MODEL SELECTION
Find a cheaper AI model
Find a cheaper AI model with comparable capability, then compare token costs, context length, and open-weight availability.
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Models with a compiled capability score and a positive blended token cost. With a reference model selected, the shared better, cheaper, and open-weight filters apply here too; the reference stays visible as an anchor. Lab colours are assigned by model-ID vendor prefix; circles are open weight and diamonds are closed weight; the selected reference has an orange halo. The dashed line marks the efficient frontier: no cheaper model has equal or higher capability. Tap a point for details; click a point on desktop to use it as the reference model.
Compare AI models by capability and cost
Model Finder helps you identify lower-cost alternatives to an AI model you already use. Select a reference model to see nearby options ranked by measured capability, then compare their input and output token prices, context length, and open-weight availability.
Capability is a weighted view of available intelligence, coding, agentic, reasoning, and tool-use evidence. Choose a workload profile—or set your own weights—to reflect the work you actually need done. Pricing is blended from the input and output mix for that profile, so a model that is cheap for short prompts does not automatically look inexpensive for output-heavy workloads.
Use the open-weight filter and parameter limit when deployment control matters. Results are intended for narrowing a shortlist, not replacing task-specific evaluation: benchmark coverage, latency, provider behavior, context usage, and model updates can all change the practical result.
Model and pricing data are generated from documented public sources and refreshed periodically. Review the methodology and validate shortlisted models against your own workload.
Frequently asked questions
How do I find a cheaper alternative to an AI model?
Search for the model you use, select it as the reference, and sort or filter the results by capability distance and blended cost per million tokens. The comparison page lets you inspect several candidates side by side.
How is comparable capability measured?
Model Finder combines normalized public benchmark evidence into capability facets. The selected profile determines how strongly each facet contributes to the comparison, and models without sufficient evidence are not treated as equivalent.
Does the cheapest model always provide the best value?
No. Value depends on the capability required and on whether your workload is input-heavy or output-heavy. A lower token price can be a poor trade if the model is too far below the capability needed for the task.
What does open weight mean?
Open-weight models make their trained weights available under a published license. That can enable self-hosting and greater deployment control, but the license, hardware requirements, and operational cost should still be evaluated separately.
Popular AI model comparisons
Start with a curated side-by-side comparison, then adjust the selection and workload profile for your use case.