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Codestral Embed 2505 vs Text Embedding 3 Small: API price, context & features · api14

Mistral: Codestral Embed 2505 vs OpenAI: Text Embedding 3 Small

A side-by-side comparison of Mistral: Codestral Embed 2505 ($0.52 / 1M tokens) and OpenAI: Text Embedding 3 Small ($0.07 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Mistral: Codestral Embed 2505	OpenAI: Text Embedding 3 Small
Provider	Mistral	OpenAI
Input	$0.52 / 1M tokens	$0.07 / 1M tokens
Context window	8,192	8,192
Max output	6,553	7,372
Input	text	text
Output	embeddings	embeddings
Tool calling
Structured output
Reasoning control
Added	Oct 30, 2025	Oct 30, 2025

Key differences

OpenAI: Text Embedding 3 Small is 87% cheaper on input tokens.

In depth

Mistral: Codestral Embed 2505 is an embeddings model from Mistral, and OpenAI: Text Embedding 3 Small is an embeddings model from OpenAI. Both sit in the embeddings category, which means they take text in and return vectors out rather than generating chat replies. Neither is a general purpose chat model, so you would not call either one to draft an answer or hold a conversation. You would call them when you need a numeric representation of text for search, clustering, deduplication or classification.

The two models are close on the specifications that matter most here. Both accept text as their only input modality and both return embeddings as their only output modality, so there is no multimodal difference to weigh. Both have a context length of 8192 tokens, which means the amount of text you can embed in a single call is the same on either side. Neither exposes any supported parameters, so you cannot tune them with extra options at request time. The one qualitative difference listed is on input price, where the OpenAI model is the cheaper of the two. Output pricing is not meaningfully different between them, so the cost gap, such as it is, comes from the input side.

For most embedding tasks the choice comes down to fit rather than raw capability, because the capability envelope is nearly identical. If you are embedding code, identifiers, or text that mixes natural language with source, Codestral Embed 2505 is the natural candidate given its lineage. If you are embedding ordinary prose for semantic search, retrieval over documentation, or clustering of user text, Text Embedding 3 Small is a reasonable default, and it is the cheaper option on input, which matters when you are embedding large corpora where input volume dominates. If your corpus is large and mostly plain text, the input price difference is the main practical argument for the OpenAI model. If your corpus is code heavy, the Mistral model is the one to try first.

Calling either model through api14 is the same operation. The API is OpenAI compatible, so you point your existing client at the api14 base URL, send your text to the embeddings endpoint, and set the model field to the id you want. To use the Mistral model, set the model id to mistralai/codestral-embed-2505. To use the OpenAI model, set it to openai/text-embedding-3-small. Nothing else in the request changes, and the response shape is the same, so you can switch between them by editing that one string. That makes it cheap to benchmark both against your own data before committing.

There is a case where the Mistral model is still the better pick even though it costs more on input. If your text is predominantly code or code adjacent, and you care about the quality of the resulting vectors for that kind of content, the Codestral model is the one built for that material, and the input price difference may be worth absorbing. Conversely, if your workload is high volume plain text and cost per input token is the deciding factor, the OpenAI model is the one to reach for. Since both share the same context length and the same input and output modalities, you can decide on content type and input cost alone.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "mistralai/codestral-embed-2505" to "openai/text-embedding-3-small" and prompts, streaming and tools keep working.

Mistral: Codestral Embed 2505 details & playground →OpenAI: Text Embedding 3 Small 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-small",
  "input": [
    "The quick brown fox",
    "Lorem ipsum"
  ]
});
console.log(result.data.map((d) => d.embedding.length));

Mistral: Codestral Embed 2505 vs OpenAI: Text Embedding 3 Small

Specifications

Key differences

In depth

Switch with one string

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