Use this model when
- The workload fits the text or chat tasks shown in this model's current catalog record.
- The input fits within the published 8.2K-token context record, with output and system overhead budgeted separately.
RerankerChat
BGE Reranker V2 M3
BAAI/bge-reranker-v2-m3BGE Reranker V2 M3 by Reranker.
- Context
- 8.2K tokens
- Endpoint
Pricing
Input$0.14 / 1M
Output$0.14 / 1M
Cache Write (5m)Not applicable
Cache Write (1h)Not applicable
Cache ReadNot applicable
Web Search$0 / 1M
Quick Start
Select an endpoint and copy a working example for this model.
Endpoint:
python
from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="BAAI/bge-reranker-v2-m3", 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="BAAI/bge-reranker-v2-m3",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )Supported Parameters
API docsCommon4 params
modelquerydocumentstop_nExtended2 params
return_documentsmax_chunks_per_docCursor IDE Model IDs
Use these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
Decision guidance
Fit the request contract before production.
Check before production
- Confirm the production client uses one of the listed request surfaces: /v1/rerank.
- Estimate a representative request from the current Pay As You Go price fields instead of extrapolating from a tiny prompt.
- Check the observed-availability card and run your own timeout, retry, and fallback test before relying on the model.
Compare with Other Models
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