rankedagi.com

Treat RankedAGI as a comparison and exploration tool, not an authoritative source. The page itself displays "Simulated Data" labels, which means at least some of the scores and rankings shown are generated for demonstration rather than pulled from official benchmark leaderboards. You can use it to get a quick sense of how models are positioned across many benchmarks, but before citing any number in a decision, verify it against the original benchmark source.

What the page actually shows

RankedAGI presents a large table of AI models ranked by a composite "RankedAGI" score, with columns spanning coding, reasoning, agentic, and multimodal benchmarks. From the visible data, the table includes:

• Model info: size, version, release date, context length, knowledge cutoff, license, and organization
• Benchmark scores: SWE-Bench (Pro, Multilingual, Verified), Terminal Bench 2.1/2.0, BrowseComp, Code Arena, OSWorld Verified, Svelte Bench, Cyber Gym, HLE (with and without tools), GPQA Diamond, Text Arena, AIME 2026, Vending Bench 2, NYT Connections, MMMU / MMMU Pro, ARC AGI 2.0, τ2-Bench Telecom, GDPval, DesignArena, Cursor Bench, and more
• Cost and context: input cost per million tokens, output cost per million tokens, context length

The ranking column assigns a rank number (#1, #2, etc.) to each score, so you can see both the raw percentage and where it places among the listed models.

Why the "Simulated Data" label matters

The page evidence shows repeated "Simulated Data" and "Simulated" markers, along with a "Show Simulated Data" control. This is the single most important signal for reliability:

• Simulated data is not measured data. A simulated score may be interpolated, estimated, or invented to fill a table. It does not necessarily reflect a model's actual performance on the real benchmark.
• Rankings built on simulated scores inherit that uncertainty. A #1 rank is only meaningful if the underlying score is real and comparable across models.
• The site does not state its data sources, update frequency, or whether scores are officially verified. Without that, you cannot assume the numbers match the benchmark maintainers' published results.

The model names in the table (for example, "Claude Fable 5," "GPT 5.6 Sol," "Gemini 3 Deep Think") also suggest a forward-looking or hypothetical dataset rather than a snapshot of currently released models. That reinforces the need to check whether a given row corresponds to a real, shipped model.

How to use it responsibly

If you want to compare models and the stakes are low, RankedAGI is a convenient way to scan many benchmarks in one view. If the stakes are higher, follow this sequence:

1. Identify the benchmark you care about (for example, SWE-Bench Verified for coding, GPQA Diamond for science reasoning).
2. Note the score and rank shown on RankedAGI.
3. Go to the benchmark's official leaderboard or the model provider's own report and confirm the number.
4. Check the model version and date. A score for "GPT 5.4" is not the same as "GPT 5.4 Pro," and a June 2026 result may not reflect a later release.
5. Only then use the number in a comparison, procurement note, or article.

Quick reliability checklist

Bottom line

RankedAGI is useful for orientation and side-by-side scanning, but its own page labels data as simulated and does not document sources or update cadence. Use it to form hypotheses about which models lead on which benchmarks, then confirm every number you intend to rely on against the original benchmark or the model provider. If you cannot trace a score to an official source, do not treat it as a fact.

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.

RankedAGI groups its rankings into four capability areas: Code, Agentic, Reason, and Text Arena. Each area contains several individual benchmarks, and models are ranked both within each benchmark (with a numeric rank like #1, #2) and across the category. The site also tracks metadata for every model — size, version, release date, context length, knowledge cutoff, license, and input/output cost per million tokens — so a ranking can be read alongside the conditions that produced it.

Code benchmarks

These measure a model's ability to write, edit, and reason about software.

In the sample data, Claude Fable 5 leads SWE Bench Pro at 94.2% (#1), while Claude Opus 5 leads SWE Bench Multilingual at 94.2% (#1) — showing that the top spot can differ by benchmark even within one category.

Agentic benchmarks

Agentic scores reflect how well a model operates as an autonomous agent — browsing, using tools, and completing multi-step tasks.

• RankedAGI Browse Comp — web browsing and information retrieval
• OSWorld Verified — controlling a desktop operating system
• τ2-Bench Telecom — task completion in a telecom domain scenario
• Vending Bench 2 — a simulated transactional environment
• GDPval AA and GDPval AA v2 — economic-value-oriented task evaluation

Reasoning benchmarks

These test abstract problem-solving rather than task execution.

