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Gemini 1.5 Pro (vision) vs OpenAI 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.

Google
Multimodal
vs
OpenAI
Embeddings
Verdict

Choose Gemini 1.5 Pro (vision) for cost-sensitive workloads β€” it is roughly 130.0Γ— cheaper on input tokens. Choose OpenAI text-embedding-3-large when you need its broader capabilities or stronger benchmarks.

Choose Gemini 1.5 Pro (vision) for long documents (2.1M tokens context). Choose OpenAI text-embedding-3-large for shorter prompts where the smaller window keeps latency and cost down.

These models serve different use cases (Multimodal vs Embeddings) β€” pick the one whose category matches your workload.

Side-by-side specs

SpecGemini 1.5 Pro (vision)OpenAI text-embedding-3-large
ProviderGoogleOpenAI
CategoryMultimodalEmbeddings
Input cost / 1M tokens€0.0010€0.130
Output cost / 1M tokens€0.0050Free
Context window2.1M tokens8K tokens
Max output tokens8,192β€”
Avg. latencyβ€”600ms
FeaturedYesYes
Newβ€”β€”
Capabilities
text
image
audio
video
text

Pricing example

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

Gemini 1.5 Pro (vision)
€0.0003

100K in Γ— €0.0010 + 50K out Γ— €0.0050

OpenAI text-embedding-3-large
€0.0130

100K in Γ— €0.130 + 50K out Γ— Free

For this workload, Gemini 1.5 Pro (vision) is cheaper than OpenAI text-embedding-3-large by €0.0126 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 β€” using Gemini 1.5 Pro (vision)
let r = await client.chat.completions.create({
  model: "gemini-1.5-pro",
  messages: [{ role: "user", content: "Hello" }],
});

// After β€” 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 β€” using Gemini 1.5 Pro (vision)
r = client.chat.completions.create(
    model="gemini-1.5-pro",
    messages=[{"role": "user", "content": "Hello"}],
)

# After β€” switched to OpenAI text-embedding-3-large
r = client.chat.completions.create(
    model="text-embedding-3-large",
    messages=[{"role": "user", "content": "Hello"}],
)
cURL
# Before β€” using Gemini 1.5 Pro (vision)
curl https://railwail.com/v1/chat/completions \
  -H "Authorization: Bearer $RAILWAIL_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemini-1.5-pro",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

# After β€” 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
Gemini 1.5 Pro (vision)

Higher coding category match or larger context wins.

Writing
Gemini 1.5 Pro (vision)

Bigger context window helps maintain long-form coherence.

Long documents
Gemini 1.5 Pro (vision)

The larger context window is the deciding factor.

Vision
Gemini 1.5 Pro (vision)

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
Gemini 1.5 Pro (vision)

The model with the lower input-token price wins.

Frequently asked questions

Which is cheaper, Gemini 1.5 Pro (vision) or OpenAI text-embedding-3-large?
Gemini 1.5 Pro (vision) is cheaper. On a 100K input + 50K output example, Gemini 1.5 Pro (vision) costs about €0.0003 versus €0.0130 for OpenAI text-embedding-3-large β€” a saving of €0.0126.
Which has more context, Gemini 1.5 Pro (vision) or OpenAI text-embedding-3-large?
Gemini 1.5 Pro (vision) has the larger context window at 2.1M tokens, compared to 8K tokens for OpenAI text-embedding-3-large.
Is Gemini 1.5 Pro (vision) better than OpenAI text-embedding-3-large for coding?
For coding-heavy workloads we lean toward Gemini 1.5 Pro (vision) on this comparison β€” 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 Gemini 1.5 Pro (vision) and OpenAI text-embedding-3-large via Railwail?
Yes. Both Gemini 1.5 Pro (vision) 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 β€” no SDK swap, no separate billing.
How do I switch from Gemini 1.5 Pro (vision) to OpenAI text-embedding-3-large?
Replace the model identifier "gemini-1.5-pro" with "text-embedding-3-large" in your request payload. Everything else β€” API key, base URL, request shape β€” stays the same. See the code example on this page for the exact one-line change.

Try Gemini 1.5 Pro (vision) and OpenAI text-embedding-3-large side by side

One API key, one endpoint, both models. Start free β€” no credit card required.

Get Started FreeView Gemini 1.5 Pro (vision)View OpenAI text-embedding-3-large