Evaluate & Benchmark
Benchmarking LLMs on Real Tasks
An async LLM benchmarking platform that evaluates models from OpenAI, Anthropic, Google, and more across 150+ real-world tasks covering coding, reasoning, structured output, and long-context retrieval.
The problem
Teams have no standardized way to evaluate new LLM releases against realistic tasks before betting production traffic on them.
- A new model drops and the only evaluation is "it felt fine in the demo."
- You're comparing providers using benchmarks that don't look like your actual traffic.
- Switching models is a leap of faith because there's no repeatable way to compare them.
What NEO built
NEO built an async benchmarking platform with curated task suites across coding, reasoning, and structured output, running multiple provider endpoints side by side with automated and LLM-as-judge scoring.
The result
150+ tasks, 10 categories
Delivers side-by-side comparisons across 150+ tasks in 10 categories, with cost estimates attached to every model.
From the blog · 8 min
Benchmarking LLMs on Real Tasks: How We Evaluated 150+ Tasks Across 10 Categories
NEO built an async LLM benchmarking platform that evaluates models from OpenAI, Anthropic, Google, and more across 150+ real-world tasks covering coding, reasoning, structured output, and long-context retrieval.
Try this in your workspace
Paste this into NEO chat to kick off the same workflow on your own data.
Benchmark these model candidates against my real task suite side by side, score them with an LLM-as-judge, and give me a cost/quality comparison before I switch providers.
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