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tencent/hy3

Novita AI/🇺🇸 US/chat

Hy3 is built for real world business scenarios with a 295B total, 21B active MoE architecture, native 256K context support, and three reasoning modes. It enhances coding, long form comprehension, multi turn dialogue, and agentic task execution, balancing reliability, efficiency, and cost across high frequency interactions and complex workflows.More
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Which id to call

novita/tencent/hy3

This exact deployment on Novita AI, with no routing and no failover. Send it as the model field.

Input /1M

$0.14

$0.04 cached

Output /1M

$0.58

4.1x input

Context

262K

262K output

Added

Jul 2026

chat

Capabilities 4/8

VisionReasoningTool callingCachingWeb searchJSON schemaComputer useImage generation

Provider rates

What tencent/hy3 costs

Provider prices per 1M tokens, updated August 28, 2026.more

These are the upstream provider rates. Pay as you go adds 5%, or 0% if you bring your own keys, and there is no per-request fee. Prompt caching and routing change what you pay against these rates, not the rates themselves.

Input /1M

$0.14

Output /1M

$0.58

Cache write /1M

-

Cache read /1M

$0.04

What a workload costs

100K input + 10K output
$0.0198
1M input + 100K output
$0.20
10M input + 1M output
$1.98

At the rates above, before caching. A cache read costs $0.04 per 1M, so repeated context lands under these figures.

model=

Which id to call

One base url, https://router.requesty.ai/v1, and one key for every id here.

novita/tencent/hy3

This exact deployment on Novita AI, with no routing and no failover. Send it as the model field.

OpenAI compatible

Call it in three lines

Change the base url, use your Requesty key, set the model to any id on the left. Existing OpenAI SDK code needs no other edit.

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from openai import OpenAI client = OpenAI( api_key="YOUR_REQUESTY_API_KEY", base_url="https://router.requesty.ai/v1", ) response = client.chat.completions.create( model="novita/tencent/hy3", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ], ) print(response.choices[0].message.content)

Reference

Specs and data terms

What the catalog reports for this deployment, and what the provider does with the traffic.

Context window262K tokens
Max output262K tokens
API typechat
AddedJul 2026
Model id
Data retentionNone
Used for trainingNo
Served from🇺🇸 US

Released 2026-07-06

Benchmark scores

Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.

Coding Indexcoding
58.8%

Artificial Analysis Coding Index: a composite of coding evaluations including LiveCodeBench, SciCode and Terminal-Bench.

GPQA Diamondreasoning
89.7%

Graduate-level physics, chemistry & biology questions designed to resist Googling.

Intelligence Indexreasoning
42.2%

Artificial Analysis Intelligence Index: a composite of multiple evaluations measuring overall model capability.

Scores from official model cards, Artificial Analysis and public leaderboards. They measure specific skills and do not capture every aspect of model quality, so test on your own workload.

Same provider

More from Novita AI

Newest first, on the same provider and the same key.

Reference

tencent/hy3 questions

How much does tencent/hy3 cost?

tencent/hy3 is priced at $0.14 per million input tokens and $0.58 per million output tokens when accessed via Requesty. Prompt caching is supported, which can cut effective input cost by up to 90% on repeated context. Those are the upstream provider rates: pay as you go adds 5% on top, or 0% if you bring your own provider keys.

What is the context window of tencent/hy3?

tencent/hy3 has a context window of 262K tokens, with a maximum output of 262K tokens per response. That's roughly 350 words of input you can fit in a single prompt.

How does tencent/hy3 perform on benchmarks?

tencent/hy3 scores 89.7% on GPQA Diamond, 58.8% on Coding Index, 47.6% on SciCode. See the full benchmark chart above for results across MMLU Pro, GPQA Diamond, SWE-Bench Verified, HumanEval, MATH, AIME, MMMU, and LiveBench.

What can tencent/hy3 do?

tencent/hy3 supports tool calling, extended reasoning, prompt caching, structured outputs (JSON schema). You can call it through any OpenAI-compatible client by pointing base_url to Requesty.

How do I use tencent/hy3 with the OpenAI SDK?

Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "novita/tencent/hy3". The Quickstart above shows Python, JavaScript and cURL snippets.

Can I run tencent/hy3 through Requesty?

Yes. tencent/hy3 runs through Requesty's OpenAI-compatible API, served from Novita AI. You do not host the model yourself: point base_url at Requesty, set the model to "novita/tencent/hy3", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.

Call tencent/hy3 through one endpoint

One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.

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