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...
The 4-step NEO workflow
- 1
Describe the task
State what you are comparing and the decision the benchmark should unblock.
- 2
Add context for NEO
Share prompts, graders, SLA targets, and providers to include.
- 3
NEO implements & delivers
NEO runs comparisons and ships a report with quality, latency, and cost.
- 4
Follow up or test it out
Re-run after changes and extend coverage where data is thin.
Ask NEO
How to run this scenario
Treat "Benchmarking LLMs on Real Tasks" as a first-class benchmark: compare models with evidence on latency, quality, and cost.
Approach
What NEO focuses on
- Define tasks, rubrics, and baselines that match production
- Run structured suites across versions and providers
- Surface regressions and cost-quality tradeoffs before you ship
Outcomes
What you get
- Comparable scores across runs with enough context to trust them
- Regression signals when prompts, models, or infra change
- A defensible pick for model and routing under your SLAs
Ready to try for yourself?
Open NEO in VS Code or Cursor and describe this scenario. NEO plans the work, runs experiments, and ships artifacts you can review and iterate on.