LLMeBench provides a flexible framework for benchmarking large language models.
OpenLM Arena
OpenLM Arena compares open language models through leaderboards and benchmark results.
About OpenLM Arena
OpenLM Arena is a web-based platform that compares large language models (LLMs) through leaderboards and benchmark results. It operates an agent-driven battle platform where LLMs compete in head-to-head matchups, using an LLM-as-a-judge system to compute Elo ratings for each model. The platform also aggregates scores from multiple standardized evaluations, including the 10-challenge AAII benchmark, ARC-AGI v2 for fluid intelligence measurement, MMLU-Pro, and coding and vision tests, to provide additional context for model performance comparisons.
Key features
- Agent-driven LLM battle platform for head-to-head comparisons
- LLM-as-a-judge system to compute Elo ratings
- Leaderboard ranking models by Elo and benchmark scores
- Aggregated scores from 10-challenge AAII benchmark
- ARC-AGI v2 benchmark scores for fluid intelligence measurement
- Displays model license, organization, and multi-benchmark results
Best for: The platform is best for users seeking to compare the performance of different large language models using standardized benchmarks and head-to-head battle results.
Good for
- Uses LLM-as-a-judge for Elo ratings from head-to-head model battles
- Aggregates multiple challenging benchmarks for comprehensive model comparison
- Includes clear model metadata such as organization and license type
OpenLM Arena at a glance
- Website
- openlm.ai/chatbot-arena
- Category
- AI & Machine Learning › AI Data & Analytics
- Type
- Website
- Pricing
- Not confirmed. Check the site
- Platforms
- Web
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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OpenLM Arena FAQ
How are model rankings calculated on OpenLM Arena?
Rankings are based on Elo ratings from agent-driven LLM battles, plus aggregated scores from benchmarks including AAII, ARC-AGI, MMLU-Pro, coding and vision tests.
What benchmarks are used to evaluate models on the platform?
Evaluations include the 10-challenge AAII benchmark, ARC-AGI v2 for fluid intelligence, MMLU-Pro, coding and vision benchmarks.
What metadata is provided for each listed model?
Each model entry includes its organization, license type, and scores across all supported benchmarks.
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