ResNet
A 2015 architecture from Microsoft Research that introduced "skip connections" — letting deep neural networks pass information around layers instead of through them. The trick allowed networks to be 100+ layers deep where prior attempts had been capped at ~20. Won ImageNet 2015 by surpassing human accuracy for the first time, and remains the default backbone for vision tasks a decade later.
How are Intelligence, Speed & Cost bucketed?
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Why it matters
ResNet's residual connection is the most-cited architectural primitive in modern deep learning — used in essentially every network with more than ~10 layers. Without it, the depth that enables scaling laws would not be practically achievable.
Core Capabilities
Context Window
Context window not disclosed.
Availability
Pricing Model
Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
What it feels like
- Vision-language model from Microsoft — 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
Tools to try
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