We use cookies. Cookies and browser storage keep you signed in and remember your preferences. With your permission we also measure how the site is used. You can any time. See our cookie policy.

New here? See how it works A 1-minute tour of the playground, models and your API key.

Muse Spark Open vs Nemotron 3.5 Super VL: API price, context & features · api14

Meta: Muse Spark Open vs NVIDIA: Nemotron 3.5 Super VL

A side-by-side comparison of Meta: Muse Spark Open ($4.38 / 1M tokens) and NVIDIA: Nemotron 3.5 Super VL ($0.52 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	Meta: Muse Spark Open	NVIDIA: Nemotron 3.5 Super VL
Provider	Meta	NVIDIA
Input	$4.38 / 1M tokens	$0.52 / 1M tokens
Output	$14.88 / 1M tokens	$1.75 / 1M tokens
Cache read	$0.52 / 1M tokens	$0.07 / 1M tokens
Context window	1,048,576	1,048,576
Max output	943,718	943,718
Input	text, image, video, file, audio	text, image, video
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Oct 15, 2026	Oct 15, 2026

Key differences

NVIDIA: Nemotron 3.5 Super VL is 88% cheaper on input tokens.
NVIDIA: Nemotron 3.5 Super VL is 88% cheaper on output tokens.
A request with 1,000 input and 500 output tokens costs $0.01 with Meta: Muse Spark Open and $0.00140 with NVIDIA: Nemotron 3.5 Super VL.

In depth

Meta: Muse Spark Open is a text output model from Meta, and NVIDIA: Nemotron 3.5 Super VL is a text output model from NVIDIA. Both are listed on api14 under the text category, and both carry a provider release date of 2026-10-15. Neither is a specialised tool endpoint or a dedicated reasoning variant as far as the listed fields go; they are general chat and completion models that you call the same way you call anything else on the gateway.

The two models share a context window of 1,048,576 tokens, so there is no meaningful difference in how much you can send in a single request. Where they diverge is input modality. Muse Spark Open accepts text, image, video, file and audio input. Nemotron 3.5 Super VL accepts text, image and video input, which means it does not take audio or arbitrary file uploads. Both return text only. On supported parameters the two lists are identical: sampling controls such as temperature, top_p, top_k, min_p, frequency and presence penalties, repetition penalty, seed, stop, max_tokens, logit_bias, logprobs and top_logprobs, plus reasoning and reasoning_effort, include_reasoning, response_format, structured_outputs, tools and tool_choice. So neither model has an exclusive capability among the parameters listed; the practical difference is the extra input types on the Meta side and the pricing on the NVIDIA side.

For most tasks either model will do the job, and the choice comes down to input type and budget. If your pipeline feeds in audio, or you need to pass a file directly rather than extracting its contents yourself, Muse Spark Open is the one that accepts those inputs. If your inputs are text, images or video, Nemotron 3.5 Super VL covers the same ground and is the cheaper option on both input and output tokens, so it is the sensible default for high volume work where cost matters. Because the context windows match, long document and long video workflows do not force one model over the other on size grounds alone.

Calling either model through api14 uses the same OpenAI-compatible interface. You point your client at the gateway, send a chat completions request in the usual shape, and set the model field to either "meta/muse-spark-open" or "nvidia/nemotron-3.5-super-vl". Everything else in the request body, including messages, tools, response_format and the sampling parameters, stays the same, so switching between the two is a one line change to the model id. The same applies to streaming and to any SDK that lets you override the base URL and model name.

There is one case where the more expensive model is still the right pick: when the request includes audio or a file upload. Nemotron 3.5 Super VL does not list those input modalities, so for those requests Muse Spark Open is the only option of the two, and the price difference stops being the deciding factor. For text, image and video work, the cheaper NVIDIA model is the better default.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "meta/muse-spark-open" to "nvidia/nemotron-3.5-super-vl" and prompts, streaming and tools keep working.

Meta: Muse Spark Open details & playground →NVIDIA: Nemotron 3.5 Super VL 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.chat.completions.create({
  "model": "nvidia/nemotron-3.5-super-vl",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

Meta: Muse Spark Open vs NVIDIA: Nemotron 3.5 Super VL

Specifications

Key differences

In depth

Switch with one string

More comparisons