Not All LLMs Are Equal
The large language model market evolves monthly. Frontier cloud models, open-weight alternatives, and dozens of specialized models each have different strengths. The model that leads public benchmarks may underperform on your specific task type, data domain, or latency requirements. Our evaluation tests models against your actual workloads with your actual data to produce recommendations grounded in empirical performance, not marketing claims.
Benchmark Testing
We create evaluation datasets from your real use cases: document classification, entity extraction, summarization, code generation, or customer interaction. Each model is tested against identical inputs for fair comparison.
Accuracy Measurement
We measure task-specific accuracy: precision, recall, F1 scores for classification; BLEU and ROUGE for generation; human evaluation ratings for subjective quality. Metrics are chosen to match your success criteria.
Latency Profiling
Response time matters for user-facing applications. We measure P50, P95, and P99 latencies under various load patterns, including time-to-first-token for streaming applications and total generation time.
Cost-Per-Request Analysis
Token pricing, context window usage, prompt engineering efficiency, and caching opportunities all affect cost. We model monthly spend at your projected volume for each candidate model.
Evaluation Pipeline
Define Tasks
Identify evaluation scenarios
Build Dataset
Create test inputs from real data
Run Tests
Execute across all candidate models
Analyze
Score accuracy, speed, and cost
Recommend
Select optimal model per task
Define Tasks
Identify evaluation scenarios
Build Dataset
Create test inputs from real data
Run Tests
Execute across all candidate models
Analyze
Score accuracy, speed, and cost
Recommend
Select optimal model per task
LLM Model Comparison
Privacy and Deployment Considerations
Where your data goes when you call an LLM API is a critical business decision. Cloud-hosted models from leading AI providers process data on their infrastructure under their terms of service. Self-hosted open-weight models run on infrastructure you control. We evaluate the privacy implications of each option against your regulatory requirements and data sensitivity.
Data retention policies. We review each provider's data handling: whether inputs are used for training, how long they are retained, whether zero-data-retention agreements are available, and what contractual protections exist under enterprise agreements.
On-premises deployment. For organizations that cannot send data to external APIs, we evaluate self-hosted model options. Quantized versions of open-source models can run on surprisingly modest hardware for many business tasks. We benchmark these against cloud options to quantify the accuracy and latency trade-offs.
Multi-model strategies. Many production systems benefit from using different models for different tasks: a fast, inexpensive model for classification and routing, a powerful model for complex reasoning, and a specialized model for domain-specific extraction. We design model routing architectures that optimize cost and performance simultaneously.
Potential Outcomes
Engagements typically produce insights around model performance for your specific evaluation tasks, including cost projections, latency profiles, and accuracy scores. Depending on scope, this may include a recommended model strategy involving a single provider or a multi-model approach.
Contact us at ben@oakenai.tech to start evaluating LLMs for your use case.