Meta: Muse Image vs Tencent: Hy Image 3.5 Preview
A side-by-side comparison of Meta: Muse Image ($8.38 / 1M tokens) and Tencent: Hy Image 3.5 Preview ($5.60 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.
Specifications
| Meta: Muse Image | Tencent: Hy Image 3.5 Preview | |
|---|---|---|
| Provider | Meta | Tencent |
| Image input | $8.38 / 1M tokens | $5.60 / 1M tokens |
| Image output | $8.38 / 1M tokens | $5.60 / 1M tokens |
| Context window | 65,536 | 100,000 |
| Max output | 58,982 | 90,000 |
| Input | text, image | text, image |
| Output | image | image |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Aug 26, 2026 | Oct 5, 2026 |
Key differences
- Tencent: Hy Image 3.5 Preview reads 1.5× more context (100,000 tokens).
In depth
Meta: Muse Image is an image generation model from Meta, published under the model id meta/muse-image. Tencent: Hy Image 3.5 Preview is an image generation model from Tencent, published under the model id tencent/hy-image-v3.5-preview. Both sit in the same category on api14, so neither is a general chat or reasoning model that you would call for text answers. They take text and image input and return image output, which makes them candidates for the same slot in a pipeline: prompt in, picture out. The choice between them comes down to context size and parameter control rather than to a difference in what kind of artifact they produce.
The clearest difference is the context window. Tencent: Hy Image 3.5 Preview accepts up to 100000 tokens, while Meta: Muse Image accepts up to 65536 tokens, so the Tencent model is the one with the bigger context. Both accept text and image as input and both emit image as output, so there is no modality gap to weigh. Where they diverge again is in supported parameters. Meta: Muse Image exposes max_tokens, repetition_penalty, temperature, top_k and top_p, so you can tune sampling behaviour and cap output length from the request body. Tencent: Hy Image 3.5 Preview lists no supported parameters, which means you send your prompt and take what the model returns. The qualitative comparison does not mark either model as cheaper on input or output, so treat them as about the same price and check the live table for the current numbers.
For most day to day image generation, the practical question is how much control you need. If you are iterating on a prompt and want to nudge temperature or top_p, or you want to bound generation with max_tokens, Meta: Muse Image gives you those knobs and is the more comfortable target. If your prompts are long, for example a detailed brief plus several reference images, or you are feeding in a large amount of textual context alongside the image input, Tencent: Hy Image 3.5 Preview is the better fit because its context window is larger. If your prompts are short and you do not intend to touch sampling parameters, either model will do the job, and the deciding factor becomes whatever the live price table shows at the moment you build the request.
Calling either model through api14 works the same way because the API is OpenAI-compatible. You point your client at the same base URL, send the same chat completions style request with your text and image content, and authenticate with the same key. The only field that changes is the model id: use meta/muse-image to route to Meta: Muse Image, or tencent/hy-image-v3.5-preview to route to Tencent: Hy Image 3.5 Preview. That means you can keep one code path, swap the id from configuration, and compare the two on your own prompts without rewriting request handling. If you already have parameter values set for the Meta model, remember that the Tencent model does not advertise support for them, so keep those settings out of the request when you switch.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "meta/muse-image" to "tencent/hy-image-v3.5-preview" and prompts, streaming and tools keep working.
import OpenAI from "openai";
const client = new OpenAI({
baseURL: "https://api14.com/api/v1",
apiKey: process.env.API14_API_KEY,
});
const result = await client.images.generate({
"model": "tencent/hy-image-v3.5-preview",
"prompt": "A minimalist product photo of a ceramic mug on a wooden table",
"n": 1,
"aspect_ratio": "1:1"
});
import { writeFile } from "node:fs/promises";
await writeFile("image.png", Buffer.from(result.data[0].b64_json, "base64"));
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