Models › Hy4 family

tencent Apache 2.0 out 59 days ago

780B parameters (8 of 256 experts per token), text in, text out, 1M tokens of context. 5 providers serve it from $0.75 per million input tokens.

Verified 2026-10-25 Rank #306 in LLMs · score 83.5 method v1 Sources 4 Downloads 30d 17.6k · #466 of 619 tracked

Can I run it?468GB at INT4Estimateneeds a multi-GPU nodemethod
What does it cost?$0.75/1M incheapest of 5Tencentfp82026-10-24
Can I use it?Apache 2.0Permissive; patent grantlicence file2026-10-24
Do people use it?17.6kdownloads, 30 days#466 of 619 we track · below the median+6 likes, downloads −21% this week514 likesHugging Face2026-10-24

Worth reading

Picked by rule from 18 tracked pieces: the lab's own post, the most shared, a field report, the latest.

The lab's own post
tencent.com 2026-08-28 387 HN pts · 259 comments sells inference
Most discussed · 14 HN pts

Hy4 Preview

Hy4 preview is a 770B-parameter model with 49B active parameters and a 1M-token context window.

hy.tencent.ai 2026-08-28 14 HN pts · 2 comments 796 words sells inference
Latest · 56 days ago

Hy4 Preview

Tencent released Hy4 Preview, a new open weight text-only LLM with 770B total parameters and 49B active parameters.

simonwillison.net 2026-08-30 4 HN pts 314 words

18 best of 18 tracked

The 4 picks above, plus these 14. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. ArticleTencent Hy4 Previewopenrouter.ai · 2026-08-28 · 4 HN pts
  2. CodeTencent Hy4 Preview LLMgithub.com · 2026-08-28 · 2 HN pts
  3. ArticleTencent Hy4 Preview: Full Specs and How It Stacks Up to GLM 5.3, Kimi K3 | MindStudiomindstudio.ai · 2026-09-02
  4. ArticleTencent Hy4 review: is the 770B open-weight model worth it? (2026) | eesel AIeesel.ai · 2026-08-30
  5. ArticleTencent Hunyuan Hy4 Preview Deep Dive: 770B MoE, Recursive Self-Improvement, and the Chinese LLM Blitz | HappyRockhappyrock.cloud · 2026-08-30
  6. ArticleHy4 preview: 770B MoE, open weights, gaps — MakerPortalmakerportal.ai · 2026-08-28
  7. ArticleHy4 preview: Tencent's Surprising, Powerful Open MoEprogressiverobot.com · 2026-08-28
  8. ArticleHunyuan Hy4 Preview: Tencent's 770B/49B, 1M-Context Open-Source Flagship — Apache 2.0, No Benchmark Table, and a Model That Helped Build Itself — Redeepredeep.blog · 2026-08-28
  9. ArticleHY4 - Zero emission electric air taxis with hydrogen fuel cellshy4.org · 2016-11-26
  10. ArticleTencent Hy4 Preview: Design, Access, and Eval Limits | IntuitionLabsintuitionlabs.ai · undated
  11. ArticleHy4-preview Model Card — Advanced Documentation (Free) | THE D*AI*LY BRIEFberi.net · undated
  12. Articlehttps://kingy.ai/blog/tencent-hy4-preview-review-hy4-vs-kimi-k3-hardwarekingy.ai · undated
  13. ArticleHydrogen fuel cell four-seater passenger plane takes to the airnewatlas.com · 2017-04-28
  14. ArticleHydrogen-electric HY4 passenger aircraft sets altitude recordnewatlas.com · 2022-04-21

Where to run it

Live hosted endpoints, cheapest first, USD per million tokens. Ties break on output price, then provider name.

5 providers from $0.75

5 providers with a live price, from $0.75 to $0.83 per million input tokens; the median is $0.83. One dot per provider, cheapest in teal.

