A Practical AI Model Comparison for Builders (2026)

Comparing AI models on vibes is how products end up 10x over budget. The comparison that holds up is cost per completed task, latency at your context size, and behaviour on your own eval cases, not leaderboard rank. This guide shows the arithmetic with real list prices, a worked example you can copy, and the three-tier routing pattern we use in client builds.

Why "which model is best" is the wrong question

Models are priced and shaped for different jobs. Every major provider now ships a tier ladder: a fast cheap model, a balanced workhorse, and a frontier model. Products that control their unit economics route each task to the cheapest tier that passes evals for it. The right question is: which tier does each of my workflows need?

The arithmetic that matters: cost per task

Per-token prices only mean something multiplied through your real usage. The formula: (input tokens × input price) + (output tokens × output price) = cost per task. Anthropic's published list prices at the time of writing: Claude Haiku 4.5 at $1/$5 per million input/output tokens, Claude Sonnet 4.5 at $3/$15, and Claude Opus 4.5 at $5/$25 (verify on the live pricing page). One concrete task: a support reply with 2,000 tokens of context in and 500 tokens out.

On Haiku: (2,000 × $1 + 500 × $5) / 1,000,000 ≈ $0.0045 per reply, about $4.50 per thousand replies. On Sonnet: ≈ $0.0135, or $13.50 per thousand. On Opus: ≈ $0.0225, or $22.50 per thousand. Same task, 5x spread. At 1,000 replies a day for a year, defaulting to the frontier tier instead of routing costs you thousands of dollars for quality your users may never notice on that task.

The same ladder exists at OpenAI and Google. The tier logic transfers even where the numbers differ. Token prices change often enough that any static table (including this one) is a snapshot: budget from the live pages.

The comparison dimensions beyond price

Latency: output tokens dominate response time, so a model that writes shorter answers often beats a lower-latency model that rambles. Cap output length before switching tiers. Context: a bigger window is not a strategy; retrieval that puts the right 5,000 tokens in front of the model beats stuffing 100,000 in, on both cost and accuracy. Tool-calling reliability: for agent work this beats benchmark scores, and it varies by task, so measure yours. Structured output: if your product parses model responses, test schema adherence.

How to run a comparison

Collect 30–50 real task cases from your product: actual inputs, with what a good output looks like. Run them through 2–3 candidate models at each relevant tier. Score with a rubric (or a frontier model as grader, spot-checked by a human). Compare pass rate, cost per task, and p95 latency, then route each workflow to the cheapest tier that passes. The whole exercise takes a day or two and tends to pay for itself in the first month of inference. We run this exact process inside client builds at Robust Devs, and model routing is one of the items in our $499 Tech Audit when we review an existing AI product.

Frequently asked questions

Should I fine-tune instead of paying for a bigger model?

Rarely as the first move. Exhaust prompting, retrieval, and routing before fine-tuning; they are cheaper, faster to iterate, and easier to undo. Fine-tuning earns its keep for narrow, high-volume, stable tasks where a fast-tier model plus tuning beats a balanced-tier model on cost.

How often should I re-evaluate my model choice?

On every provider release that touches your tiers, and quarterly regardless. Keep the eval suite from your first comparison; re-running it against a new model is an afternoon, not a project.

Do open-weight models belong in the comparison?

Yes, when data control or unit economics at scale demand them, but factor in the ops cost of serving them. For most funded startups pre-scale, hosted frontier tiers win on total cost.

What does model choice do to my MVP budget?

It is one of the five big drivers. The full breakdown with worked numbers is in our AI MVP cost guide.