Text Embedding Ada 002 v2

OpenAI embedding

Text Embedding Ada 002 v2 is an OpenAI embedding model.It supports a 8,191-token context window.Input is priced at $0.1000/M tokens Route Text Embedding Ada 002 v2 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: May 12, 2026 View source ↗
Cost calculator

Estimate Text Embedding Ada 002 v2 spend

Pick a workload, fine-tune the sliders, and see the monthly bill.

~3K in / ~400 out · 5K req/day
3,000
08,191
400
016,000
5,000
01,000,000
Per request
$0.000300
in $0.000300 · out $0.000000
Per day
$1.50
5,000 requests
Per month
$45.66
152,188 requests

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
Batch input $0.0500/M

Limits

Context window
8,191 tokens
Max input
8,191 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 Ada 002 v2 yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Text Embedding Ada 002 v2 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.

SDK
Native Future AGI client (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 Ada 002 v2 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="openai/text-embedding-ada-002-v2",
    messages=[{"role": "user", "content": "Hello, Text Embedding Ada 002 v2!"}],
)

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="openai/text-embedding-ada-002-v2",
    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"))
Set AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗

Compare with similar models

Text Embedding Ada 002 v2 doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark.

FAQ

How much does Text Embedding Ada 002 v2 cost?

Input is priced at $0.1000 per 1M tokens and output at $0.000000 per 1M tokens (OpenAI, last verified May 12, 2026).

What is the context window of Text Embedding Ada 002 v2?

Text Embedding Ada 002 v2 supports a 8,191-token context window.

Does Text Embedding Ada 002 v2 support function calling?

Text Embedding Ada 002 v2 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 Ada 002 v2 good for production?

Text Embedding Ada 002 v2 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 Ada 002 v2 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 Ada 002 v2

Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.