analiza aiqbench
This article outlines a practical framework for selecting AI models based on specific task requirements, cost-per-task metrics, and latency constraints rather than generic leaderboards. It emphasizes integrating live benchmark data and pricing directly into the development workflow via MCP servers to ensure optimal model selection for distinct use cases like coding or document extraction.
How to Choose the Best AI Model (Live, in Your Editor)
There's no single best AI model, only the best model for a given task, budget, and moment. This article describes a six-step framework for choosing one, and how to run every step from your editor through the OpenRouter MCP server.
To choose an AI model, define the task, shortlist candidates from live usage and benchmark data, compare price and latency across providers, then test the finalists on your own prompts. Judge them on cost per completed task rather than cost per token, and expect the answer to change as new models are released. We won’t name a single best model. Any name we printed would be out of date within a month, and the right model depends on what you’re building and what you’re willing to pay for correct results. This article describes the framework we use to answer the question, and how to run it without leaving your editor. Our MCP server connects your assistant to live usage rankings, third-party benchmarks, per-provider pricing, and a way to send test prompts to candidate models. Best means your task, your cost per completed task, and your latency budget. A leaderboard rank isn’t one of the three. Use benchmarks to build the shortlist and your own prompts to pick the winner. Ori Eval can write and run that comparison for you. Query live rankings and prices from your editor over MCP, then measure what each test call costs with get-generation. If no…
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