ByteDance Seed: Seedream 5.0 Flash vs Tencent: Hy Image 3.5 Preview
A side-by-side comparison of ByteDance Seed: Seedream 5.0 Flash ($0.06 / 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
| ByteDance Seed: Seedream 5.0 Flash | Tencent: Hy Image 3.5 Preview | |
|---|---|---|
| Provider | ByteDance Seed | Tencent |
| Image output | $0.06 / image | $5.60 / 1M tokens |
| Image input | - | $5.60 / 1M tokens |
| Context window | - | 100,000 |
| Max output | - | 90,000 |
| Input | text, image | text, image |
| Output | image | image |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Oct 1, 2026 | Oct 5, 2026 |
In depth
ByteDance Seed: Seedream 5.0 Flash is an image model from ByteDance Seed, released on 1 October 2026. Tencent: Hy Image 3.5 Preview is an image model from Tencent, released on 5 October 2026. Both sit in the image category, meaning they take a prompt and return an image rather than text, and both are offered through api14 alongside the rest of the catalogue. They are close contemporaries from two different large providers, so the choice between them comes down to how each handles input and how much room it gives you to steer the result.
On capabilities the two are similar in shape but not identical. Both accept text and image as input and both produce image as output, so either can be used for text to image work or for image to image work where you supply a reference. The clearest difference is context. Tencent: Hy Image 3.5 Preview lists a context length of 100000 tokens, while ByteDance Seed: Seedream 5.0 Flash does not publish a context length. That matters when your prompt is long, when you attach several reference images, or when you want to describe a scene in detail without trimming. The other difference is parameter support. Seedream 5.0 Flash accepts frequency_penalty, max_tokens, temperature and top_p, so you can tune sampling behaviour and cap output length from the request. Hy Image 3.5 Preview lists no supported parameters, so you send the prompt and take what comes back.
For day to day work, pick Seedream 5.0 Flash when you want control over generation. If you are iterating on a style and need to nudge temperature or top_p between runs, or you want max_tokens to bound a batch job, that model exposes the knobs. Pick Hy Image 3.5 Preview when the prompt itself is the hard part: long descriptive briefs, multi image references, or pipelines where you would rather not manage sampling settings at all. If your inputs are short and your settings are fixed, either model will do the job and the decision is mostly about which output style you prefer after a quick test.
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 use the chat completions style request you already have. The only change between the two is the model id string: use "bytedance-seed/seedream-5-0-flash" for the ByteDance model and "tencent/hy-image-v3.5-preview" for the Tencent model. Everything else in the request body stays as it is, apart from the parameters each model supports, so switching is a one line edit and you can compare outputs on the same prompt without rewriting your integration.
There is no strong case in the facts here for one model being the fallback for the other. Neither is listed as cheaper on input or output, neither has a larger context advantage beyond the published figure, and neither is the only one offering tools, structured output or reasoning. Treat them as two comparable image endpoints and route by prompt length and parameter needs rather than by any hard capability gap.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "bytedance-seed/seedream-5-0-flash" 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"));
More comparisons
- OpenAI: GPT Image 2.6 Flare vs OpenAI: GPT Image 2.6 Sunburst
- OpenAI: GPT Image 2.6 Flare vs Auto Router
- OpenAI: GPT Image 2.6 Flare vs Auto Code
- OpenAI: GPT Image 2.6 Flare vs Sourceful: Riverflow V2.6 Pro
- OpenAI: GPT Image 2.6 Sunburst vs Auto Router
- OpenAI: GPT Image 2.6 Sunburst vs Auto Code
- OpenAI: GPT Image 2.6 Sunburst vs Sourceful: Riverflow V2.6 Pro
- Auto Router vs Auto Code