Which AI model should you use?
Tell us about your use case and we'll recommend the top 3 models — with cost estimates and rationale.
How this calculator works
This calculator estimates a typical monthly token profile for your use case (chatbot, coding, data extraction, summarization, analysis, or creative writing — each has a different realistic mix of input versus output tokens) and prices every model in the database against that profile, using the current prices shown in the calculator. It then applies a weighted score built from four factors: raw capability for your use case, whether the model's context window and required features (vision, function calling, JSON mode) are actually supported, latency tier, and cost relative to your stated budget.
How heavily each factor counts depends on the priority you choose. A cost priority weights price most heavily; a quality priority weights capability most heavily; balanced splits the difference. Models missing a feature you marked as required take a real penalty rather than being silently ranked lower, and going over your stated budget subtracts points too, so the top result should genuinely fit what you asked for rather than just being the most capable model in the database.
How to use it
- 1.Select your primary use case — it determines the token profile the cost estimate is based on.
- 2.Set your priority: cost, balanced, or quality — this changes how the scoring weighs price against capability.
- 3.Enter your approximate monthly budget so the calculator can flag models that would go over it.
- 4.Choose the context window you need (short, medium, long) based on your typical prompt and document size.
- 5.Select any required features (vision, function calling, JSON mode), then review the ranked recommendations and their per-model rationale.
Common mistakes
- ⚠Choosing the wrong use case — the cost estimate is built from a token profile specific to each use case — picking "summarization" when you're really building a chatbot will skew the estimated monthly cost.
- ⚠Skipping required features — if your app needs vision or function calling, mark it — otherwise a model that can't actually support your integration may still rank highly on cost and capability alone.
- ⚠Setting priority to "quality" out of caution — this removes most of the weight on cost from the score, which can recommend a premium model you don't actually need for the task at hand.
- ⚠Underestimating context window needs — if your prompts include long documents, chat history, or retrieved context, select "long" — a model with too small a window will truncate input in production regardless of how well it scored.
Frequently asked questions
How is the monthly cost estimate calculated?▾
Each use case has a typical monthly input/output token profile built in. The calculator prices that profile against every model's current input and output rates, shown in the comparison, rather than asking you to estimate token counts yourself.
What does the priority setting actually change?▾
It changes how heavily cost, capability, and latency count toward each model's final score. "Cost" weights price most heavily, "quality" weights raw capability most heavily, and "balanced" splits the difference — the same models can rank differently depending on which you pick.
What happens if a model is missing a feature I need?▾
Marking a feature as required (vision, function calling, JSON mode) applies a real penalty to any model that doesn't support it, so models lacking a hard requirement should rank lower even if they otherwise score well on cost or capability.
Why did a model outside my budget still appear in results?▾
Going over budget lowers a model's score but doesn't remove it from the list — this lets you see close alternatives just above your budget in case a small increase is worth the capability gain.
How is this different from the Model Advisor calculator?▾
Both rank models against your requirements, but this one is built around a use-case cost profile and a numeric budget, while the Model Advisor uses a five-question flow focused on task type, latency, quality bar, and context length. Try both if you want a second opinion.