word2vec
A 2013 method that turned every English word into a 300-dimensional vector, where geometric operations on vectors corresponded to semantic operations on words: vector("king") − vector("man") + vector("woman") landed near vector("queen"). The first technique that made "embeddings" — numerical representations of meaning — a standard tool every NLP system would use.
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
Every modern LLM, every vector search system, every recommendation engine, every multimodal AI — all start from the assumption that high-dimensional vector representations are the right abstraction for meaning. word2vec is when that assumption became operationally proven for language.
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
- 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