Executive Summary
A model that works well in a prototype can become harder to justify as usage grows. GenAI Protos benchmarks the client's existing model against selected hosted and open-weight alternatives on one real business workload, using the same representative inputs and agreed operating requirements. The result is evidence-based guidance on whether to keep the current model, test an alternative, or revisit the application design.
01Client Requirement
The engineering team needs to know whether the model selected during prototyping remains suitable for sustained use. Public benchmarks provide useful context, but they do not show how a model handles the client's task mix, response-time requirements, or expected traffic. Comparing token prices alone also leaves out failed requests, retries, and infrastructure costs.
- Which models meet the required task quality?
- Where do they make errors that could affect business decisions?
- How do response times change under expected load?
- What does a successfully completed task cost?
- Do the results hold on examples kept separate from development?
02Our Approach
We start by defining what a successful task looks like with the client's engineers and business reviewers, including acceptable outputs, the errors that matter most, and cases where the model should decline to answer. We assemble a representative test set covering routine requests, difficult inputs, and questions that cannot be answered from the available information. A separate holdout set stays unused during prompt development and configuration changes.
The current model provides the baseline. Selected alternatives run through the same evaluation harness, with shared baseline prompts, consistent scoring, matched concurrency levels, and agreed retry and timeout rules. Model versions, serving configurations, and any necessary differences are documented.
Cost calculations include failed attempts and retries. For open-weight models, we document hosting, utilisation, and operating-cost assumptions so self-hosted inference is never treated as free. We then map the quality, latency, and cost frontier: the options where improving one measure means accepting a trade-off elsewhere. Final candidates are checked against the hidden holdout before any recommendation is made.
- Task success and high-risk error rates
- Median and p95 end-to-end latency
- Retries, timeouts, and throughput
- Cost per successful task
- Sensitivity to input length, task mix, and request volume
03Outcome
The outcome is a shortlist of models tied to the client's actual workload and operating limits. This gives the engineering team a basis for model selection and capacity planning. The findings may support a change, or show that the current model remains the most practical choice. Any savings or performance improvements come only from measured results.
- A reusable benchmark with test cases, configurations, and evaluation code
- A comparison of quality, latency, reliability, and cost
- Examples of important failures and the conditions that trigger them
- Holdout results and remaining uncertainties
- A recommendation on which options warrant further testing and when to repeat the benchmark