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Evaluate & Benchmark

Model comparison, consistency testing, latency profiling, and automated eval suites across providers and hardware.

The problem

Choosing and trusting a model means comparing quality, latency, and cost across providers and versions. Manual spreadsheets and one-off scripts do not scale.

  • A new model release gets evaluated by vibes, not by a rerun of your actual test suite.
  • Two model variants both look fine until someone checks with real statistics, not eyeballing.
  • Embedding quality drifts for months before anyone notices retrieval got worse.

How NEO helps

NEO runs structured benchmarks and regression suites, tracks cost and quality tradeoffs, and surfaces regressions before they hit production so you can pick models with evidence.

  • Runs async, multi-provider benchmarks scored by both automated checks and LLM-as-judge
  • Optimizes prompts in a closed dual-LLM loop with every iteration logged to a ledger
  • Statistically tests 3+ model variants with proper correction, not just eyeballed deltas
  • Audits embedding spaces for drift and outliers with CI-ready reports

Featured scenarios (3)

3 of 4 shown

Benchmarking LLMs on Real Tasks
150+ tasks, 10 categories

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.

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Multi-Variant LLM A/B Testing
<0.5ms routing overhead

Evaluate & Benchmark

Multi-Variant LLM A/B Testing

Statistical A/B/n testing for 3+ model variants with ANOVA, Bonferroni correction, and sub-millisecond routing overhead.

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Semantic Embedding Space Auditor
5 semantic checks, reproducible

Evaluate & Benchmark

Semantic Embedding Space Auditor

UMAP + HDBSCAN clustering with five semantic health checks: outliers, boundary ambiguity, polarity mismatch, and global collapse.

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Run this workflow in your workspace

Install NEO in VS Code or Cursor and ship training, evals, and deployment from chat.

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eval_pipeline.py
  def run_eval(model, tasks):-     results = model.predict(tasks)+     results = batch_eval(model, tasks)+     log_regression(results)      return score(results)