You're spending on AI APIs but you don't actually know if you're using the right model. Most teams default to GPT-4 or whatever their developer tried first. That's leaving performance, cost, and accuracy on the table.

We run structured, production-grade evaluations across OpenAI, Gemini, Claude, Mistral, DeepSeek, and other models to find the exact right fit for each feature in your product. Not the most expensive model. Not the trendiest. The one that actually performs best for your specific use case.

We use the AI Iron Triangle (Fast, Cheap, Good): every AI task requires trade-offs between speed, token cost, and reasoning depth. We formally score models against this triangle for each feature, so you're never overpaying for intelligence you don't need or under-investing where accuracy matters.

For subjective quality tasks, we run Collaborative Self-Assessment: models evaluate each other's outputs on creativity, depth, clarity, engagement, and self-awareness, scored numerically. This surfaces strengths you'd miss in a single-model test.

Switching from GPT-4 to GPT-4o-mini cut costs by 100x and reduced latency from 86 seconds to 5 seconds, with no loss in conversational quality

A "Nano" model confidently categorized "Plan my wedding" as Low Priority, proving that cheaper models fail silently on nuanced reasoning

GPT-5.1 returned fabricated ingredient lists and fake image URLs in a health app, forcing a revert to the older model to maintain user trust

Gemini's Audio Foundation API processed voice correction in a single step where competitors required multi-step pipelines

Each model has a distinct personality: GPT excels at creative synergy, Claude at empathy and narrative, DeepSeek at multi-perspective analysis, Gemini at analytical precision, Mistral at concise practical clarity

As an accepted member of the Contra Labs network, we apply the same structured evaluation methodology used in frontier AI research to your product. The Contra Labs network partners with leading AI labs and product companies to benchmark models across design, video, development, and creative domains.

Any team running AI in production (or about to). SaaS products with AI features. Mobile apps using LLM APIs. Startups choosing their first model stack. Enterprises evaluating whether to switch providers. If you're paying for AI tokens and haven't formally benchmarked your options, you're probably overspending.

A comprehensive evaluation report covering every AI-powered feature in your product, with model-by-model scoring, cost projections, latency benchmarks, accuracy testing against ground-truth data sets, and a concrete recommendation for which model to use where. Plus a vendor-agnostic architecture so you can swap models as the landscape shifts.