• HLE and HLE w/ Tools — Humanity's Last Exam, with and without tool access
• GPQA Diamond — graduate-level science questions
• AIME 2026 — competition mathematics
• ARC AGI 2.0 — abstract pattern reasoning
• MMMU, MMMU Pro, and MMMU Pro w/ Tools — multimodal reasoning
• NYT Connections — word-association puzzle solving

The "w/ Tools" variants matter: a model's HLE or MMMU Pro score can change substantially depending on whether it may call external tools, so compare like with like.

Text Arena

Text Arena is a separate category for general text-generation quality, typically derived from head-to-head human or model comparisons rather than a fixed answer key.

How to read a ranking

1. Pick the category that matches your use case (code, agentic work, reasoning, or general text).
2. Within that category, choose the benchmark closest to your actual task — SWE Bench Verified for bug-fixing, Terminal Bench for shell work, GPQA Diamond for science Q&A.
3. Check the model's metadata row (context length, cutoff, cost, license) before treating a high score as usable for you.
4. Note that the site labels some entries as Simulated or Preview data, and the interface includes a "Show Simulated Data" toggle — treat simulated figures as illustrative rather than measured.

Because ranks are per-benchmark, a model can be #1 in one column and outside the top ten in another. The category view is a convenience grouping, not a single composite score.

RankedAGI (rankedagi.com) is a benchmark aggregator that puts many AI models side by side across coding, reasoning, agentic, and multimodal tests, then lets you sort and filter them by cost, context length, license, and organization. Use it when you need a fast, cross-model view of who leads on a specific task — for example, picking a model for a coding agent versus one for long-context reasoning. Treat its numbers as a starting point for shortlisting, not as a final verdict, because the site itself flags parts of its data as simulated.

Start From the Task, Not the Overall Ranking

The site organizes benchmarks into groups, so the first decision is which group matches your job:

If your task is coding, read the Code group first; if it is research or analysis, the Reason group is more informative. A model that tops one group is often not the leader in another — the sample data shows different models holding #1 in SWEBench Verified versus HLE versus Text Arena.

Filter by Real Constraints Before You Fall in Love With a Rank

Rankings ignore your budget and infrastructure. The site exposes columns that let you filter on practical limits:

• Input / Output Cost per million tokens — shown per model (for example, $10 / $50 for one flagship, $5 / $25 for another). Filter to your cost ceiling first, then compare ranks within that set.
• Context Length — shown in tokens (e.g., 1M). If you feed long documents or codebases, drop models below your required window.
• License — Proprietary vs. open. If you need self-hosting or redistribution, filter to open licenses.
• Org. — Anthropic, OpenAI, Google, and others. Useful when you want to compare versions within one vendor or avoid vendor lock-in.
• Size / Version / Released / Cutoff Knowledge — helps you tell a newer model from an older one with a similar name.

A practical order: set cost and context filters → pick the benchmark group for your task → sort by that benchmark → shortlist 2–3 models → check license and release date.

Compare Versions and Vendors Deliberately

The model list mixes many versions of the same family (multiple Claude, GPT, Gemini, Qwen, and GLM entries). To compare meaningfully:

1. Sort by the benchmark that matches your task, not by name.
2. Note the Released date and Version so you are not comparing a preview against a stable release.
3. Compare within an org to see whether an upgrade actually improves your target benchmark, and across orgs to see the best available option.
4. Watch the #rank next to each score — the site shows both the raw percentage and its rank, so a 90% score can still be #3 if the field is strong.

For example, if two models from the same vendor differ mainly in context length and cost but score within a point on your benchmark, the cheaper one is usually the better default.

Watch for the "Simulated Data" Label

The page includes explicit "Simulated Data" and "Simulated" markers, and a "Show Simulated Data" control. That is your signal that some entries are illustrative rather than verified results.

• Before you commit to a model, cross-check the headline number against the benchmark's official leaderboard (SWEBench, LiveBench, LMSYS, etc.).
• Prefer models whose scores you can trace to a primary source.
• Treat close rank gaps (a point or two) as ties, since simulated or rounded data cannot resolve them.

A Quick Decision Recipe

1. Name your task and pick the matching benchmark group.
2. Apply cost, context, and license filters.
3. Sort by the one or two benchmarks closest to your task.
4. Shortlist 2–3 models and verify their scores on the official benchmark site.
5. Test the shortlist on your own data before standardizing on one.

RankedAGI is most useful as a wide-angle lens for narrowing the field; the final choice still depends on your budget, latency needs, and a hands-on trial.

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.