Text Embedding 005
Google Vertex AI embeddingText Embedding 005 is a Google Vertex AI embedding model.It supports a 2,048-token context window.Input is priced at $0.1000/M tokens Route Text Embedding 005 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Text Embedding 005 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 May 12, 2026.
| Input | $0.1000/M | |
| Output | — |
Limits
- Context window
- 2,048 tokens
- Max input
- 2,048 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
Where it's strong
Watch out for
- !small context (under 16K tokens)
Benchmarks pending
We haven't logged public benchmark scores for Text Embedding 005 yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Text Embedding 005 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.# Text Embedding 005 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="vertex-ai/text-embedding-005",
messages=[{"role": "user", "content": "Hello, Text Embedding 005!"}],
)
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="vertex-ai/text-embedding-005",
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 ↗Compare with similar models
Text Embedding 005 doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark.
- Gemini Embedding 2Google Vertex AI · $0.200/M in · $0.000000/M out · 8,192 ctx
- Gemini Embedding 2 previewGoogle Vertex AI · $0.200/M in · $0.000000/M out · 8,192 ctx
- Text Embedding Large exp 03.07Google Vertex AI · $0.1000/M in · $0.000000/M out · 8,192 ctx
- Text Embedding preview 0409Google Vertex AI · $0.006250/M in · $0.000000/M out · 3,072 ctx
FAQ
How much does Text Embedding 005 cost?
Input is priced at $0.1000 per 1M tokens and output at $0.000000 per 1M tokens (Google Vertex AI, last verified May 12, 2026).
What is the context window of Text Embedding 005?
Text Embedding 005 supports a 2,048-token context window.
Does Text Embedding 005 support function calling?
Text Embedding 005 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 Text Embedding 005 good for production?
Text Embedding 005 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 Text Embedding 005 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 Text Embedding 005
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Third-party evals — verify the marketing.
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