BGE Large En
Databricks embeddingBGE Large En is a Databricks embedding model.It supports a 512-token context window.Input is priced at $0.100/M tokens Route BGE Large En via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate BGE Large En spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $0.1000/M input · $0.000000/M output. Provider pricing changes. Production costs vary with retries, streaming overhead, and tool-call rounds.
Want this for free? Cache + route via Agent Command Center — first 100K requests and 100K cache hits free every month.
Pricing
Per-token rates, expressed in USD per 1M tokens. Verified Oct 22, 2026.
| Input | $0.100/M | |
| Output | — | |
| Cached input | $0.100/M |
Context & limits
- Context window
- 512 tokens
- Max input
- 512 tokens
- Max output
- —
- Modalities
- embedding, text
Capabilities
- Function calling — not advertised
- Parallel tool calls — not advertised
- Vision input — not advertised
- Audio input — not advertised
- Audio output — not advertised
- PDF input — not advertised
- Streaming ✓ supported
- Structured output — not advertised
- Prompt caching — not advertised
- Reasoning — not advertised
Strengths & caveats — when to pick BGE Large En
Data-driven from BGE Large En's percentile within embedding peers.
Where it's strong
Watch out for
- !limited context — 512-token window is in the bottom quartile; not ideal for long documents or large RAG
- !small context (under 16K tokens)
Benchmarks pending
We haven't logged public benchmark scores for BGE Large En yet. Have one to contribute? Submit a source — citations help us prioritise.
Call BGE Large En via Agent Command Center
One OpenAI-compatible endpoint. Routing, fallback, semantic caching, guardrails, and cost tracking come along for the ride. First 100K requests + 100K cache hits free every month.
agentcc / @agentcc/client). Per-call metadata — provider, cost, latency, cache hit, request id — is returned on x-agentcc-* response headers, so any HTTP client can read it.# BGE Large En via the Agent Command Center Python SDK
# pip install agentcc
import os
from agentcc import AgentCC
client = AgentCC(
api_key=os.environ["AGENTCC_API_KEY"], # from app.futureagi.com → Settings → API Keys
base_url="https://gateway.futureagi.com/v1",
)
resp = client.chat.completions.create(
model="databricks/databricks-bge-large-en",
messages=[{"role": "user", "content": "Hello, BGE Large En!"}],
)
print(resp.choices[0].message.content)
print(f"Tokens: {resp.usage.total_tokens}")
# Per-call gateway metadata is returned on x-agentcc-* response headers.
# When you need it programmatically, use .with_raw_response to get them:
raw = client.chat.completions.with_raw_response.create(
model="databricks/databricks-bge-large-en",
messages=[{"role": "user", "content": "Same call, but I want the headers."}],
)
print("Provider:", raw.headers.get("x-agentcc-provider"))
print("Latency:", raw.headers.get("x-agentcc-latency-ms"), "ms")
print("Cost: ", raw.headers.get("x-agentcc-cost"), "USD")
print("Cache: ", raw.headers.get("x-agentcc-cache"))AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗Explore similar models
BGE Large En doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark. Side-by-side links appear where a comparison page exists; otherwise, open the model profile.
FAQ
How much does BGE Large En cost?
Input is priced at $0.100 per 1M tokens and output at $0.000000 per 1M tokens (Databricks, last verified Oct 22, 2026).
What is the context window of BGE Large En?
BGE Large En supports a 512-token context window.
Does BGE Large En support function calling?
BGE Large En does not currently advertise function-calling support. For agentic workloads, prefer a tool-calling-capable model and route via Agent Command Center for fallback.
Is BGE Large En good for production?
BGE Large En is best evaluated against your own production traces. Pipe traffic through Agent Command Center to compare it head-to-head against alternatives in shadow mode.
How can I route to BGE Large En with fallback?
Use Agent Command Center: a single OpenAI-compatible endpoint that supports cost-optimized routing, latency-aware retries, model fallback, and shadow traffic. Configure once, swap models without app changes.
Useful links for BGE Large En
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.