elsewhere: United States.

Check before buying: Many current models score very high on GSM8K, and the test problems are likely part of some training data, so the dataset separates weak models better than strong ones. Use it as a basic check and add harder or company-specific tests. The problems are in English only.

LegalBench is an open benchmark for evaluating legal reasoning in English language models. Legal and IT teams use it to see how well a model handles contracts, statutes and court decisions.

Key functions

• Tests classification, extraction, generation and entailment on legal text
• Few-shot training splits per task for prompting experiments

Compared with MMLU-Pro

• Strongest elsewhere: United States.

Check before buying: All tasks are in English and largely based on US law, so results do not transfer to German or EU law. Check the license of each task you use, since sources differ. Model outputs on legal questions still need review by qualified lawyers.

SWE-bench tests whether AI coding systems can fix real issues from open-source GitHub repositories. Its leaderboards are a common reference when companies compare coding assistants and agents.

Key functions

• Measures the share of real repository issues an AI agent resolves
• Containerized evaluation harness with Docker for reproducible runs
• SWE-bench Verified: 500 problems confirmed solvable by engineers

Compared with MMLU-Pro

• Strongest elsewhere: United States.

Check before buying: Public issues and fixes may have been seen during model training, so strong scores can overstate performance on your own code base. Running the harness needs Docker and compute, or a cloud option. Most tasks come from Python projects; test agents on your own repositories before a purchase decision.

FinanceBench is a test set for question answering on company financial reports. Finance and data teams use the open sample to check whether a model or RAG setup finds correct figures in filings.

Key functions

• Questions with answers and evidence strings from financial documents
• Designed to test retrieval and long-context setups for financial question answering

Check before buying: The open sample is licensed CC BY-NC 4.0, which excludes commercial use; clarify internal use with legal and ask Patronus AI about the full set. With 150 cases the sample is small, so add your own questions on your company's reports. The filings are US company documents in English.

LiveBench is an open benchmark for language models designed to limit test set contamination. It regularly adds new questions and scores answers automatically against objective solutions.

Key functions

• New questions from recent datasets, arXiv papers and news to limit contamination
• Automatic scoring against verifiable answers
• Agentic coding tasks run in Docker

Check before buying: Running the full benchmark costs API tokens for every model tested, and agentic coding tasks need Docker. The README notes that local model inference is unmaintained, so serve local models behind an OpenAI-compatible API. Questions are in English. The results say little about German-language performance.