Top Super Intelligence Models: 10 Frontier AI Profiles

A shortlist you can actually compare

These profiles follow the current overall ranks, rather than treating a famous brand or a new release as evidence of superiority. Each card identifies the strongest measured pillars within that model, lists the catalog facts available today, and links to the underlying results. “Strongest” compares the model’s own pillar scores; it does not mean that it wins that pillar against every competitor. The SI Score beside each profile is adjusted for evidence support.

The top ten are rank-eligible models, not every model with a score. A provisional score remains outside this shortlist until the evidence meets the published ranking rule. There may be strong models outside the ten, including specialized systems with only one reported capability. Treat these cards as a broad frontier comparison and use task rankings when you have a specific job to accomplish.

Read price and context together

API prices are per million tokens, split into input and output. An inexpensive input rate can coexist with an expensive output rate. Long conversations, retrieved documents, tool outputs and generated code change the balance. Compare both rates, then calculate a bill using your expected token mix. Prices do not include your application’s infrastructure, and a missing figure should be treated as an unresolved cost rather than a free service.

The context window is an advertised limit, not a test of reliable recall across that entire window. A large window can accommodate more material but does not prove that a model will find the relevant detail or follow every constraint. For document-heavy workflows, test the actual document length and placement of critical information. The long-context board orders the known limits; the linked source establishes what each limit means.

Evidence before commitment

Downloadable weights can make local hosting or customization possible, while licenses and hardware determine whether those options fit your use case. Confidence tells you how much expected benchmark-source weight has arrived, not whether a model is safe or suitable for a particular deployment. Open each profile before choosing: result notes retain the benchmark variant, the source and the retrieval date. The comparison pages put shared tests beside each other so a difference is easier to inspect.