How to Evaluate a New LLM for Production: Method and Criteria

When a new model launches promising drastically lower costs and permissive licenses, enthusiasm is legitimate — but production demands method, not faith.

What to Actually Check (Verifiable Checklist)

| Criterion | How to Verify Today |

|-----------|----------------------|

| License | Read the `LICENSE` file on the official repo (Hugging Face / GitHub). MIT, Apache-2.0, BSD = commercial green light. Custom licenses with "non-compete" or "usage restriction" clauses = legal risk requiring legal review. |

| Real API Pricing | Check the provider's pricing page (OpenRouter, Together, Fireworks, DeepSeek Platform, etc.). Compare $/M token input/output against the models you currently use. |

| Benchmark on Your Task | Public benchmarks (MMLU, HumanEval, GPQA) are weak indicators. Take 100-500 real examples from your workflow (code review, changelog, data extraction, classification). Run side-by-side: current model vs. candidate. Measure: accuracy, latency, cost, failure rate. |

| Availability & SLA | Is the model served by a provider with an SLA? Are there rate limits? What happens if the provider shuts down the endpoint? Do you have a Plan B (self-hosted, alternative provider)? |

| Context & Languages | Verify effective context window (not just claimed) and quality on the languages you need (Italian, German, Japanese, etc.). |

Method: Vertical Migration, Not "Big Bang"

Don't migrate everything. Migrate one vertical, measurable workflow.

Concrete Example: Changelog Generation from Commit Messages

1. Take 500 real commits from your repo.

2. Generate changelog with current model (e.g., GPT-4o mini: ~$0.15/$0.60 per M tokens → estimated cost ~$X).

3. Regenerate with candidate model via API (estimated cost ~$Y).

4. Have 2-3 senior developers evaluate quality blind (they don't know which model generated what).

5. If quality delta is <5% and savings >80%, you have the business case for that workflow. Then the next one.

You don't need corporate "buy-in." You need a task, a benchmark, a number.

What We Offer

Progetto Siliceo helps technical teams industrialize LLMs in production: model evaluation on real tasks, RAG/agentic architecture, cost optimization, observability. We don't sell GPUs. We sell speed to value.

Want a real benchmark on your code? Write to us. You bring the repo, we bring the task. We measure together.