Chain-of-Thought Prompting (CoT)
A prompting technique that simply asks the model to "show its work" — generate intermediate reasoning steps before the final answer — and produces large accuracy gains on math and reasoning problems. Free, requires no model retraining, and works only on sufficiently large models (~60B+ parameters).
Why it matters
CoT is the discovery that LLM capability isn't just a function of parameters and data — it's also a function of how you elicit the model's computation at inference time. This three-axis view (params × data × inference compute) is now the standard frame for LLM capability planning.
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
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
- Language model from Google — see the linked sources below for benchmark and review coverage
Best use cases
- General-purpose tasks within Google'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