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OpenAI text-embedding-3-large vs OpenAI text-embedding-3-small: Which AI Model Should You Choose?

Pricing, context windows, latency, capabilities, and a one-line code switch — everything you need to pick the right model.

OpenAI
Embeddings
vs
OpenAI
Embeddings
Verdict

Choose OpenAI text-embedding-3-small for cost-sensitive workloads — it is roughly 6.5× cheaper on input tokens. Choose OpenAI text-embedding-3-large when you need its broader capabilities or stronger benchmarks.

Side-by-side specs

SpecOpenAI text-embedding-3-largeOpenAI text-embedding-3-small
ProviderOpenAIOpenAI
CategoryEmbeddingsEmbeddings
Input cost / 1M tokens$0.16$0.024
Output cost / 1M tokensn/an/a
Context window8K tokens8K tokens
Max output tokens——
Avg. latency600ms500ms
FeaturedYesYes
New——
Capabilities
text
text

Pricing example

A typical chat workload of 100,000 input tokens plus 50,000 output tokens.

OpenAI text-embedding-3-large
$0.016

100K in × $0.16 + 50K out × n/a

OpenAI text-embedding-3-small
$0.0024

100K in × $0.024 + 50K out × n/a

For this workload, OpenAI text-embedding-3-small is cheaper than OpenAI text-embedding-3-large by $0.013 per request.

Switch in one line

Both models live behind Railwail's OpenAI-compatible endpoint. Replace the model string and you are done.

JavaScript / TypeScript
import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.RAILWAIL_API_KEY,
  baseURL: "https://railwail.com/v1",
});

// Before — using OpenAI text-embedding-3-large
let r = await client.chat.completions.create({
  model: "text-embedding-3-large",
  messages: [{ role: "user", content: "Hello" }],
});

// After — switched to OpenAI text-embedding-3-small
r = await client.chat.completions.create({
  model: "text-embedding-3-small",
  messages: [{ role: "user", content: "Hello" }],
});
Python
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["RAILWAIL_API_KEY"],
    base_url="https://railwail.com/v1",
)

# Before — using OpenAI text-embedding-3-large
r = client.chat.completions.create(
    model="text-embedding-3-large",
    messages=[{"role": "user", "content": "Hello"}],
)

# After — switched to OpenAI text-embedding-3-small
r = client.chat.completions.create(
    model="text-embedding-3-small",
    messages=[{"role": "user", "content": "Hello"}],
)
cURL
# Before — using OpenAI text-embedding-3-large
curl https://railwail.com/v1/chat/completions \
  -H "Authorization: Bearer $RAILWAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-large",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

# After — switched to OpenAI text-embedding-3-small
curl https://railwail.com/v1/chat/completions \
  -H "Authorization: Bearer $RAILWAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

Which one wins for...

Quick verdicts derived from public specs. Always validate on your own workload.

Coding
OpenAI text-embedding-3-large

Higher coding category match or larger context wins.

Writing
OpenAI text-embedding-3-large

Bigger context window helps maintain long-form coherence.

Long documents
OpenAI text-embedding-3-large

The larger context window is the deciding factor.

Vision
Tie

Multimodal/vision support is required for image inputs.

Real-time chat
OpenAI text-embedding-3-small

Lower average latency wins for interactive UX.

Cost-sensitive
OpenAI text-embedding-3-small

The model with the lower input-token price wins.

Frequently asked questions

Which is cheaper, OpenAI text-embedding-3-large or OpenAI text-embedding-3-small?
OpenAI text-embedding-3-small is cheaper. On a 100K input + 50K output example, OpenAI text-embedding-3-small costs about $0.0024 versus $0.016 for OpenAI text-embedding-3-large — a saving of $0.013.
Which has more context, OpenAI text-embedding-3-large or OpenAI text-embedding-3-small?
OpenAI text-embedding-3-large and OpenAI text-embedding-3-small have similar context windows (8K tokens vs 8K tokens).
Is OpenAI text-embedding-3-large better than OpenAI text-embedding-3-small for coding?
For coding-heavy workloads we lean toward OpenAI text-embedding-3-large on this comparison — it scores higher on the relevant heuristics (category, tags, or context window). Both models are usable for code via Railwail's OpenAI-compatible endpoint, so the safest path is to A/B test on your own prompts.
Can I use both OpenAI text-embedding-3-large and OpenAI text-embedding-3-small via Railwail?
Yes. Both OpenAI text-embedding-3-large and OpenAI text-embedding-3-small are accessible through a single Railwail API key and the OpenAI-compatible /v1/chat/completions endpoint. You only change the "model" parameter to switch between them — no SDK swap, no separate billing.
How do I switch from OpenAI text-embedding-3-large to OpenAI text-embedding-3-small?
Replace the model identifier "text-embedding-3-large" with "text-embedding-3-small" in your request payload. Everything else — API key, base URL, request shape — stays the same. See the code example on this page for the exact one-line change.

Try OpenAI text-embedding-3-large and OpenAI text-embedding-3-small side by side

One API key, one endpoint, both models. Start free — no credit card required.

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