What Model Information and Metrics Can I Compare on RankedAGI?

RankedAGI presents each AI model as a row in a large comparison table, so you can line up dozens of models against the same set of attributes at once. For every model you get two kinds of data: descriptive metadata (size, version, release date, context length, knowledge cutoff, license, organization) and performance/cost metrics (benchmark scores with ranks, plus input and output cost per million tokens). You can filter and sort by model size, by individual benchmarks, and by cost to narrow the field to what matters for your use case.

Model metadata you can compare

Each model entry carries a consistent set of descriptive fields, which makes side-by-side comparison straightforward:

• Size — parameter scale where disclosed (for example, "1.6T" for DeepSeek V4 Pro, "754B" for GLM 5.1, "284B" for DeepSeek V4 Flash, "27B" for Qwen 3.6).
• Version — the specific release label (e.g., Claude Opus 5, GPT 5.5 Pro, Gemini 3 Pro).
• Released — the release or preview date (e.g., "Latest Jun 9, 2026," "Preview Apr 7, 2026").
• Context length — the maximum context window (e.g., 1M for several frontier models).
• Cutoff knowledge — the model's training knowledge cutoff.
• License — for example, Proprietary.
• Org. — the developing organization (Anthropic, OpenAI, and others).

Because these fields are uniform across rows, you can scan a column to see, say, which models offer a 1M context window or which are proprietary versus otherwise licensed.

Benchmark scores and ranks

The core of the comparison is the benchmark grid. RankedAGI lists a large set of benchmarks and shows each model's score alongside a rank (like #1, #2) so you can see both absolute performance and relative standing.

Benchmark categories visible on the site include:

A single model row can show many scores at once — for example, a top-ranked model might display 94.2% (#1) on one benchmark, 88.0% (#2) on another, and 92.9% (#2) on a third. This lets you judge whether a model is broadly strong or specialized in one area like coding versus reasoning.

Cost metrics

Cost is shown as two separate figures per model:

• Input Cost /M — price per million input tokens.
• Output Cost /M — price per million output tokens.

These appear as concrete values (for example, $10 / $50, $5 / $25, or $25 / $125), so you can weigh a model's benchmark standing against its price. A model with a slightly lower rank but much lower cost may be the better fit depending on your budget and workload.

How to filter and sort

The table is built for narrowing down, not just browsing:

• Select all / Reset controls let you toggle which models are included.
• You can filter by model size, by benchmark, and by cost.
• Columns can be used to sort models by a given benchmark, by cost, or by other attributes.

The practical workflow: pick the benchmark that matches your task (e.g., SWEBench Verified for coding, GPQA Diamond for reasoning), sort by that column, then check the cost and context-length columns for the top candidates before deciding.

A note on the data

The site's interface references "Simulated Data" and "Simulated" in its display, and the model list includes entries that read as previews or placeholders (e.g., "Preview" dates, "Contemplating" status). Treat the numbers as a comparison aid rather than a guaranteed production spec, and verify any critical figure against the model provider's own documentation before relying on it.