The Enterprise LLM Selection Framework: Beyond Benchmark Scores

MMLU, HumanEval, and MATH benchmarks measure academic performance on standardised tasks. Your enterprise use case is not standardised. A model that scores 85% on MMLU may perform worse than a 78%-scoring model on your specific document types, query patterns, and output format requirements. Benchmark scores are a starting point for shortlisting, not a selection criterion. The only evaluation that matters is performance on your data, for your use case, at your required quality bar.

Evaluate every LLM candidate across six dimensions: (1) Task performance — accuracy on a golden dataset of 500+ examples from your specific use case. (2) Latency and throughput — median and p99 latency for your typical prompt size, at your target throughput (requests per second). (3) Context length — does the model reliably use information from across its full context window, or does attention degrade at long contexts? (4) Fine-tuning and customisation — can you adapt the model on proprietary data, and how much does this improve task performance? (5) Data residency and compliance — where is inference run, and what data processing agreements apply? (6) Total cost of ownership — API cost vs. self-hosted infrastructure cost at your volume.

The majority of enterprise use cases are best served by fine-tuning a mid-sized foundation model (7B–70B parameters) rather than calling a frontier API or training from scratch. Fine-tuning on 1,000–10,000 domain-specific examples typically closes 60–80% of the performance gap between a general-purpose model and a frontier model, at 5–20% of the inference cost. The exception is tasks requiring broad world knowledge, complex reasoning, or code generation — these are genuinely better served by frontier APIs (GPT-4o, Claude Opus, Gemini Ultra) until smaller models catch up.

For healthcare, financial services, defence, and government deployments, data residency is a compliance requirement that eliminates most API providers by default. If your data is classified, PHI, PII, or subject to data localisation regulations, your only options are on-premise deployment, a private cloud region with a BAA or equivalent agreement, or a sovereign cloud offering. Map your compliance requirements before evaluating models — there is no point benchmarking GPT-4o if you cannot legally send your data to OpenAI.

API costs that seem negligible in a pilot become significant at production scale. A use case processing 100,000 documents per month at 2,000 tokens per document costs $40,000–$120,000 per month at frontier API pricing. A self-hosted 70B parameter model on 4× A100 GPUs costs $8,000–$12,000 per month in cloud compute, with amortised model management overhead. The break-even point is typically 500,000–2,000,000 tokens per day. Build a three-year TCO model before committing to an API-only architecture.

Run a structured 4-week evaluation: Week 1 — build your golden dataset and define your quality metrics. Week 2 — shortlist 3–5 models and run automated evals against the golden dataset. Week 3 — human evaluation of outputs from top 2 models on 200 representative examples, latency and cost benchmarking. Week 4 — compliance and data residency review, final vendor negotiation. Most enterprise LLM selections that go wrong skip weeks 1 and 4 — they evaluate on the wrong data and discover compliance problems after committing to a vendor.

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