AI Model Comparison: Intelligence, Speed & Price

How to choose an AI model for your workload

Start with a constraint that can rule a model out: your access route, required tools, response time or budget. Only then compare capability. An inexpensive row is not useful if its endpoint lacks the feature your workflow needs; a stronger composite score does not establish that it handles your exact files or task better.

Use the metrics for different decisions

• Interactive work: consider time to the first usable answer and total completion time, not only output tok/s. First-token timing can include reasoning tokens rather than the first answer token. A fast stream can still have a long wait before useful text.
• Budget planning: the side-by-side “Blended 3:1” field is a fixed uncached mix: 75% input plus 25% output prices. It is not a subscription fee, your invoice, or the source’s current cache-weighted headline blend. Cache eligibility, reasoning output, tools and retries can change your bill. Enter your own mix in the API Cost Calculator.
• Cost per task: the displayed source field describes its benchmark workload. It is useful context, but your task can consume a different number of tokens or need a retry. Do not convert it directly into a promised cost for your document or coding project.
• Missing metrics: “Not provided” means this feed lacks that value. It does not mean zero ability, free usage or a model that has failed the test. Keep the Intelligence Index version and exact model variant aligned.

These distinctions follow the Artificial Analysis definitions; HiseHub’s fixed 3:1 planning mix is explicitly separate from its current blended-price methodology. Hosted endpoint speed is not a benchmark of your own GPU.

A practical five-task check before committing

Choose two or three candidates and five non-sensitive tasks that represent your actual work. Keep the input, tools and settings as comparable as possible. Decide the acceptance rules first: required facts present, runnable code or valid output format, no invented citations, and an answer you can use without a full rewrite. Record pass/fail, time and consumed tokens; preserve failed attempts rather than counting only the best answer.

A useful personal comparison is total spend across all attempts ÷ accepted results, alongside editing time and failure severity. This is a suggested evaluation method, not a claim that HiseHub has tested the listed models. Small differences in a composite leaderboard should not replace that evidence.

If you want to run an open-weight model locally, use the hardware-to-model finder to check memory separately. It answers a different question from this hosted benchmark table.