$0.010$0.10$1.00$10.00Tencent: $0.75 in / $2.25 out per 1M · observed 2026-10-24 · https://openrouter.ai/tencent/hy4-previewDeepInfra: $0.79 in / $2.38 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/tencent/Hy4-preview?expand[]=inferenceProviderMappingNovita: $0.83 in / $2.50 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/tencent/Hy4-preview?expand[]=inferenceProviderMappingCohere: $0.83 in / $2.50 out per 1M · observed 2026-10-24 · https://openrouter.ai/tencent/hy4-previewSiliconFlow: $0.83 in / $2.50 out per 1M · observed 2026-10-24 · https://openrouter.ai/tencent/hy4-previewcheapest $0.75median $0.83
$0.010$0.10$1.00$10.00Tencent: $0.75 in / $2.25 out per 1M · observed 2026-10-24 · https://openrouter.ai/tencent/hy4-previewDeepInfra: $0.79 in / $2.38 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/tencent/Hy4-preview?expand[]=inferenceProviderMappingNovita: $0.83 in / $2.50 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/tencent/Hy4-preview?expand[]=inferenceProviderMappingCohere: $0.83 in / $2.50 out per 1M · observed 2026-10-24 · https://openrouter.ai/tencent/hy4-previewSiliconFlow: $0.83 in / $2.50 out per 1M · observed 2026-10-24 · https://openrouter.ai/tencent/hy4-previewcheapest $0.75 · median $0.83 (white tick)
#Cheapest 5In /1MOut /1MContextQuantSeen
1 Tencent $0.75 $2.25 1M fp8 Oct 24
2 DeepInfra $0.79 $2.38 1M fp4 Oct 25
3 Cohere $0.83 $2.50 256K unknown Oct 24
4 Novita $0.83 $2.50 977K fp8 Oct 25
5 SiliconFlow $0.83 $2.50 1M fp8 Oct 24

All 5 providers →

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

Widely served (5 API providers); 7 quantized builds for local use.

  • As a service5Cohere, DeepInfra, Novita, SiliconFlow +1
  • On own hardware7quantized builds
  • Built on22 fine-tunes · 0 adapters · 0 merges
  • In apps8Hugging Face Spaces

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Needs an 8×80 GB node even at INT4

About 468 GB at INT4, 936 GB at FP8, 1.87 TB at BF16: weights only, so leave room for the KV cache. Every expert must be resident, so total parameters count.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 468 GB of weights (estimate; KV cache and activations extra)INT4468 GBFP8: about 936 GB of weights (estimate; KV cache and activations extra)FP8936 GBBF16: about 1.87 TB of weights (estimate; KV cache and activations extra)BF161.87 TB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 468 GB of weights (estimate; KV cache and activations extra)INT4468 GBFP8: about 936 GB of weights (estimate; KV cache and activations extra)FP8936 GBBF16: about 1.87 TB of weights (estimate; KV cache and activations extra)BF161.87 TB

Estimate params × bytes per param × 1.2 · how we estimate

The story so far

Weights, every dated article, and repo commits on one rail.

Most coverage came in the first two weeks

Teal is the weights (2026-08-27), amber triangles are 10 dated articles (each links to it), grey ticks are repo commits.

What people built on it

9 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

9 derivatives on the Hub, most of them quantized

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

7 quantized (Hugging Face model tree)2 fine-tunes (Hugging Face model tree)7 quantized2 fine-tunes
7 quantized (Hugging Face model tree)2 fine-tunes (Hugging Face model tree)7 quantized2 fine-tunes

All variants →

Reference

Everything else, folded: the full tables, every source.

Use-case evidence

4 use cases

What it is good at 4 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

23 fields

Listed in LLMs · MoE

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesHYV4ForCausalLM
Model typehy_v4
Dtypebfloat16
Params by dtypeBF16: 779,930,197,312 · F32: 30,795,421
Size bandXXL
MoEyes
Experts256
Experts per token8
Context length1,048,576 (config.json max_position_embeddings)
PapersarXiv:2512.02556, arXiv:2603.12201
LicenseApache 2.0 (apache-2.0)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · HYV4ForCausalLM · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: not in registry · 773cb938fe9484a8f7e7e46fb1b1fe96412c59ed

vLLM registry (7050ef881ff0835f6890a47db1859a24c65481d6) · SGLang registry (773cb938fe9484a8f7e7e46fb1b1fe96412c59ed) · Matched by architecture class name against each project's registry on main; 'supported' means the architecture is registered, not that this checkpoint was tested.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for Hy4-preview: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)1.87 TB · Cluster (≥640 GB)
FP8 (1 byte/param)936 GB · Cluster (≥640 GB)
INT4 (0.5 bytes/param)468 GB · Multi-GPU node (<640 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra. MoE: all experts must be resident, so total parameters count.

