What Is RankedAGI and What Does It Offer?

RankedAGI (rankedagi.com) is a comparison site that ranks AI models by their latest benchmark scores. It aggregates results across many benchmarks — including SWE Bench Verified, Codeforces, LMSYS, and LiveBench — into a single sortable table, so you can compare models on performance, cost, context length, and other attributes side by side. It is aimed at developers, researchers, and anyone choosing between AI models who wants benchmark-based evidence rather than vendor marketing.

What RankedAGI actually shows

The core of the site is a large ranking table. Each row is a model, and each column is a metric. Based on the site's own data, the table covers:

• Model identity: name, size, version, release date, context length, knowledge cutoff, license, and organization.
• Benchmark scores: percentages or Elo-style scores for dozens of benchmarks, with a rank (e.g. #1, #2) next to each result.
• Cost: input cost per million tokens and output cost per million tokens.
• A composite "RankedAGI" score: an aggregate ranking column that orders models overall.

The site lists 210 models in its table, spanning organizations such as Anthropic, OpenAI, Google, and others.

Benchmarks covered

The benchmarks are grouped into categories. From the site's column set:

Each benchmark cell shows a score and a rank, so you can see both absolute performance and how a model places against the field.

How to read the rankings

A few things to keep in mind when using the table:

• Ranks are per-benchmark, not global. A model ranked #1 on one benchmark may sit far lower on another. The composite RankedAGI column is the only overall ordering.
• Scores are not directly comparable across benchmark types. A 94.2% on one benchmark and an Elo of 1654 on another measure different things; compare within a column, not across columns.
• Cost is per million tokens. Input and output prices are listed separately, so a model with cheap input can still be expensive to run if output dominates your workload.
• Context length and cutoff matter for fit. A high-scoring model with a short context window or an old knowledge cutoff may not suit your task regardless of its rank.

Who it is for

RankedAGI is most useful if you are:

• Selecting a model for a project and want to weigh benchmark performance against cost and context length.
• Comparing vendors on the same benchmarks rather than relying on individual announcements.
• Tracking model releases — the table includes release dates and version numbers, so you can see how recently a model was added.

It is less suited to tasks that need a single definitive "best model" answer, because the right choice depends on which benchmarks match your use case.

Practical example

Suppose you need a model for a coding agent with a large codebase in context. You would:

1. Sort or scan the SWE Bench Verified and Agentic columns for high scores.
2. Filter to models with a context length large enough for your codebase (the table shows values like 1M tokens).
3. Check input/output cost to estimate running expense.
4. Confirm the license and organization meet your constraints.

The result is a shortlist backed by benchmark data, which you can then test directly.

Caveats

The site's page includes labels such as "Simulated" and "Simulated Data," and a "Show Simulated Data" control, indicating some displayed values may be simulated rather than measured. Treat the numbers as a starting point for comparison and verify against primary benchmark sources before making a decision. The site does not state pricing for its own use, so no assumption should be made about whether access is free or requires login.