Voyage AI voyage-3 vs Text Embedding 3 Large: Which AI Model Should You Choose?
Pricing, context windows, latency, capabilities, and a one-line code switch β everything you need to pick the right model.
Choose Voyage AI voyage-3 for cost-sensitive workloads β it is roughly 21.7Γ cheaper on input tokens. Choose Text Embedding 3 Large when you need its broader capabilities or stronger benchmarks.
Side-by-side specs
| Spec | Voyage AI voyage-3 | Text Embedding 3 Large |
|---|---|---|
| Provider | xAI | OpenAI |
| Category | Embeddings | Embeddings |
| Input cost / 1M tokens | β¬0.060 | β¬1.30 |
| Output cost / 1M tokens | Free | Free |
| Context window | 32K tokens | β |
| Max output tokens | β | β |
| Avg. latency | β | 600ms |
| Featured | Yes | Yes |
| New | β | β |
| Capabilities | text | β |
Pricing example
A typical chat workload of 100,000 input tokens plus 50,000 output tokens.
100K in Γ β¬0.060 + 50K out Γ Free
100K in Γ β¬1.30 + 50K out Γ Free
For this workload, Voyage AI voyage-3 is cheaper than Text Embedding 3 Large by β¬0.1240 per request.
Switch in one line
Both models live behind Railwail's OpenAI-compatible endpoint. Replace the model string and you are done.
import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.RAILWAIL_API_KEY,
baseURL: "https://railwail.com/v1",
});
// Before β using Voyage AI voyage-3
let r = await client.chat.completions.create({
model: "voyage-3",
messages: [{ role: "user", content: "Hello" }],
});
// After β switched to Text Embedding 3 Large
r = await client.chat.completions.create({
model: "text-embedding-3-large",
messages: [{ role: "user", content: "Hello" }],
});from openai import OpenAI
client = OpenAI(
api_key=os.environ["RAILWAIL_API_KEY"],
base_url="https://railwail.com/v1",
)
# Before β using Voyage AI voyage-3
r = client.chat.completions.create(
model="voyage-3",
messages=[{"role": "user", "content": "Hello"}],
)
# After β switched to Text Embedding 3 Large
r = client.chat.completions.create(
model="text-embedding-3-large",
messages=[{"role": "user", "content": "Hello"}],
)# Before β using Voyage AI voyage-3
curl https://railwail.com/v1/chat/completions \
-H "Authorization: Bearer $RAILWAIL_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "voyage-3",
"messages": [{"role": "user", "content": "Hello"}]
}'
# After β switched to 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"}]
}'Which one wins for...
Quick verdicts derived from public specs. Always validate on your own workload.
Higher coding category match or larger context wins.
Bigger context window helps maintain long-form coherence.
The larger context window is the deciding factor.
Multimodal/vision support is required for image inputs.
Lower average latency wins for interactive UX.
The model with the lower input-token price wins.
Frequently asked questions
Try Voyage AI voyage-3 and Text Embedding 3 Large side by side
One API key, one endpoint, both models. Start free β no credit card required.