Hy4 preview
9358°
Run this model
Offers / Ads
Tencent | 1,048,576 token context | $0.83/M input, $2.50/M output. Tencent: Hy4 preview is a mixture-of-experts model from Tencent, with 49B active parameters out of 770B total. It is designed for coding agents, complex tool-use workflows, and productivity tasks that require planning, context continuity, and sustained multi-step execution.
Guides for this model
Best local LLM by use case (October 2026): the right open model for every job
How much VRAM do I actually need for local AI?
Open weights vs open source vs API-only: what you are actually allowed to do
What is a GGUF file? Quantized models explained (Q4, Q8, fp8, NF4)
Captain's notes
Notes from 2026-10-13
Hy4 preview is a 770B mixture-of-experts model from Tencent, activating 49B parameters per token, with a one-million-token context window. Its training was built around real professional workflows — software engineering, finance, game development, scientific research — rather than generic benchmarks. If you run long-horizon agentic tasks or need a frontier-class open-weights model, this is one to evaluate seriously.
Known for
- 770B total parameters, 49B active per token via a 256-expert MoE architecture.
- One-million-token context window for sustained, multi-file workflows.
- Built-in MTP layer enables speculative decoding for faster inference.
- Trained on real professional tasks: coding, finance, game dev, research.
- Open weights available on Hugging Face, supporting local or self-hosted deployment.
Watch out for
- 770B total parameters means self-hosting requires serious multi-GPU infrastructure; most users will hit an API.
- Weights are open but check the licence terms before any commercial or derivative use.
- At $2.25/M output tokens it is not a budget pick; factor that into agentic workflows with long outputs.
Prompts to try
Multi-file refactor plan
You have a Python web service split across twelve files: routes, models, services, tests, and a config layer. The team wants to migrate from synchronous SQLAlchemy to async SQLAlchemy 2.0 without breaking the test suite. Write a step-by-step migration plan, flag every file that needs changes, and draft the updated base model class.
Agentic tool-use scaffold
Design a minimal agent loop in Python that uses three tools: web_search(query), read_file(path), and write_file(path, content). The agent should accept a high-level goal, break it into subtasks, call tools in sequence, and verify each result before moving on. Show the full implementation with error handling.
Financial model from messy context
Here are three years of quarterly revenue, cost, and headcount figures pasted from a spreadsheet with inconsistent formatting. Clean the data, calculate YoY growth rates and operating margin for each quarter, identify the two quarters with the sharpest margin compression, and explain likely causes based on the numbers alone.
Compare with
- Hy3Open-weights model262,144 token context · $0.13/M input, $0.53/M output
- Hy3 previewOpen-weights model262,144 token context · $0.18/M input, $0.60/M output
- Hunyuan A13B InstructOpen-weights model131,072 token context · $0.14/M input, $0.57/M output
- GPT-6 LunaAPI-only model1,050,000 token context · $0.10/M input, $0.50/M output
More text models from tencent
Questions people ask
- Are the weights open and can I run Hy4 preview locally?
- Yes, the weights are published on Hugging Face at tencent/Hy4-preview. Running it locally is technically possible but the 770B total parameter count demands substantial multi-GPU infrastructure — check the VRAM guide before you start.
- What does Hy4 preview cost and what is its context limit?
- Via API it is priced at $0.75 per million input tokens and $2.25 per million output tokens, with a one-million-token context window.
- What is Hy4 preview actually built for?
- Tencent designed it around coding agents, complex tool-use workflows, and multi-step productivity tasks — software engineering, data analysis, game development, and scientific research — rather than as a general-purpose chat model.
Captain's verdictA serious open-weights frontier model built for agents and long-horizon work, not casual chat — the infrastructure cost is real.
Model card on Hugging Face
Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.
Ranking badge
Updated Oct 22, 2026
Made this model? Show this badge on your page.
Paste it into your Hugging Face card or Civitai description.
Also ranked: #3 Hottest Text / LLM model · #3 Hottest Text / LLM text model
Not this creator?
