The Capability Radar maps LLM benchmark performance across all 11 task dimensions in a single visual profile. The shape shows where a model is strong, where it trails, and how two models compare — with a source-attributed benchmark table beneath every chart.
A capability radar — also called a radar chart or spider chart — is a data visualization that displays multivariate data across multiple axes, each representing a different dimension of performance. Each axis radiates from a central point, and values are plotted along each axis and connected to form a shape. That shape is the profile.
Radar charts are used wherever multidimensional comparison matters: athletic performance, product evaluation, risk assessment, and — increasingly — AI model selection. The human visual system recognizes patterns faster than it compares numbers.
Model Optimizer applies this format to LLM benchmark performance across 11 task dimensions — turning benchmark data into a visual capability profile for every evaluated model.
Benchmark tables tell you how a model scored. The Capability Radar shows you what that means.
Every model evaluated in Model Optimizer generates a visual benchmark profile across eleven task dimensions. That profile creates a capability shape — a fast, readable view of where each model is strong, where it trails, and how its performance varies across different kinds of AI work.
The shape is the insight. And when two models are displayed on the same radar, the shape becomes a decision.
The Capability Radar is more than a summary. It is a visual decision tool — especially when comparing two models.
A model that leads on analytical and factual task types but trails on creative or language-heavy dimensions produces a different shape than a model optimized for rewriting, content generation, or structured outputs. A narrow indentation on two or three dimensions can reveal more about operational suitability than a ranked list of scores alone.
The Capability Radar evaluates each model across the 11 task types in the Model Optimizer framework:
Each dimension is scaled to its benchmark score and plotted as a point on the radar. The resulting shape is the model's capability profile.
The Capability Radar supports direct two-model comparison. A primary model is displayed as one profile. A comparison model is overlaid on the same radar, making overlap and divergence immediately visible.
Benchmark tables require comparison. Radar profiles reveal it.
Dimensions where one model leads, dimensions where both models are closely matched, and dimensions where the gap is significant are visible at a glance. That makes cross-model capability evaluation faster, clearer, and more directly useful for model selection decisions.
Every Capability Radar is paired with a Benchmark Details table providing the source context behind the visual profile — task type, benchmark used, score, rank, percentile, and source attribution.
Every score on the radar has a traceable source in the table beneath it — a published benchmark or a Model Optimizer proprietary evaluation developed for task types where no widely adopted benchmark exists.
| Task type | Benchmark | Score | Rank | Percentile | Source |
|---|---|---|---|---|---|
| Reasoning | GPQA Diamond | 78.2 | #2 | 96th | GPQA |
| Code Generation | SWE-bench Verified | 65.4 | #3 | 91st | SWE-bench |
| QA Factual | SimpleQA | 88.1 | #1 | 98th | OpenAI |
| QA Analytical | MMLU-Pro | 84.7 | #2 | 94th | TIGER-Lab |
| Extraction | Model Optimizer Eval | 90.6 | #4 | 89th | Model Optimizer |
| Schema Mapping | Model Optimizer Eval | 79.3 | #5 | 82nd | Model Optimizer |
Illustrative example rows drawn from the 11 task types. Each live radar displays the full 11-dimension table with the source for every score.
For the full evaluation framework, see LLM Benchmarking Methodology.
The Capability Radar is the visual entry point into Model Optimizer's capability intelligence layer.
The radar shows you what a model can do. Model Fitness shows you whether it is the right model for the work you actually do.
Those are the two questions every model selection decision requires.
Model Optimizer evaluates 36+ models across the same 11 task types — every score traceable to a published benchmark or a Model Optimizer evaluation. Start free and see the capability profile of every model you run.
Get Started FreeThe Capability Radar is a visual benchmark tool that maps LLM performance across 11 task dimensions — Reasoning, Code Generation, QA Factual, QA Analytical, Classification, Extraction, Structured Routing, Creative Writing, Rewriting, Technical Writing, and Schema Mapping — in a single radar chart profile. The resulting shape makes model strengths, weaknesses, and capability gaps visible at a glance.
Yes. The Capability Radar supports direct two-model comparison. A primary model profile is displayed, and a second model is overlaid on the same radar. Overlap, divergence, and performance gaps across all 11 dimensions are immediately visible without parsing benchmark tables.
Every score on the Capability Radar traces to a specific benchmark source — either a published benchmark such as GPQA Diamond or MMLU-Pro, or a Model Optimizer proprietary evaluation developed for task types where no widely adopted published benchmark exists. The Benchmark Details table beneath every radar displays the source for each dimension.
Source attribution means every score displayed in Model Optimizer identifies its origin — the benchmark name, the organization responsible for it, and the evaluation year. Proprietary evaluations are clearly labeled as Model Optimizer evaluations. No score is presented without a traceable source.
Model Optimizer currently evaluates 36+ models across 11 task dimensions, and the field grows as new models are released. Each model is scored using the most credible available source for each task — a published benchmark where one exists, or a Model Optimizer proprietary evaluation where one does not.
The shape of a model's radar profile shows where it is strong, where it trails, and how its capabilities are distributed across task types. A model optimized for reasoning and code generation produces a different shape than one optimized for creative writing and rewriting. When two models are compared on the same radar, the overlap and divergence between their shapes makes the capability differences between them immediately visible — without requiring row-by-row benchmark comparison.