BGE M3 Multi 8k
Embedding model by BAAI.
BAAI dense vector embedding model. Transforms natural language text into rich semantic embeddings for search and retrieval.
Context window
Maximum input context length.
8.2K
Maker
BAAI
Specifications
Context window, ranked against other embedding models
Proportional scale: 0 to 8.2K tokens. Not ranked, no published context window: text-embedding-3-large, text-embedding-3-small.
8.2K
tokens—
embedding
Pricing
Rates are read live
Pricing for BAAI/bge-m3-multi-8k is fetched from the live catalogue on load, so it is never served from a cache that could outlive a repricing.
Availability
Not enough data yet
We have not collected enough health probes for this model to publish an uptime or latency figure. A percentage from a handful of samples is fabricated precision, so none is shown until the sample count supports one.
How BGE M3 Multi 8k is billed
Billed at the provider's list price, with no per-token markup.
- Exact list price
- Tokens settle at the published rate of the model that served your call.
- Cache discounts passed through
- Provider prompt-caching discounts reach your balance with no added fee.
- Cost on every response
- Each response carries the
x-nr-request-costheader, so you can reconcile spend per request.
FAQs
What is BGE M3 Multi 8k?
BAAI dense vector embedding model. Transforms natural language text into rich semantic embeddings for search and retrieval.
How much does BGE M3 Multi 8k cost?
BGE M3 Multi 8k is billed at the provider's list price on nRouter with no per-token markup — tokens settle at the exact upstream rate. Input and output rates are read live, so see the pricing section above for the current figures.
What is the context length of BGE M3 Multi 8k?
BGE M3 Multi 8k supports a 8,192-token context window.
Does BGE M3 Multi 8k support tool calling and structured outputs?
No. BGE M3 Multi 8k does not support tool calling, so it cannot invoke external tools or functions.
What inputs and outputs does BGE M3 Multi 8k support?
BGE M3 Multi 8k is an embedding model: it takes text as input and returns vector embeddings as output.
How do I use BGE M3 Multi 8k via nRouter?
Use any OpenAI-compatible client: point it at https://api.nrouter.ai/v1 with your nRouter API key and set the model to "deepinfra/BAAI/bge-m3-multi-8k".
When was BGE M3 Multi 8k released?
BGE M3 Multi 8k was released on October 7, 2026.
Where is the BGE M3 Multi 8k documentation?
Start at nrouter.ai/docs. The quick-start guide (nrouter.ai/docs/getting-started/quick-start) covers API keys and your first request, and the API reference (nrouter.ai/docs/api-reference) lists every parameter.