LLM Benchmark Tool
for Your Actual Task

Generic benchmarks like MMLU and HumanEval don't test what matters for YOUR use case. OpenMark lets you benchmark 100+ LLMs on your own prompts with deterministic scoring.

LLM benchmark results showing model rankings by accuracy, cost, and speed

Why Generic LLM Benchmarks Fall Short

MMLU tests college Q&A. HumanEval tests 164 Python functions. GPQA tests graduate-level science. These benchmarks are useful for researchers, but they don't tell you which model works best for your customer support bot, your data pipeline, your content generation system, or your agentic workflow.

Problem

Benchmark Contamination

Models may have seen test data during training. Scores look better than real-world performance. A model scoring 90% on MMLU might score 60% on your task.

Problem

Task Mismatch

Your legal contract review, medical analysis, or code migration has nothing in common with standardized tests. Generic benchmarks test generic skills.

Problem

Missing Cost Data

Leaderboards rank by accuracy but ignore that a "better" model might cost 50x more per task. For production workloads, cost matters as much as quality.

Problem

No Stability Metrics

A model averaging 85% accuracy might swing between 65% and 100%. For production, you need consistent results — not just high averages.

How OpenMark Benchmarking Works

OpenMark is a custom LLM benchmarking tool that tests models on YOUR prompts:

1️⃣ Describe your task in plain language — "Compare how models extract key dates from legal contracts" or "Test which model best classifies customer feedback by sentiment."
2️⃣ AI generates your benchmark — Our agent creates a structured YAML benchmark with your prompts, expected outputs, and scoring criteria. Review and edit if needed.
3️⃣ Select models to test — Pick specific models or use Smart Pick for an auto-selected diverse set across providers and price tiers.
4️⃣ Run and compare — OpenMark sends identical requests to every model, scores deterministically, and ranks them by accuracy, cost, speed, and stability.
100+
Models
18
Scoring Modes
15+
Providers

18 Scoring Modes for Any Task

OpenMark supports 18 deterministic scoring modes — no LLM-as-judge, no subjective evaluation:

Exact Match

Perfect string matching for precise answers

Contains

Check for required keywords or phrases

Regex

Pattern matching for structured outputs

Numeric

Tolerance-based number comparison

JSON Schema

Validate structured output against a schema

Multi-Label

Match multiple expected labels or categories

Plus 12 more modes including multi-label, ordered list, boolean, code execution, and pipeline variables for multi-step reasoning.

"Static benchmarks tell you which model is generally smart. Custom benchmarks tell you which model is smart at YOUR job. That's the only benchmark that matters for production."

What Makes This LLM Benchmark Different

✅ Your prompts, your data: Test on the exact prompts you'll use in production — not generic academic tests.
✅ Deterministic scoring: Same result every time. No LLM judging another LLM. No human voting variance.
✅ Real API costs: See what each model actually costs for your task — not just per-token rates.
✅ Stability metrics: Run multiple times to see consistency. Critical for production deployment decisions.
✅ 100+ models: Compare across OpenAI, Anthropic, Google, DeepSeek, Mistral, xAI, and more. Use results to inform model routing and orchestration — route simple tasks to budget models, complex ones to premium.
✅ Quick benchmark: Describe what you want to test in plain language — get results in minutes, not hours.

LLM Benchmarking FAQ

What is an LLM benchmark?

An LLM benchmark is a structured test that evaluates language model performance on specific tasks. Generic benchmarks (MMLU, HumanEval) test standardized skills. Custom benchmarks (like OpenMark) test models on YOUR actual prompts and use cases.

How long does a benchmark take?

A typical benchmark with 8-10 models completes in 2-5 minutes. Results stream in real-time as each model responds. Faster models finish first.

Can I benchmark reasoning models like o3 and DeepSeek Reasoner?

Yes. OpenMark supports reasoning models and handles extended thinking time. Timeout profiles automatically adjust for slower models.

Benchmark LLMs on YOUR Task

100+ models. Deterministic scoring. Real costs.
Free tier — no credit card required.

Run Your First Benchmark — Free →

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