Meta: Llama 3.1 70B Instruct vs Tencent: Hy3 preview
A side-by-side comparison of Meta: Llama 3.1 70B Instruct ($2.52 / 1M tokens) and Tencent: Hy3 preview ($0.63 / 1M tokens) using current USD prices and capabilities from the api14 catalog. Both run through the same OpenAI-compatible API.
Specifications
| Meta: Llama 3.1 70B Instruct | Tencent: Hy3 preview | |
|---|---|---|
| Provider | Meta | Tencent |
| Input | $2.52 / 1M tokens | $0.63 / 1M tokens |
| Output | $2.52 / 1M tokens | $2.10 / 1M tokens |
| Cache read | - | $0.21 / 1M tokens |
| Context window | 131,072 | 262,144 |
| Max output | 8,192 | 235,929 |
| Input | text | text |
| Output | text | text |
| Tool calling | ||
| Structured output | ||
| Reasoning control | ||
| Added | Jul 23, 2024 | Apr 22, 2026 |
Key differences
- Tencent: Hy3 preview is 75% cheaper on input tokens.
- Tencent: Hy3 preview is 17% cheaper on output tokens.
- Tencent: Hy3 preview reads 2.0× more context (262,144 tokens).
- Only Tencent: Hy3 preview supports tool calling.
- Only Tencent: Hy3 preview supports reasoning effort control.
- A request with 1,000 input and 500 output tokens costs $0.00378 with Meta: Llama 3.1 70B Instruct and $0.00168 with Tencent: Hy3 preview.
In depth
Meta: Llama 3.1 70B Instruct is a text model from Meta, released on 2024-07-23. Tencent: Hy3 preview is a text model from Tencent, released on 2026-04-22. Both are served through api14 as chat style models with a single text input and text output, so at the level of basic request shape they look similar. The differences show up in context length, supported parameters and price.
The most obvious split is context. Llama 3.1 70B Instruct has a context window of 131072 tokens, while Hy3 preview doubles that at 262096 tokens, so Hy3 preview is the one to reach for when your prompt, documents or conversation history are long. Hy3 preview also exposes parameters that Llama 3.1 70B Instruct does not: include_reasoning, reasoning, reasoning_effort, seed and tools, plus tool support. That means Hy3 preview is the only one of the two that can do tool calling and the only one with reasoning controls. Llama 3.1 70B Instruct supports max_tokens, stop, temperature, top_k and top_p, which covers ordinary sampling but nothing beyond it. On price, Hy3 preview is the cheaper option for both input and output tokens.
For day to day work, the choice usually comes down to whether you need the extra features. If you are doing straightforward generation, summarisation, classification or chat where a fixed prompt fits comfortably and you do not need the model to call functions, Llama 3.1 70B Instruct is a reasonable, well understood option and its sampling parameters give you the usual knobs. If you need the model to call tools, if you want to tune reasoning effort, or if your inputs are large enough that a 131072 token window is tight, Hy3 preview is the better fit, and it is also the cheaper of the two on both input and output. The larger context and the reasoning parameters make it the more flexible model for agent style or retrieval heavy workloads.
Calling either one through api14 works the same way. Both are OpenAI compatible, so you point your existing client at the api14 base URL, send your key, and set the model field. To use Llama 3.1 70B Instruct you pass "meta-llama/llama-3.1-70b-instruct" as the model id. To use Hy3 preview you pass "tencent/hy3-preview". Everything else in the request body stays as it is, and the response comes back in the same shape. Switching between the two is a one line change, which makes it easy to test both against your own prompts before committing.
There is still a case for Llama 3.1 70B Instruct even though Hy3 preview is cheaper and has more features. If your workload is plain text generation with no tool calls and no need for reasoning controls, and your prompts fit inside 131072 tokens, then the extra parameters on Hy3 preview buy you nothing, and you can pick either without losing anything functional. In that situation the decision is mostly about which model's outputs you prefer on your own data, which is something only your own evaluation can settle.
Switch with one string
Both models use the same endpoint and request shape. Change the model value from "meta-llama/llama-3.1-70b-instruct" to "tencent/hy3-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.chat.completions.create({
"model": "tencent/hy3-preview",
"messages": [
{
"role": "user",
"content": "Explain vector databases in two sentences."
}
]
});
console.log(result.choices[0].message.content);
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