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Gemini Embedding 2 vs Text Embedding 3 Large: API price, context & features · api14

Google: Gemini Embedding 2 vs OpenAI: Text Embedding 3 Large

A side-by-side comparison of Google: Gemini Embedding 2 ($0.70 / 1M tokens) and OpenAI: Text Embedding 3 Large ($0.45 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Google: Gemini Embedding 2	OpenAI: Text Embedding 3 Large
Provider	Google	OpenAI
Input	$0.70 / 1M tokens	$0.45 / 1M tokens
Audio input	$22.75 / 1M tokens	-
Image	$0.000002 / image	-
Context window	8,192	8,192
Max output	7,372	7,372
Input	text, image, file, audio, video	text
Output	embeddings	embeddings
Tool calling
Structured output
Reasoning control
Added	May 20, 2026	Oct 30, 2025

Key differences

OpenAI: Text Embedding 3 Large is 35% cheaper on input tokens.

In depth

Google: Gemini Embedding 2 is an embeddings model from Google, listed under the model id google/gemini-embedding-2. OpenAI: Text Embedding 3 Large is an embeddings model from OpenAI, listed under the model id openai/text-embedding-3-large. Both sit in the embeddings category, and both return embeddings as their only output, so neither is a chat or completion model you would use to generate text. The choice between them comes down to what you are embedding and how much you want to pay for it.

The two models share the same context window of 8192 tokens, so neither gives you more room per request than the other. Where they part ways is input. Gemini Embedding 2 accepts text, image, file, audio and video as input, while Text Embedding 3 Large accepts text only. If your pipeline needs to embed anything other than text, Gemini Embedding 2 is the one that can do it, and Text Embedding 3 Large simply cannot take that input at all. Neither model exposes supported parameters in the listing, so you are not choosing between them on the basis of tunable knobs. On price, Text Embedding 3 Large is the cheaper of the two for input, so text-only workloads pay less there.

For a text-only workload, such as embedding documents, product descriptions, support tickets or search queries, Text Embedding 3 Large is the straightforward pick: it handles text, it shares the same context length, and it is cheaper on input. Reach for Gemini Embedding 2 when your data is not purely text. Mixed media collections, image libraries, audio archives or video material all fall inside its input modalities, and a single model that covers those cases saves you from running a separate pipeline per media type. If you are unsure, start with the text-only model for text and switch to Gemini Embedding 2 only where you actually have non-text input to embed.

Calling either model through api14 works the same way, because the API is OpenAI-compatible. You send your request to the embeddings endpoint with your api14 key, and the only thing that changes between the two is the model id string. Use google/gemini-embedding-2 for Google's model and openai/text-embedding-3-large for OpenAI's. Your request shape, your client library and your response parsing stay as they are, so you can move a text workload from one to the other by editing that single field, or route non-text input to Gemini Embedding 2 while keeping text on Text Embedding 3 Large in the same codebase.

There is a case where Text Embedding 3 Large remains the better pick even though Gemini Embedding 2 covers more input types. If everything you embed is text and input cost matters to you, the cheaper input price on the OpenAI model is a real reason to stay with it rather than pay for multimodal capability you will never call. The reverse also holds: if you have non-text input, no price advantage makes Text Embedding 3 Large usable for it, since text is the only modality it accepts. Match the model to the data you actually have.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "google/gemini-embedding-2" to "openai/text-embedding-3-large" and prompts, streaming and tools keep working.

Google: Gemini Embedding 2 details & playground →OpenAI: Text Embedding 3 Large details & playground →

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api14.com/api/v1",
  apiKey: process.env.API14_API_KEY,
});

const result = await client.embeddings.create({
  "model": "openai/text-embedding-3-large",
  "input": [
    "The quick brown fox",
    "Lorem ipsum"
  ]
});
console.log(result.data.map((d) => d.embedding.length));

Google: Gemini Embedding 2 vs OpenAI: Text Embedding 3 Large

Specifications

Key differences

In depth

Switch with one string

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