Does it recommend a specific model name?

It recommends a tier — frontier, mid-tier, small/mini, or self-hosted open weights — and names current representative models in each. Tiers are more durable than exact model names, which change every few months, so the guidance stays useful.

How does privacy affect the recommendation?

A strict privacy or data-residency requirement pushes the wizard toward self-hosted open-weight models or providers with zero-retention enterprise terms, even if a hosted frontier model would otherwise win on accuracy.

Why does latency matter so much?

Frontier reasoning models are accurate but slow and expensive per call. If you need sub-second responses for an interactive product, the wizard favors smaller, faster models and suggests reserving frontier calls for hard sub-tasks.

Is the recommendation a guarantee?

No. It is a strong starting point based on common trade-offs. Always confirm with your own evaluation set, because real performance depends on your exact prompts, data, and quality bar.

What if cost and accuracy conflict?

The wizard weighs your stated ceiling against your accuracy requirement and explains the tension explicitly, often suggesting a routing strategy — a cheap model for easy cases, an escalation to a frontier model for hard ones.

What is the AI Model Selection Wizard?

A guided wizard covering task type, accuracy needs, latency budget, privacy level, monthly volume, and cost ceiling that recommends an LLM tier with clear pros, cons, and alternatives so you pick the right model without guesswork. It runs free in your browser on Gera Tools, with nothing uploaded.

AI Model Selection Wizard

Name: AI Model Selection Wizard
Creator: Gera Tools
License: https://creativecommons.org/licenses/by/4.0/

AI model selection wizard

Choosing an LLM is a multi-variable trade-off: accuracy versus cost, speed versus capability, hosted convenience versus data control. This wizard asks seven focused questions and scores the main model tiers against your answers, then recommends the best fit with the reasoning behind it — so you stop guessing and start from a defensible choice you can validate on your own data.

How it works

You answer questions about task type, required accuracy, latency budget, privacy needs, monthly call volume, and your cost ceiling. The wizard applies a weighted scoring model: hard constraints like strict privacy or tight latency can veto a tier outright, while soft preferences shift the balance. It then surfaces the winning tier, the runner-up, and a clear explanation of the trade-offs. All scoring runs locally in your browser; nothing is sent anywhere.

The four model tiers

Rather than recommending a specific model name that may be superseded next month, the wizard works with four durable tiers:

Frontier — the most capable hosted models from major providers. Highest accuracy on complex reasoning, coding, and nuanced writing. Slowest and most expensive per call. Appropriate for tasks where quality is the only constraint and latency is acceptable.

Mid-tier — recent capable models that balance quality with cost and speed. Often the right default for business applications where good (not necessarily best-in-class) accuracy is sufficient and you serve many users.

Small / mini — compact hosted models optimised for speed and low cost. Work well for classification, structured extraction, summarisation of short texts, and high-volume tasks where the accuracy bar is moderate.

Self-hosted open weights — models like Llama 3 you run on your own infrastructure. Introduce operational overhead but give you full data control, no per-call cost at inference time, and the ability to fine-tune on proprietary data.

Worked example

Say you are building a document Q&A feature for a legal firm. You need high accuracy on complex reasoning (high requirement), responses in under 5 seconds (moderate latency), strict data residency that disallows third-party cloud processing (hard constraint), and you expect about 10,000 calls per month. The wizard would likely veto hosted frontier and mid-tier models on the privacy constraint, then weigh self-hosted open weights against a compliant private-cloud offering — surfacing the self-hosted route with a note on the operational overhead and the compliant private-cloud option as a runner-up.

Tips and notes

Tiers beat names. Exact model names churn; the frontier / mid / mini / open-weight framing stays stable, so map the recommendation to whatever is current at your provider.
Privacy and latency are vetoes. When either is strict, accept that the very top accuracy tier may be off the table.
Consider routing. If cost and accuracy conflict, run a cheap model first and escalate only the hard cases — often cheaper than one big model everywhere.
Always evaluate. Treat the recommendation as a hypothesis and confirm it on a real eval set before committing production traffic.
Revisit when your volume changes. A cost ceiling that makes sense at 1,000 calls per month may unlock a different tier at 1,000,000.