Analyze the key factors that drive AI model rankings. Drivers represent the specific signals that most influence where a model lands in the leaderboard -- both positive boosters and negative detractors.
Unique Drivers
6
Models with Drivers
300
Top Positive Driver
Context Window
278 models
Top Negative Driver
Output Capacity
52 models
How often each driver appears across all ranked models, broken down by impact type.
| Driver | Total | Net Impact |
|---|---|---|
| Capabilities | 300 | +105 |
| Context Window | 282 | +278 |
| Recency | 267 | +216 |
| Output Capacity | 204 | +93 |
| Benchmarks | 112 | +74 |
| Pricing | 35 | -15 |
The top 6 most common positive-impact drivers that boost model rankings.
1.1M token context window
Released within the last month
Supports reasoning, vision, tools, JSON mode, web search, streaming
Up to 66K output tokens per request
$180.00/M output tokens
The top 5 most common negative-impact drivers that push model rankings down.
Up to 66K output tokens per request
Supports reasoning, vision, tools, JSON mode, web search, streaming
$180.00/M output tokens
Released within the last month
Top 20 models by score with their individual driver breakdown.
Drivers grouped by their underlying signal category, showing the distribution of positive, negative, and neutral impacts.
| Signal | Count |
|---|---|
| capability | 300 |
| context_window | 282 |
| recency | 267 |
| output_capacity | 204 |
| benchmark | 112 |
| pricing_tier | 35 |
How driver analysis works.
Drivers are the specific factors that most influence a model's position in the leaderboard. Each driver captures a distinct aspect of model quality, pricing, capabilities, or market performance that contributes to the composite ranking score.
Drivers are derived from the scoring algorithm that evaluates models across multiple dimensions. The algorithm identifies which signals have the greatest impact on each model's final ranking, then surfaces the top contributors as drivers with their corresponding impact direction and metric values.
Positive drivers help a model rank higher -- the model excels in this area. Negative drivers push a model down -- this is an area of weakness. Neutral drivers are present but do not significantly affect ranking in either direction.
Dive deeper with signal analysis, benchmark comparisons, or browse all explorer tools.
Ranking drivers are the individual factors that push a model's composite score up or down. Positive drivers (like strong benchmark performance or competitive pricing) boost a model's rank, while negative drivers (like limited capabilities or high cost) pull it down.
Each driver represents the difference between a model's signal score and the average across all models, weighted by importance. Signals include capability breadth, pricing tier, context window size, recency, output capacity, and versatility.
Capability breadth and pricing tier each account for 25% of the composite score, making them the two most influential factors. Context window and recency each contribute 15%, while output capacity and versatility each add 10%.