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Ling 3.1 Flash vs Muse Spark 1.2 Contributor: API price, context & features · api14

inclusionAI: Ling 3.1 Flash vs Meta: Muse Spark 1.2 Contributor

A side-by-side comparison of inclusionAI: Ling 3.1 Flash ($0.26 / 1M tokens) and Meta: Muse Spark 1.2 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 1.2 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	Aug 21, 2026

Key differences

inclusionAI: Ling 3.1 Flash is 25% cheaper on input tokens.
Meta: Muse Spark 1.2 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 1.2 Contributor.

In depth

inclusionAI: Ling 3.1 Flash is a text model from inclusionAI, released on 2026-10-02. Meta: Muse Spark 1.2 Contributor is a text model from Meta, released on 2026-08-21. Both are served through api14 under the text category, so at the surface level they occupy the same slot in a model picker. The differences show up in what each accepts as input and in how each is priced.

On capabilities, the two diverge mainly around modalities. Ling 3.1 Flash takes text in and returns text out. Muse Spark 1.2 Contributor also returns text, but it accepts image, video and file input alongside text, which makes it the only one of the two that can look at something other than a string of characters. Context length is effectively a tie: Ling 3.1 Flash lists 1,000,000 tokens and Muse Spark 1.2 Contributor lists 1,048,576 tokens, a difference small enough that it should not drive a decision. The parameter lists overlap heavily, both expose tools, structured outputs, reasoning controls, temperature, top_k, top_p, repetition_penalty, response_format and max_tokens. Ling 3.1 Flash additionally exposes frequency_penalty, presence_penalty, logit_bias, logprobs, top_logprobs, min_p, seed and stop, which matters if you rely on logprobs, deterministic seeding or token-level bias. Muse Spark 1.2 Contributor exposes reasoning_effort, which Ling 3.1 Flash does not list.

For picking between them, start with the input. If your task involves a screenshot, a chart, a PDF or a video frame, Muse Spark 1.2 Contributor is the one that can accept it, since Ling 3.1 Flash is text only. If your task is pure text at long context, such as summarising a large corpus, searching across a big document set or running an agent loop over text tool calls, both are viable and the context windows are close enough to ignore. In that case the tiebreakers are price and parameters. Ling 3.1 Flash is the cheaper of the two on input, while Muse Spark 1.2 Contributor is the cheaper of the two on output, so the better choice depends on your input to output ratio. A workload that sends large prompts and returns short answers leans toward Ling 3.1 Flash. A workload with small prompts and long generations leans toward Muse Spark 1.2 Contributor. If you need logprobs, top_logprobs, seed or logit_bias, Ling 3.1 Flash is the only option here, since Muse Spark 1.2 Contributor does not list them. If you want to tune how much reasoning effort the model spends, Muse Spark 1.2 Contributor exposes reasoning_effort and Ling 3.1 Flash does not.

Calling either one through api14 works the same way, because the gateway is OpenAI compatible. You point your existing client at the api14 base URL, send your key, and set the model field to the id you want. The only change between the two is that string: use inclusionai/ling-3.1-flash for Ling 3.1 Flash, or meta/muse-spark-1.2-contributor for Muse Spark 1.2 Contributor. Your request body, your messages array, your tool definitions and your response parsing stay as they are. If you already have code written against the OpenAI chat completions shape, switching models is a one line edit, and you can keep both ids in a config map and route per request without maintaining two clients.

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-1.2-contributor" and prompts, streaming and tools keep working.

inclusionAI: Ling 3.1 Flash details & playground →Meta: Muse Spark 1.2 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-1.2-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 1.2 Contributor

Specifications

Key differences

In depth

Switch with one string

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