All variants

9 variants

Lineage

Original model Family: tencent/Hy4 9 derivatives on the Hub

quantized: 7finetune: 2

Top variants (9 shown, ranked by downloads)

Relation
Quant
Size
9 rows
Variant Relation Quant Params Band License Author Likes Downloads
6block/Hy4-preview-GGUF quantized GGUF — — apache-2.0 6block 1 2k
inferencerlabs/Hy4-preview-MLX-Q4i quantized MLX — — — inferencerlabs 0 2k
qtum/Hy4-preview-GGUF quantized GGUF — — apache-2.0 qtum 3 1.4k
mlx-community/Hy4-preview-4bit quantized -4bit 770B XXL apache-2.0 mlx-community 2 1.3k
Oxmiq/Hy4-preview-NVFP4-W4A16 quantized NVFP4 780B XXL apache-2.0 Oxmiq 2 1.1k
avar6/Hy4-Preview-gguf quantized gguf — — — avar6 0 714
anemll/Hy4-preview-FlashMoE-STQ1_0 quantized GGUF — — apache-2.0 anemll 1 425
tiny-random/hy4-preview finetune — 10.9M S — tiny-random 0 19
yujiepan/hy4-preview-tiny-random finetune — 10.9M S — yujiepan 0 19

Repository changelog

17 commits

Repository changelog 17 commits

DateCommitSHA
2026-08-28 update 705d81e
2026-08-28 update ce1ba0b
2026-08-28 update e985be8
2026-08-28 update ac1efab
2026-08-28 update 215d117
2026-08-28 Upload folder using huggingface_hub 3595342
2026-08-28 Upload folder using huggingface_hub 0aa562b
2026-08-28 Upload folder using huggingface_hub 79c3855
2026-08-28 Update transformers_version to 5.16.2 5a05584
2026-08-28 Update transformers_version to 5.16.2 6e95c4e
2026-08-28 Update transformers_version to 5.17.0 c744f4b
2026-08-28 Update transformers_version to 5.17.0 910e6dd
2026-08-28 Update transformers_version to >=5.16.1 2e275d1
2026-08-28 Update transformers_version to >=5.16.1 5cdbd68
2026-08-28 Add .gitignore 3a88b69
2026-08-27 Upload folder using huggingface_hub 15d5a3d
2026-08-27 initial commit 7a996c9

All providers

5 providers · 5 endpoints

Where to run 5 providers · 5 endpoints

Hosted endpoints observed for Hy4-preview, sorted by input price (USD per million tokens). Each row names its observer and date; prices change daily, so check the source before quoting. Cheapest observed input price: $0.75/M.

ProviderModel refContextQuant$/M in$/M outtok/sTTFTToolsStatusObserved
Tencent
openrouter
tencent/hy4-preview 1M fp8 $0.75 $2.25 — — yes live 2026-10-24
DeepInfra
hf, deepinfra, openrouter
tencent/Hy4-preview 1M fp4 $0.79 $2.38 48.7 1235 ms yes live 2026-10-25
Novita
hf, openrouter
tencent/hy4-preview 977K fp8 $0.83 $2.50 24.1 2565 ms yes live 2026-10-25
Cohere
openrouter
tencent/hy4-preview 256K unknown $0.83 $2.50 — — yes live 2026-10-24
SiliconFlow
openrouter
tencent/hy4-preview 1M fp8 $0.83 $2.50 — — yes live 2026-10-24

