text-ada
text-ada has model metadata, but missing tracked provider pricing keeps it from being a default production pick.
Use it for
- Teams evaluating general LLM work
- Workloads that can use a 2k context window
Do not use it for
- Cost-sensitive launches that need sourced token pricing
- Vision or document-understanding workloads
- Strict JSON or tool-calling flows
- Family
- GPT-3 Instruct
- Released
- 2020-06-01
- Context
- 2k
- Parameters
- 350M
- Architecture
- Decoder Only
- Knowledge cutoff
- 2019-10
- Specialization
- general
- Training
- finetuned
About
OpenAI's text-embedding-ada-002 is a robust model adept at converting text into numerical vectors to encapsulate semantic meaning, making it ideal for tasks like semantic search, document similarity assessments, and text clustering. Though it has a knowledge cutoff at September 2021, limiting its awareness of newer events, it remains efficient and cost-effective. It may not excel in benchmarks involving classification, yet it proves beneficial for search engines, recommendation systems, and numerous natural language tasks. Despite the existence of newer models with enhanced multilingual capabilities, text-embedding-ada-002 is still favored for its balanced performance and affordability 2567.
text-ada is a model in the GPT-3 Instruct family. The structured metadata tracks a 2k-token context window. No headline benchmark score is tracked for text-ada yet.
Top use-case fit
No primary decision-task fit is mapped for this model yet.
Provider price ladder
No tracked provider token pricing is available for this model yet.
Capabilities
No model capability flags are currently sourced.
Benchmark peer barsfor Coding
No task-mapped benchmark peers are available for this model yet.
Migration checks
No linked migration route is available for this model yet.
API versions
text-ada-001