AlexNet
ImageNet Classification with Deep CNNs
A deep neural network that, in 2012, smashed the world record on a standard image classification challenge by such a wide margin that nearly every computer vision researcher abandoned their previous approach within a year. It used a graphics card (GPU) for training, proving that the right hardware could make previously impractical techniques suddenly practical.
Why it matters
Before AlexNet, "neural networks" was a fringe specialty. After AlexNet, "deep learning" was the field. The 12 years from this paper to ChatGPT are essentially one continuous compounding curve, and this is its inflection point.
Core Capabilities
Vision
Understands images, scenes, and visual context.
Research
Foundational paper or scientific contribution.
Context Window
Context window not disclosed.
Availability
API
Not available
Product / App
Not available
Open Source
Not released
Enterprise
—
Pricing Model
Research artifact
Not commercially released.
Research Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- Vision-language model from University of Toronto — see the linked sources below for benchmark and review coverage
- Vision and multimodal tasks are the typical fit per the published model card
Best use cases
- Vision tasks (charts, documents, images) per the model card
- See the model spec and sources block for benchmarked use cases
Not ideal for
- Tasks far outside the modalities listed in this model's spec
- Workflows where a more recent successor in the same family scores higher