Get your own badge
Hy4 preview Popularity Trend
Heat score since 28 Aug 2026
Heat score
9,358°
This week
▲
29.4%
9,323° then, 9,358° now
This month
▲
349%
9,159° then, 9,358° now
Past year
n/a
Wasn't released/tracked
7,000°
Floor
The scale runs from 0° to 9,999°. Everything below 7,000° is kept for models no source reports on yet, which carry a stand-in number there so lists stay complete. 7,000° is where measured data begins: a model with real data but almost no downloads sits at the floor, and the measurements fill the degrees above it. The floor says nothing about the model; only the degrees above it do, and they are what separates one model from another.
+1,974°
OpenRouter tokens
37,964,608,298,713 tokens this model processed through OpenRouter in the last 30 days, prompts and answers together. Every 10,000,000 tokens count as one download in the download term, 3,796,460 in all; the last day's tokens join the adoption rate. Source: OpenRouter (openrouter.ai/rankings).
+384°
Adoption rate
About 133,694 downloads a day, measured between two readings of the sources, never guessed from a lifetime average. Speed weighs a quarter of the download term, so a fresh surge shows without letting a one-day spike outrank years of use.
Show detailed breakdown
Every number on this site can be checked. This model's heat score is 9,358°, and here it is built one ingredient at a time, with the running total after each: the floor, the degrees each source adds, the bonus for the kind of model and what the age of its weights takes away. The colors are the bands of the chart above. The glossary at the end defines every term.
- 1Start at the floor.Why 7,000° and not zero: the scale runs from 0° to 9,999°, and the part below 7,000° is reserved for models no source reports on yet, which carry a stand-in number there so the lists stay complete and never outrank a model with data. 7,000° is where measured data begins. A model with real data but almost no downloads sits here; everything that follows is measurement, and the room above the floor holds ten tenfold steps in downloads at 300° each. The floor is the same for every model, so it decides nothing; the degrees above it do.7,000°running total
- 2Add the downloads.3,796,460 download-equivalents in all, from OpenRouter tokens. Ten times the downloads adds the same 300°, so size matters but never runs away with the score.+1,974°8,974°running total
- 3Add the adoption rate.About 133,694 downloads a day right now, measured between two readings, at a quarter of the weight.+384°9,358°running total
- =The heat score.Where this model stands on every list of this site. Nothing else adjusts it.9,358°
Glossary: the terms used here
- Heat score
- One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
- Floor
- The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
- Download-equivalents
- Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
- Log scale
- Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
- Likes
- Thumbs up and likes from every source, worth half a download each on the log scale.
- Adoption rate
- How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
- Measured and estimated
- Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
- Seeder
- Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
- AI Horde
- A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
- Generation sites
- Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
- OpenRouter tokens
- A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
- Full model (checkpoint)
- The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
- LoRA and embedding
- Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
- Family and weights
- The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
- Age
- Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
- Family boost
- A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
- Stand-in score
- A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
- New! (grace period)
- A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
- Trend
- How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
- Typical model
- The middle of the catalog: half the models with a figure grew faster over the period, half slower.
How this model's popularity has moved against the rest of the catalog: its growth in downloads, likes and usage over each period, measured against the typical model's growth over the same period, with its heat score at the time. Above the typical growth reads as a rise, below it as a fall. The past is reconstructed from today's totals, the model's first day and its measured download speed where two readings exist; a model that did not exist at the start of a period shows n/a.
Hy4 preview is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models and check its heat score to see how it is trending.
Browse by Model Family
Text / LLM Models
434
Anima Models
528
Chroma Models
19
Fibo Models
2
Flux Models
1,496
Flux 2 / Klein Models
1,239
Flux 3 Models
3
Moss Models
3
Iris Models
1
MiniMax H3 Models
364
Hunyuan Models
204
Ideogram Models
20
Krea2 Models
717
Qwen Models
20
Qwen2 Models
47
Ltx2 Models
153