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Summarize vs Xing4.0 29B A4B (free): API price, context & features · api14

ScaleDown: Summarize vs XingChen: Xing4.0 29B A4B (free)

A side-by-side comparison of ScaleDown: Summarize ($0.14 / 1M tokens) and XingChen: Xing4.0 29B A4B (free) (Free) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.

Specifications

	ScaleDown: Summarize	XingChen: Xing4.0 29B A4B (free)
Provider	ScaleDown	XingChen
Input	$0.14 / 1M tokens	-
Context window	1,000,000	262,144
Max output	4,000	235,929
Input	text, image, file	text
Output	text	text
Tool calling
Structured output
Reasoning control
Added	Oct 13, 2026	Oct 16, 2026

Key differences

ScaleDown: Summarize reads 3.8× more context (1,000,000 tokens).
Only XingChen: Xing4.0 29B A4B (free) supports tool calling.
Only XingChen: Xing4.0 29B A4B (free) supports structured JSON output.
Only XingChen: Xing4.0 29B A4B (free) supports reasoning effort control.

In depth

ScaleDown: Summarize is a text model from ScaleDown, and XingChen: Xing4.0 29B A4B (free) is a text model from XingChen. Both are served through api14 as chat-style models, and both accept text as input and return text as output. The naming hints at their intended shape: Summarize is positioned around summarization work, while Xing4.0 29B A4B is a general purpose model from XingChen offered on a free tier. Neither is a multimodal generator, so you are choosing between two text-in, text-out endpoints.

The clearest split is context and modality. ScaleDown: Summarize takes a context window of 1,000,000 tokens and accepts text, images and files as input, so you can hand it a long document, a scanned page or an attachment alongside your prompt. XingChen: Xing4.0 29B A4B has a 262,144 token context window and accepts text only. On the parameter side the two are very different. ScaleDown: Summarize exposes only max_tokens, so you get little control over sampling. Xing4.0 29B A4B exposes a wide set, including temperature, top_p, top_k, min_p, frequency_penalty, presence_penalty, repetition_penalty, seed, stop, response_format, structured_outputs, tools, tool_choice, include_reasoning and reasoning. That means tool calling, structured output and reasoning controls are available only on the XingChen model.

For practical work, pick ScaleDown: Summarize when the job is long input text and you want a short answer back: condensing a contract, a research paper, a long thread, or a batch of documents you would rather pass as files or images than paste as plain text. The large window and file and image input make it the natural fit for that shape of task. Pick Xing4.0 29B A4B when you need the model to do more than read and condense: calling tools, returning JSON that matches a schema, or exposing a reasoning trace. It is also the better choice when you want to tune sampling behaviour with temperature, penalties, seeds or stop sequences, since ScaleDown: Summarize gives you none of those knobs.

Calling either one through api14 is the same request shape, because the gateway is OpenAI-compatible. You point your client at the api14 base URL, send a chat completions request with your messages, and set the model field. To use ScaleDown: Summarize, set model to "scaledown/summarize". To use Xing4.0 29B A4B, set model to "xingchen-agi/xing4.0-29b-a4b:free". Everything else in the payload stays the same, so switching between them is a one-line change. The one caveat is parameters: if you send temperature, tools or response_format to ScaleDown: Summarize, it will not honour them, because only max_tokens is supported there. Keep your request body to the common subset if you want the same code to work against both.

There is still a case for the XingChen model on summarization itself. If your summaries need to come back as structured JSON, or you want the model to call a tool to fetch the document first, or you want to control sampling and reasoning, then Xing4.0 29B A4B is the better pick even though its context window is smaller. The reverse also holds: if you need to feed in an image or a file, or your input runs past a quarter of a million tokens, ScaleDown: Summarize is the only one of the two that can take it.

Switch with one string

Both models use the same endpoint and request shape. Change the model value from "scaledown/summarize" to "xingchen-agi/xing4.0-29b-a4b:free" and prompts, streaming and tools keep working.

ScaleDown: Summarize details & playground →XingChen: Xing4.0 29B A4B (free) 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": "xingchen-agi/xing4.0-29b-a4b:free",
  "messages": [
    {
      "role": "user",
      "content": "Explain vector databases in two sentences."
    }
  ]
});
console.log(result.choices[0].message.content);

ScaleDown: Summarize vs XingChen: Xing4.0 29B A4B (free)

Specifications

Key differences

In depth

Switch with one string

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