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UI-TARS 7B vs Muse Spark 1.3 Contributor: API price, context & features · api14

ByteDance: UI-TARS 7B vs Meta: Muse Spark 1.3 Contributor

A side-by-side comparison of ByteDance: UI-TARS 7B ($0.35 / 1M tokens) and Meta: Muse Spark 1.3 Contributor ($0.35 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	ByteDance: UI-TARS 7B	Meta: Muse Spark 1.3 Contributor
Provider	ByteDance	Meta
Input	$0.35 / 1M tokens	$0.35 / 1M tokens
Output	$0.70 / 1M tokens	$0.70 / 1M tokens
Cache read	$0.35 / 1M tokens	$0.00700 / 1M tokens
Web search	-	$8.75 / 1K searches
Context window	128,000	1,048,576
Max output	2,048	943,718
Input	image, text	text, image, video, file
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Jul 22, 2025	Sep 2, 2026

Key differences

Both have about the same input price.
Meta: Muse Spark 1.3 Contributor reads 8.2× more context (1,048,576 tokens).
Only Meta: Muse Spark 1.3 Contributor supports tool calling.
Only Meta: Muse Spark 1.3 Contributor supports reasoning effort control.
A request with 1,000 input and 500 output tokens costs $0.000700 with ByteDance: UI-TARS 7B and $0.000700 with Meta: Muse Spark 1.3 Contributor.

In depth

ByteDance: UI-TARS 7B is a text model from ByteDance, and Meta: Muse Spark 1.3 Contributor is a text model from Meta. Both are served through api14 as chat models with text output, so from the caller's point of view they sit in the same slot in your code. The difference is in what you can feed them and how much you can feed at once. UI-TARS 7B accepts image and text input. Muse Spark 1.3 Contributor accepts text, image, video and file input, so it covers a wider range of source material. On context, Muse Spark 1.3 Contributor is the bigger of the two, with a context window several times larger than the 128000 tokens UI-TARS 7B allows. That gap matters when you are passing long documents, long transcripts or many attachments in a single request.

The two also differ in the parameters they expose. Muse Spark 1.3 Contributor supports tools and tool choice, so it can drive function calling, and it supports reasoning and reasoning effort along with include reasoning. UI-TARS 7B does not list those, but it does list a set of sampling and decoding controls that Muse Spark 1.3 Contributor does not: frequency penalty, logit bias, logprobs, presence penalty, seed, stop, top logprobs and temperature. Both list structured outputs, so neither has a clear edge there. In short, if your application needs the model to call tools or to expose a reasoning step, Muse Spark 1.3 Contributor is the one that supports it. If you need fine control over sampling, log probabilities or deterministic seeds, UI-TARS 7B gives you those knobs.

For picking between them, start with the input. If your task is screenshot understanding, UI work or any image plus text prompt where the prompt fits comfortably in 128000 tokens, UI-TARS 7B is a reasonable default and its decoding controls make it easy to tune. If you are feeding video, files, or very long text, Muse Spark 1.3 Contributor is the practical choice because UI-TARS 7B cannot take those inputs and would truncate or reject the request. If your workflow is agentic, with tool calls and multi step reasoning, Muse Spark 1.3 Contributor is again the better fit. Price is not a deciding factor here: the two are about the same on both input and output, so choose on capability rather than cost.

Calling either one through api14 is the same operation. You send an OpenAI-compatible chat completion request to the gateway and set the model field. Use bytedance/ui-tars-1.5-7b for UI-TARS 7B and meta/muse-spark-1.3-contributor for Muse Spark 1.3 Contributor. Everything else in the request body stays the same shape, so switching between them is a one line change, and you can keep a single client and a single set of message handling code. Just remember that parameters are not interchangeable: sending tools or reasoning to UI-TARS 7B, or logprobs or seed to Muse Spark 1.3 Contributor, is not supported, so gate those fields on the model id you are using.

There are still cases where UI-TARS 7B is the better pick even though Muse Spark 1.3 Contributor is larger and more flexible. If you rely on logprobs or top logprobs for scoring or filtering, or on seed for reproducible outputs, or on frequency and presence penalties and logit bias to shape generation, UI-TARS 7B is the model that exposes them. The same goes for stop sequences and top k. For a tightly scoped image and text task that fits its context window, it does the job without the extra surface area of a reasoning and tool calling model.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "bytedance/ui-tars-1.5-7b" to "meta/muse-spark-1.3-contributor" and prompts, streaming and tools keep working.

ByteDance: UI-TARS 7B details & playground →Meta: Muse Spark 1.3 Contributor 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": "meta/muse-spark-1.3-contributor",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

ByteDance: UI-TARS 7B vs Meta: Muse Spark 1.3 Contributor

Specifications

Key differences

In depth

Switch with one string

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