Scaling Laws for Neural Language Models (Kaplan et al.)
A paper that turned "more data + more compute = better model" from a hopeful intuition into a quantitative formula. It let researchers forecast — before training — how much capability a given training budget would buy. This made it possible for executives to underwrite billion-dollar training runs.
Why it matters
Without legible scaling forecasts, the 2020s AI capex cycle would have been an order of magnitude smaller. Investment committees do not write nine-figure checks against vibes — they write them against curves.
Core Capabilities
Research
Foundational paper or scientific contribution.
Long Documents
Handles entire codebases, books, and multi-doc RAG.
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
- Language model from OpenAI — see the linked sources below for benchmark and review coverage
Best use cases
- General-purpose tasks within OpenAI's deployment footprint
- 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