Model Comparison
Side-by-side empirical benchmark testing across commercial foundation models, open-weights checkpoints, and internal fine-tunes on your actual enterprise data.
What We Evaluate
Public model leaderboards measure generic trivia and math puzzles that have little correlation with your actual business logic. We execute head-to-head evaluation runs on your exact tasks, measuring task completion accuracy, latency distributions (p50/p95/p99), token consumption, and edge-case failure modes.
Data-Driven Routing & Economics
Often, an open-weights 8B model fine-tuned on your domain out-performs a frontier 70B parameter model on specific extraction tasks while reducing inference cost by 85%. Our comparisons provide clear empirical evidence to guide model selection and routing tiering.
| Evaluation Dimension | Model Alpha (Frontier) | Model Beta (Mid-Tier) | Model Gamma (Fine-Tuned) |
|---|---|---|---|
| Domain Task Accuracy | 88.4% | 81.2% | 92.6% (Highest) |
| RAG Groundedness | 91.8% | 86.0% | 93.2% |
| Coding Pass@1 | 74.2% (Highest) | 59.1% | 68.0% |
| Latency (p95) | 1,640ms | 920ms | 340ms (Fastest) |
| Est. Cost / 1,000 Tasks | $14.20 | $4.80 | $0.65 (Lowest) |
Compare Models on Your Workload
Make empirical, evidence-backed model architecture choices based on your actual data, latency requirements, and budget.