AI testing & review · United States
Build custom benchmarks to compare frontier models on performance, cost, and time efficiency for your specific tasks
Ahrefs Domain Rating set in type 2 Nov 2026, reset 2 Nov 2026.
A benchmarking platform that lets you create custom evaluations using your own tasks and data, then run frontier models against them to compare performance
Optima is a custom benchmarking platform that enables organizations to build their own evaluation frameworks tailored to their specific use cases. Unlike standardized benchmarks that measure general model capability, Optima lets you upload your own tasks, datasets, or agent trajectories and run them against frontier models to get performance results with detailed cost and time per task comparisons.
The platform supports multiple evaluation types including Q&A tasks with objective grading, document-based questions, agentic task completion with tools, and interactive conversations. You can grade results using custom rubrics or Artificial Analysis's panel of judges from major evaluations.
Optima handles the entire workflow: you describe your context and provide examples, the build agent helps draft tasks and rubrics with you, you select your evaluation type and models to run, and finally you receive graded results showing how each model performed against your criteria. Pricing is based on token usage for running benchmarks and per-criterion or per-match costs for grading, with no markup on raw model token costs.
Token usage for creating and running benchmarks at raw model cost. Rubric grading: $0.002 per criterion per model (standard judges) or $0.040 per criterion (premium judges), $0.10 on agentic tasks. Pairwise grading: $0.006 per match (standard, $0.150 premium), $0.015 per match on document tasks (sta
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Measured after Optima went live on Zivaro Launch on 1 November 2026.
Day 1 since launch. Next receipt: day 30, 1 Dec 2026. These are measurements, not proof of cause: many things move these numbers. Figures we have not measured are left out.
Optima supports Q&A with objective grading, document input where models answer questions about uploaded files, agentic tasks where models complete deliverables using tools, interaction simulations with different user types, and rubric-only grading.
Creating and running benchmarks is charged at the raw token cost of the models used with no markup. Grading is priced per criterion for rubric grading ($0.002 standard, $0.040 premium, $0.10 for agentic) or per match for pairwise grading ($0.006-$0.045 standard, $0.150 premium depending on task type).
Yes, your own agent can compete in the same benchmark run as frontier models through an HTTP interface, allowing you to compare it directly against other models.
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