Articles and coverage

18 pieces

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Official1

tencent.com2026-08-28officialHN front pageHN 387 pts · 259 comments

Analysis9

simonwillison.net2026-08-30authorityHN 4 pts · 0 comments314 words
Tencent released Hy4 Preview, a new open weight text-only LLM with 770B total parameters and 49B active parameters. The model features a 1M token context window and occupies 1.56TB on Hugging Face. It supports two reasoning effort levels: "high" as the default and "no_think" to disable reasoning.written, reviewed, cited
openrouter.ai2026-08-28authorityHN 4 pts · 0 comments315 words
Tencent Hy4 preview is a mixture-of-experts model with 49B active parameters out of 770B total designed for coding agents and complex tool-use workflows. The model achieved 90.3% on AutoExacto benchmarks and 77.7% on GPQA Diamond when routed through Tencent Cloud.generated, cited
hy.tencent.ai2026-08-28HN 14 pts · 2 comments796 words
Hy4 preview is a 770B-parameter model with 49B active parameters and a 1M-token context window. In a blind evaluation, 163 internal experts rated Hy4 preview slightly ahead of GLM 5.3 and Kimi K3 on 203 engineering tasks.generated, cited
github.com2026-08-28authorityHN 2 pts · 0 comments1,026 words
Tencent released Hy4 preview, a 770B parameter Mixture-of-Experts model with 49B activated parameters per token and a 1M context length. The architecture includes 78 layers, a native MTP layer for speculative decoding, and Gated DeepSeek Sparse Attention with IndexCache. Engineers can deploy the Apache License 2.0 model using vLLM or SGLang for production serving.written, reviewed, cited
  • The model defaults to a deep chain-of-thought reasoning mode suitable for complex tasks like math and coding.
  • Engineers can disable this reasoning mode by passing a specific extra_body argument to the API call.
  • The model exhibits a known tendency to over-verify its own work and spends longer than necessary on complex reasoning.
mindstudio.ai2026-09-021,351 words
eesel.ai2026-08-30about the Hy4 family1,970 words
happyrock.cloud2026-08-305,662 words
samihedhli.com2026-08-291,902 words
makerportal.ai2026-08-28
All coverage tracked for this model (18) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
Tencent Hy4 Preview: Design, Access, and Eval Limits | IntuitionLabsintuitionlabs.ai—2026-10-07exa
Hy4-preview Model Card — Advanced Documentation (Free) | THE D*AI*LY BRIEFberi.net—2026-10-07exa
https://kingy.ai/blog/tencent-hy4-preview-review-hy4-vs-kimi-k3-hardwarekingy.ai—2026-10-07exa
Tencent Hy4 Preview: Full Specs and How It Stacks Up to GLM 5.3, Kimi K3 | MindStudiomindstudio.ai2026-09-022026-10-07exa
Hy4 Previewsimonwillison.net2026-08-302026-10-07hn
Tencent Hy4 review: is the 770B open-weight model worth it? (2026) | eesel AIeesel.ai2026-08-302026-10-07exa
Tencent Hunyuan Hy4 Preview Deep Dive: 770B MoE, Recursive Self-Improvement, and the Chinese LLM Blitz | HappyRockhappyrock.cloud2026-08-302026-10-07exa
HY4 Preview Benchmarked: 770B MoE Frontend Performancesamihedhli.com2026-08-292026-10-07exa
Tencent Hy4 Preview LLMgithub.com2026-08-282026-10-07hn
Hy4 previewtencent.com official2026-08-282026-10-07hn
Tencent Hy4 Previewopenrouter.ai2026-08-282026-10-07hn
Hy4 Previewhy.tencent.ai2026-08-282026-10-07hn
Hy4 preview: 770B MoE, open weights, gaps — MakerPortalmakerportal.ai2026-08-282026-10-07exa
Hy4 preview: Tencent's Surprising, Powerful Open MoEprogressiverobot.com2026-08-282026-10-07exa
Hunyuan Hy4 Preview: Tencent's 770B/49B, 1M-Context Open-Source Flagship — Apache 2.0, No Benchmark Table, and a Model That Helped Build Itself — Redeepredeep.blog2026-08-282026-10-07exa
Hydrogen-electric HY4 passenger aircraft sets altitude recordnewatlas.com2022-04-212026-10-07hn
Hydrogen fuel cell four-seater passenger plane takes to the airnewatlas.com2017-04-282026-10-07hn
HY4 - Zero emission electric air taxis with hydrogen fuel cellshy4.org2016-11-262026-10-07hn

Papers

2 papers

Papers 2

arXiv links found in the model card and its tags. "Peer-reviewed" is shown only with evidence: a published venue in OpenAlex (measured) or the authors' own journal reference on arXiv (reported).

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  • GLM-5 · 754B
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Sources

4 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:37 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 15:06 UTC · Last verified 2026-10-25

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