LLM Leaderboard?
Build Your Own.
Generic leaderboards rank models on standardized tests. But your production task isn't standardized. Create a personal AI model leaderboard that ranks 100+ models on YOUR actual use case.
The Problem with LLM Leaderboards
LMSYS Chatbot Arena, Open LLM Leaderboard, MMLU rankings — they all suffer from the same problems:
Benchmark Contamination
Models may have trained on test data. A model scoring 92% on MMLU might score 65% on a novel, unseen task — like YOUR use case.
No Cost Data
Leaderboards rank by accuracy alone. A model that's 3% better but costs 50x more isn't the "best" for production workloads.
Generic Tasks
MMLU tests general knowledge. HumanEval tests Python. Your customer support, legal review, or data extraction pipeline isn't tested anywhere.
Subjective Voting
Arena-style leaderboards use human voting — subjective, noisy, and biased toward verbose/confident-sounding responses.
A real OpenMark leaderboard — YOUR task, YOUR rankings, YOUR data.
Your Custom LLM Leaderboard
OpenMark lets you create a leaderboard that matters — one based on YOUR actual prompts and use cases:
Leaderboard You Can Sort
Unlike static leaderboards, OpenMark's results table is interactive. Sort by any column to find the model that fits your priority:
| Rank | Model | Score | Cost | Acc/$ | Speed |
|---|---|---|---|---|---|
| 1 | claude-sonnet-4.5 Anthropic | 82% | $0.0038 | 118.5K | 24s |
| 2 | gpt-4o OpenAI | 78% | $0.0045 | 95.2K | 18s |
| 3 | deepseek-v3 DeepSeek | 75% | $0.0003 | 878.8K | 22s |
| 4 | gemini-2.5-flash | 73% | $0.0005 | 450.1K | 15s |
↑ Example data. YOUR leaderboard will reflect YOUR task's results.
"We replaced our weekly leaderboard check with a monthly OpenMark benchmark on our actual production prompts. We caught a model regression that leaderboards missed — our production pipeline would have broken."
FAQ
How is this different from the Chatbot Arena leaderboard?
Chatbot Arena ranks by human voting on random conversations — subjective and generic. OpenMark ranks by deterministic scoring on YOUR actual prompts, with cost and speed data included.
Can I compare my rankings over time?
Yes. Run the same benchmark monthly to track model improvements, regressions, and pricing changes. Your benchmark history is saved for comparison.
Does this replace standard benchmarks?
Not entirely. Standard benchmarks are useful for general capability assessment. But for production decisions, you need a leaderboard based on YOUR specific task. Learn more about custom benchmarking →
Build Your Own LLM Leaderboard
Rank 100+ models on YOUR task. Real data, not generic scores.
Free tier — no credit card required.