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mxbai-embed-large-v1 vs OpenAI text-embedding-3-large: 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.

xAI
Embeddings
vs
OpenAI
Embeddings
Verdict

Choose OpenAI text-embedding-3-large for long documents (8K tokens context). Choose mxbai-embed-large-v1 for shorter prompts where the smaller window keeps latency and cost down.

Side-by-side specs

Specmxbai-embed-large-v1OpenAI text-embedding-3-large
ProviderxAIOpenAI
CategoryEmbeddingsEmbeddings
Input cost / 1M tokensn/a$0.16
Output cost / 1M tokensn/an/a
Context window512 tokens8K tokens
Max output tokens——
Avg. latency—600ms
Featured—Yes
New——
Capabilities
text
text

Pricing example

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

mxbai-embed-large-v1
n/a

100K in × n/a + 50K out × n/a

OpenAI text-embedding-3-large
$0.016

100K in × $0.16 + 50K out × n/a

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 mxbai-embed-large-v1
let r = await client.chat.completions.create({
  model: "mixedbread-ai/mxbai-embed-large-v1",
  messages: [{ role: "user", content: "Hello" }],
});

// After — switched to OpenAI text-embedding-3-large
r = await client.chat.completions.create({
  model: "text-embedding-3-large",
  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 mxbai-embed-large-v1
r = client.chat.completions.create(
    model="mixedbread-ai/mxbai-embed-large-v1",
    messages=[{"role": "user", "content": "Hello"}],
)

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

# After — switched to 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"}]
  }'

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-large

Lower average latency wins for interactive UX.

Cost-sensitive
Tie

The model with the lower input-token price wins.

Frequently asked questions

Which is cheaper, mxbai-embed-large-v1 or OpenAI text-embedding-3-large?
mxbai-embed-large-v1 and OpenAI text-embedding-3-large cannot be compared on per-token price: at least one of them has no per-token price listed (it is priced per run or not yet priced). See each model page for its current price.
Which has more context, mxbai-embed-large-v1 or OpenAI text-embedding-3-large?
OpenAI text-embedding-3-large has the larger context window at 8K tokens, compared to 512 tokens for mxbai-embed-large-v1.
Is mxbai-embed-large-v1 better than OpenAI text-embedding-3-large 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 mxbai-embed-large-v1 and OpenAI text-embedding-3-large via Railwail?
Yes. Both mxbai-embed-large-v1 and OpenAI text-embedding-3-large 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 mxbai-embed-large-v1 to OpenAI text-embedding-3-large?
Replace the model identifier "mixedbread-ai/mxbai-embed-large-v1" with "text-embedding-3-large" 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 mxbai-embed-large-v1 and OpenAI text-embedding-3-large side by side

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

Get Started FreeView mxbai-embed-large-v1View OpenAI text-embedding-3-large