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Ling 3.1 Flash vs Muse Spark 1.3 Contributor: API price, context & features · api14

inclusionAI: Ling 3.1 Flash vs Meta: Muse Spark 1.3 Contributor

A side-by-side comparison of inclusionAI: Ling 3.1 Flash ($0.26 / 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

	inclusionAI: Ling 3.1 Flash	Meta: Muse Spark 1.3 Contributor
Provider	inclusionAI	Meta
Input	$0.26 / 1M tokens	$0.35 / 1M tokens
Output	$0.79 / 1M tokens	$0.70 / 1M tokens
Cache read	$0.05 / 1M tokens	$0.00700 / 1M tokens
Web search	-	$8.75 / 1K searches
Context window	1,000,000	1,048,576
Max output	262,144	943,718
Input	text	text, image, video, file
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Oct 2, 2026	Sep 2, 2026

Key differences

inclusionAI: Ling 3.1 Flash is 25% cheaper on input tokens.
Meta: Muse Spark 1.3 Contributor is 11% cheaper on output tokens.
A request with 1,000 input and 500 output tokens costs $0.000656 with inclusionAI: Ling 3.1 Flash and $0.000700 with Meta: Muse Spark 1.3 Contributor.

In depth

inclusionAI: Ling 3.1 Flash is a text model from inclusionAI, released on 2026-10-02. Meta: Muse Spark 1.3 Contributor is a text model from Meta, released on 2026-09-02. Both are served through api14 as text category models, and both take text in and return text out. The naming tells you something about positioning: Flash suggests a lean, fast option, while the Contributor label on the Meta model suggests a specific tier of Meta's Muse Spark line rather than a general flagship.

The two diverge most on input modalities and context. Ling 3.1 Flash accepts text only, while Muse Spark 1.3 Contributor accepts text, image, video and file inputs, so anything involving a screenshot, a clip or an uploaded document points to the Meta model. Context length is effectively the same: Ling 3.1 Flash offers 1,000,000 tokens and Muse Spark 1.3 Contributor offers 1,048,576 tokens, a difference of roughly five percent that will not decide a real workload. On parameters, Ling 3.1 Flash exposes a wider set, including frequency_penalty, logit_bias, logprobs, min_p, presence_penalty, seed, stop, top_logprobs and a plain reasoning flag. Muse Spark 1.3 Contributor covers the core sampling and tool parameters plus reasoning_effort, which lets you dial reasoning intensity rather than just toggle it. Neither model is listed as uniquely supporting tools, structured outputs or reasoning, so treat those as available on both.

For day to day work, pick Ling 3.1 Flash when your input is plain text and you want fine grained control over sampling and decoding. The logprobs and top_logprobs support makes it the better fit for classification, scoring, reranking or any pipeline where you inspect token level confidence, and the seed and penalty parameters help when you need reproducible or carefully shaped output. Pick Muse Spark 1.3 Contributor when the request includes an image, a video or a file, since Ling 3.1 Flash cannot accept those at all. It is also the natural choice when you want to tune reasoning effort per request instead of enabling reasoning wholesale.

Calling either model through api14 works the same way. Both are OpenAI-compatible, so you point your existing client at the api14 base URL, send your key, and set the model field to inclusionai/ling-3.1-flash or meta/muse-spark-1.3-contributor. Everything else in the request body, messages, temperature, max_tokens, tools, response_format, stays where it is. Switching between the two is a one line change to the model id, which makes it cheap to route requests by content type: text only jobs to Ling 3.1 Flash, anything with an attachment to Muse Spark 1.3 Contributor.

Cost is the one place where the choice is not a clean sweep. Ling 3.1 Flash is the cheaper of the two on input, while Muse Spark 1.3 Contributor is the cheaper on output. The live price table on this page has the actual numbers, so check it against your expected input to output ratio before committing. If your workload is input heavy, such as long document analysis, Ling 3.1 Flash will usually come out ahead. If it is output heavy, such as long generations from short prompts, Muse Spark 1.3 Contributor is likely the better pick, and it brings multimodal input along with it.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "inclusionai/ling-3.1-flash" to "meta/muse-spark-1.3-contributor" and prompts, streaming and tools keep working.

inclusionAI: Ling 3.1 Flash 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);

inclusionAI: Ling 3.1 Flash vs Meta: Muse Spark 1.3 Contributor

Specifications

Key differences

In depth

Switch with one string

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