Recraft: Recraft V4.1 Pro Vector vs Tencent: Hy Image 3.5 Preview
A side-by-side comparison of Recraft: Recraft V4.1 Pro Vector ($1.05 / image) 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
| Recraft: Recraft V4.1 Pro Vector | Tencent: Hy Image 3.5 Preview | |
|---|---|---|
| Provider | Recraft | Tencent |
| Image output | $1.05 / image | $5.60 / 1M tokens |
| Image input | - | $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 | May 13, 2026 | Oct 5, 2026 |
Key differences
- Tencent: Hy Image 3.5 Preview reads 1.5× more context (100,000 tokens).
In depth
Recraft: Recraft V4.1 Pro Vector is an image model from Recraft, released on 2026-05-13. Tencent: Hy Image 3.5 Preview is an image model from Tencent, released on 2026-10-05. Both sit in the same category on api14, so neither is a text or reasoning model you would call for chat completions. They take text and image input and return image output, which makes them candidates for the same slot in a pipeline: you describe what you want, optionally attach a reference image, and get an image back.
The main measurable difference is context. Tencent: Hy Image 3.5 Preview has a context window of 100000 tokens, while Recraft: Recraft V4.1 Pro Vector has 65536 tokens, so the Tencent model is the one with the bigger context. That matters when your prompt is long, when you are feeding in a large reference image or several of them, or when the request carries a lot of descriptive detail that has to survive intact. The input and output modalities are otherwise the same on both sides, and neither model lists supported parameters, so you should not expect to tune sampling behaviour through extra fields. 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 practical selection, start with the task rather than the brand. If your prompts are short and the reference material is light, either model fits and you can pick on output quality from your own tests. If you are working with long, heavily specified prompts or large image inputs and you want the most headroom before anything gets truncated, the larger context on Tencent: Hy Image 3.5 Preview is the concrete reason to choose it. If you have already tuned prompts against Recraft V4.1 Pro Vector and they behave well within its context, there is no capability gap listed here that forces a switch.
Calling either one through api14 works the same way, because the gateway exposes an OpenAI-compatible API. You send your request to the same endpoint with the same request shape, and the only thing that changes is the model id. Use "recraft/recraft-v4.1-pro-vector" for the Recraft model and "tencent/hy-image-v3.5-preview" for the Tencent model. Your text and image inputs go in as usual, and the image comes back in the response. This means you can keep one client, one set of retry and logging code, and one place where the model id is configured, then switch between the two by editing that single string.
That also gives you a clean fallback path. If one model is unavailable or its output does not suit a particular request, you can point the same call at the other id without rewriting your integration. Since the two share input and output modalities and neither exposes extra parameters, a swap is close to a drop-in change, with the context window being the one difference worth watching when you move a long prompt from the Tencent model to the Recraft model.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "recraft/recraft-v4.1-pro-vector" 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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