Use this model when
- The workload needs vector embeddings through the model's listed embedding endpoint.
- The input fits within the published 8.2K-token context record, with output and system overhead budgeted separately.
jina-embeddings-v5-text-smallJina Embeddings V5 Text Small by Jina AI.
Select an endpoint and copy a working example for this model.
from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="jina-embeddings-v5-text-small", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1024, temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(# model="jina-embeddings-v5-text-small",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )modelinputencoding_formatdimensionsuserUse these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
Decision guidance
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See how this model compares to others from the same provider.
Initialized observational baseline with no recorded failures
