How to Actually Choose an AI Model
Eight models, all claiming to be the best at everything. Here's the framework we built to cut through the noise — and the free tool that runs it in four questions.
There’s a pattern that plays out every time a new AI model ships. The announcement says it’s better at coding, better at reasoning, better at creative work, better across every benchmark. The pricing page says it’s affordable. The leaderboard puts it in the top three.
And then you have to pick one.
In 2024 there were three or four serious options. In 2026 there are eight, and counting. The benchmarks are gamed, the pricing keeps changing, and “frontier model” now means something different depending on who’s publishing the marketing copy.
We built a tool to solve this for ourselves. It turned out to be useful enough to share.
The real question isn’t “which model is best”
Every model comparison article starts from the wrong premise. Best is not a fixed property of a model — it’s a relationship between the model and the task.
Claude Opus is extraordinary at nuanced reasoning, long-document synthesis, and anything that requires careful, patient thinking. It’s also the most expensive model we track, by a significant margin. For a task that runs once a day on a piece of long-form content, that cost is irrelevant. For a task that runs ten thousand times a day on short text inputs, it’s the wrong choice by an order of magnitude.
GPT-4o is fast and vision-capable and has a mature ecosystem. Gemini 2.5 Pro has a context window that makes the others look like sticky notes. Llama 4 Maverick runs open-source and can be self-hosted. DeepSeek V3 costs a fraction of the frontier models and punches well above its price point for coding work.
None of these is the right answer. All of them are, in the right situation.
Four questions that actually narrow it down
When we looked at how we were making model selection decisions internally, it came down to four things:
What kind of task is it? Coding, writing, reasoning, data extraction, vision — these map differently to different models. A model that excels at code generation may be mediocre at synthesizing a research document. The task type is the first filter.
How long is the context? If you’re feeding a full codebase or a long legal document, most models drop out immediately. Gemini 2.5 Pro’s million-token context window exists for a reason. If your context is a short instruction and a user query, context window size is irrelevant.
How sensitive is the cost? Running AI in production has a fundamentally different cost profile than running it ad hoc. A model that costs $15 per million input tokens is reasonable for a weekly research task and unreasonable for real-time API responses serving a consumer product.
Do you need to self-host? Privacy requirements, air-gapped environments, and compliance constraints often make cloud API access impossible. This immediately narrows the field to open-source models that can be run locally.
Four questions. Most combinations of answers point clearly to one or two models. The remaining ambiguity is where personal preference and ecosystem familiarity reasonably apply.
What we built
The tool at tools.modologystudios.com/which-model runs this logic in the browser with no backend. You answer four questions — task type, context size, cost sensitivity, self-host requirement — and get a specific recommendation with the reasoning behind it.
It covers Claude Opus 4.6, Claude Sonnet 4.6, Claude Haiku 4.5, GPT-4o, GPT-4o mini, Gemini 2.5 Pro, Llama 4 Maverick, and DeepSeek V3. We update the model list and pricing when the landscape changes.
No account required. Nothing stored. The whole thing is a decision tree running in 300 lines of JavaScript.
What it doesn’t do
It doesn’t tell you which model will produce the best output for your specific prompt. That requires testing. It doesn’t account for latency, geographic availability, rate limits, or integration complexity — all of which are real factors in production.
What it does is reduce the decision from “evaluate eight complex systems across dozens of dimensions” to “answer four questions.” That’s enough to stop most of the paralysis.
If you find yourself reaching for GPT-4o by default on everything because it’s familiar, run through the questions. You might find you’re paying twice what you need to for a task that Haiku handles just as well. Or you might confirm that GPT-4o was the right call and stop second-guessing it.
Either way, the decision is made deliberately instead of by inertia.