// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "tencent/hy3"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "tencent/hy3"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.puter.com/puterai/openai/v1/",
api_key="YOUR_PUTER_AUTH_TOKEN",
)
response = client.chat.completions.create(
model="tencent/hy3",
messages=[
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
)
print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
-d '{
"model": "tencent/hy3",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Tencent Hy3 is the official release of Tencent Hunyuan's Hy3 series, a hybrid fast-and-slow-thinking Mixture-of-Experts model with 295B total parameters and 21B active per query, following the earlier hy3-preview. It supports a 262K-token context window and up to 131K output tokens.
Compared to the preview, Tencent reports agent and coding capability gains of 20-30%, a hallucination rate cut from 12.5% to 5.4%, and commonsense error rates nearly halved. It scores 78 on SWE-bench Verified, 57.9 on SWE-bench Pro, and 90.4 on GPQA Diamond, and Tencent says it matches flagship models with two to five times its parameter count.
Hy3 ships under Apache 2.0 and already powers Tencent products like WorkBuddy/CodeBuddy and Yuanbao, with a reported 90% task resolution rate on Tencent's internal WorkBuddy platform. It's a good fit for developers building coding agents, tool-using workflows, and long-context reasoning pipelines who want strong performance at a lower active-parameter cost.
Context Window 203K
tokens
Max Output 131K
tokens
Input Cost $0.13
per million tokens
Output Cost $0.53
per million tokens
Release Date Jul 6, 2026
Output Speed 76
tokens / sec
Latency 1.85s
time to first token
Model Playground
Try Hy3 instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
Benchmarks
How Hy3 performs on standard evaluations.
| Benchmark | Score |
|---|---|
| GPQA Diamond Graduate-level science Q&A | 89.7% |
| Humanity's Last Exam Cross-domain reasoning | 33.5% |
| SciCode Scientific programming | 47.6% |
| LCR Long-context reasoning | 74.7% |
Scores sourced from Artificial Analysis.
Find other Tencent models →
Hy-MT2 1.8B
Hy-MT2 1.8B is a lightweight multilingual translation model from Tencent Hunyuan, part of the Hy-MT2 family that also includes 7B and 30B-A3B (MoE) variants for heavier workloads. It translates across 33 languages, including Chinese, Cantonese, English, French, Japanese, Korean, Arabic, Russian, Tibetan, and Uyghur, and is trained to follow translation-specific instructions such as terminology guidance, style adaptation, and structured data translation. Tencent reports that the 1.8B model outperforms mainstream commercial translation APIs from providers like Microsoft and Doubao overall, though it hasn't published specific BLEU or COMET scores to back up the comparison. The larger 7B and 30B-A3B models are reported to outperform DeepSeek-V4-Pro and Kimi K2.6 on translation tasks in fast-thinking mode. With an 8,192-token context window, it's a fit for developers who need fast, low-cost, dedicated translation via API rather than a general-purpose chat model.
ChatHy-MT2 30B-A3B
Hy-MT2 30B-A3B is a multilingual translation model from Tencent's Hunyuan team, built as a Mixture-of-Experts network with 30B total parameters and roughly 3B active per token. It's part of the Hy-MT2 family, released alongside 1.8B and 7B variants, and is designed for fast, instruction-following translation across 33 languages, including style guides, terminology lists, and structured data formats. Tencent reports it outperforming open-source models including DeepSeek-V4-Pro and Kimi K2.6 on translation quality evaluations, and approaching closed-source Gemini 3.1 Pro. It ships under the Apache 2.0 license. It's a fit for developers building translation features who need multilingual quality without the cost of a general-purpose flagship model, particularly when terminology, tone, or output format need to be controlled via instructions.
ChatHy 3 Preview
Tencent Hy3 is a 295B-parameter Mixture-of-Experts reasoning model developed by Tencent's Hunyuan team, with only 21B parameters active per query. It supports a 256K-token context window and configurable reasoning levels (disabled, low, high), letting you trade off latency and depth per request. Hy3 is particularly strong on coding and agentic tasks. It scores 74.4% on SWE-bench Verified for real-world bug fixing and 67.1% on BrowseComp for complex web research. Its MoE architecture delivers competitive performance against much larger models — matching Kimi-K2.5 (1T+ parameters) on agent benchmarks at a fraction of the compute cost. Best suited for developers building agentic workflows, code generation pipelines, and multi-step reasoning applications where cost-efficiency matters.
Frequently Asked Questions
You can access Hy3 by Tencent through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Hy3 to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0.13 |
| Output | $0.53 |
Hy3 was created by Tencent and released on Jul 6, 2026.
Hy3 supports a context window of 203K tokens. For reference, that is roughly equivalent to 406 pages of text.
Hy3 can generate up to 131K tokens in a single response.
Hy3 scores 42.2 on the Artificial Analysis Intelligence Index, outperforming 89% of tracked models. On coding, it scores 58.8 (outperforms 73% of models).
Yes — the Hy3 API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Get started with Puter.js
Add Hy3 to your app without worrying about API keys or setup.
Read the Docs View Tutorials