hy4-preview
Tencent/🇨🇳 China/chat
Hy4 preview is Tencent's flagship class MoE model with 770B total parameters and 49B activated, supporting a 1M token context window. It is optimized for agent, coding, and productivity scenarios, with strong task decomposition, context tracking, instruction following, and long chain execution. It is suited for coding agents, complex tool use, and multi step agentic workflows.MoreLess
Which id to call
tencent/hy4-previewThis exact deployment on Tencent, with no routing and no failover. Send it as the model field.
Input /1M
$0.83
$0.04 cached
Output /1M
$2.50
3.0x input
Context
1.0M
66K output
Added
Aug 2026
chat
Capabilities 4/8
Provider rates
What hy4-preview costs
Provider prices per 1M tokens, updated September 28, 2026.moreless
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
$0.83
Output /1M
$2.50
Cache write /1M
-
Cache read /1M
$0.04
What a workload costs
- 100K input + 10K output
- $0.11
- 1M input + 100K output
- $1.08
- 10M input + 1M output
- $10.84
At the rates above, before caching. A cache read costs $0.04 per 1M, so repeated context lands under these figures.
OpenAI compatible
Call it in three lines
Change the base url, use your Requesty key, set the model to tencent/hy4-preview. Existing OpenAI SDK code needs no other edit.
123456789101112131415from openai import OpenAI client = OpenAI( api_key="YOUR_REQUESTY_API_KEY", base_url="https://router.requesty.ai/v1", ) response = client.chat.completions.create( model="tencent/hy4-preview", 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.
Public leaderboards
Benchmark scores
Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.
No benchmarks are published for this exact variant yet.
Region-specific deployments and highspeed tiers usually share scores with their base model. Try the base model page or the Tencent models overview.
Same provider
More from Tencent
Newest first, on the same provider and the same key.
Reference
hy4-preview questions
How much does hy4-preview cost?
hy4-preview is priced at $0.83 per million input tokens and $2.50 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 hy4-preview?
hy4-preview has a context window of 1.0M tokens, with a maximum output of 66K tokens per response. That's roughly 1,398 words of input you can fit in a single prompt.
What can hy4-preview do?
hy4-preview 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 hy4-preview 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 "tencent/hy4-preview". The Quickstart above shows Python, JavaScript and cURL snippets.
Can I run hy4-preview through Requesty?
Yes. hy4-preview runs through Requesty's OpenAI-compatible API, served from Tencent. You do not host the model yourself: point base_url at Requesty, set the model to "tencent/hy4-preview", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.
Call hy4-preview through one endpoint
One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.