NVIDIA: Nemotron 3.5 Embed 1B (free) vs NVIDIA: Nemotron 3.5 Embed 8B (free)
A side-by-side comparison of NVIDIA: Nemotron 3.5 Embed 1B (free) (Free) and NVIDIA: Nemotron 3.5 Embed 8B (free) (Free) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.
Specifications
| NVIDIA: Nemotron 3.5 Embed 1B (free) | NVIDIA: Nemotron 3.5 Embed 8B (free) | |
|---|---|---|
| Provider | NVIDIA | NVIDIA |
| Context window | 32,768 | 32,768 |
| Max output | 29,491 | 29,491 |
| Input | text, image | text, image |
| Output | embeddings | embeddings |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Oct 21, 2026 | Oct 21, 2026 |
In depth
NVIDIA: Nemotron 3.5 Embed 1B (free) and NVIDIA: Nemotron 3.5 Embed 8B (free) are both embedding models from NVIDIA, offered on api14 under the same Nemotron 3.5 Embed family. The naming tells you the main structural difference: one is a 1B variant and the other an 8B variant, so the parameter count is the headline distinction between them. Both are listed in the embeddings category, meaning neither one generates chat completions or free-form text. They exist to turn input into vectors that you can store, compare and search over. Both carry the ":free" suffix in their model id, which is how api14 exposes them.
On the specifications that are actually listed, the two models are identical. Both accept text and image as input modalities, and both return embeddings as their only output modality. Both have a context length of 32768 tokens, so there is no difference in how much input either can take in a single request. Neither model lists any supported parameters, so you should not expect to pass sampling controls, tool definitions or structured output schemas to them. The qualitative comparison fields come back empty or tied across the board: no cheaper input side, no cheaper output side, no bigger context side, and no model that is alone in supporting tools, structured output or reasoning. In other words, the published facts do not separate them on capability, context or modality.
For picking between them, the practical question is capacity versus cost, and the only signal you have is the parameter count. The 1B model is the smaller of the two, so it is the natural first call when you want a lightweight embedding pass over a large volume of text or images and you are optimizing for throughput or resource use. The 8B model is the larger one, so it is the natural choice when you suspect the smaller model is not capturing enough of the signal in your data and you are willing to spend more compute per embedding to get a richer representation. Because no benchmark, score or ranking is published for either model here, you should decide empirically: embed a sample of your own corpus with both, then measure retrieval or clustering quality on your task rather than trusting the parameter count alone.
Calling either model through api14 works the same way, because api14 exposes an OpenAI-compatible API. You point your existing OpenAI client at the api14 base URL, send your text or image input to the embeddings endpoint, and set the model field to the id you want. The only change between the two calls is that string: "nvidia/nemotron-3.5-embed-1b:free" for the smaller model and "nvidia/nemotron-3.5-embed-8b:free" for the larger one. Your request shape, authentication and response parsing stay identical, so you can switch models by changing one variable and re-running the same code path.
There is no task in the listed facts where one model is clearly the better pick over the other. They share the same context length, the same input and output modalities, the same lack of supported parameters, and the same provider release date. If you need a tiebreaker beyond size, use the live price table on this page, since pricing is shown there rather than stated here, and run your own evaluation on a representative sample before committing to one model for a full indexing job.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "nvidia/nemotron-3.5-embed-1b:free" to "nvidia/nemotron-3.5-embed-8b:free" and prompts, streaming and tools keep working.
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": "nvidia/nemotron-3.5-embed-8b:free",
"input": [
"The quick brown fox",
"Lorem ipsum"
]
});
console.log(result.data.map((d) => d.embedding.length));
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