Models › Hy3 family

tencent Apache 2.0 out 4 months ago

299B parameters (8 of 192 experts per token), text in, text out, 256K tokens of context. 7 providers serve it from $0.083 per million input tokens.

Verified 2026-10-25 Rank #149 in LLMs · score 93.1 method v1 Sources 4 Downloads 30d 941.7k · #118 of 619 tracked

Changed this week: Cheapest input price fell 36.5%: $0.130 → $0.083 per 1M (OpenRouter).2026-10-24 · 1 new quantized build on the Hub (66 in all).2026-10-24

Can I run it?179GB at INT4Estimateneeds a multi-GPU nodemethodCommunity: Tencent Hy3: 74.4% SWE-bench With Only 21B Active Parameters (2026) →
What does it cost?$0.083/1M incheapest of 6Tencentfp82026-10-24
Can I use it?Apache 2.0Permissive; patent grantlicence file2026-10-24
Do people use it?941.7kdownloads, 30 days#118 of 619 we track · top 19%+5 likes, downloads +31% this week987 likesHugging Face2026-10-24

Worth reading

Picked by rule from 15 tracked pieces: the lab's own post, the most shared, a field report, the latest.

The lab's own post

Hy3

Tencent released Hy3 on July 6, 2026, as an updated version of its Hunyan model available under the Apache 2.0 license.

hy.tencent.com 2026-07-07 561 HN pts · 120 comments 197 words sells inference
Latest · 2 months ago

OpenCode go provides Hy3 with 8X usage

OpenCode Go is a $10 per month subscription providing reliable access to curated coding models for agentic workflows.

  • Muse Spark 1.3 and 1.2 Contributor models require permission to use prompts for training future Meta models.
  • Grok 4.7 disables the stateful Responses API, Files and Collections, and the Batch API due to ZDR.
opencode.ai 2026-08-21 2 HN pts 1k words

15 best of 15 tracked

The 4 picks above, plus these 11. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. DiscussionHy3 – 295B MoE model (21B active, 192 experts)twitter.com · 2026-07-07 · 2 HN pts
  2. ArticleWhat Is Hy3? Tencent's 295B Open-Source MoEkie.ai · 2026-07-16
  3. ArticleTencent's Hy3 Bets On AI Agents Over Model Sizeforbes.com · 2026-07-13
  4. ArticleTencent Hy3: 74.4% SWE-bench With Only 21B Active Parameters (2026)aimadetools.com · 2026-07-11
  5. ArticleTencent Hunyuan Hy3 Deep Dive: 295B MoE Fast-Slow Thinking Fusion, Apache 2.0 Open Source, 90% Agent Task Success Rate | HappyRockhappyrock.cloud · 2026-07-08
  6. ArticleTencent Hy3: 295B Params, 21B Active — Can You Run It? - DEV Communitydev.to · 2026-07-08
  7. ArticleTencent Hunyuan Hy3: Does Smaller Actually Beat Flagship Open Weights · Groundygroundy.com · 2026-07-08
  8. ArticleTencent Hunyuan Hy3 Developer Guide & Benchmarks | Lushbinarylushbinary.com · 2026-07-06
  9. ArticleTencent's Hunyuan Hy3: Open-Weight Reasoning Arrivesdigitalapplied.com · 2026-07-05
  10. ArticleTencent used Anthropic's Claude to fine-tune it's new Hy3 AI modelreuters.com · 2026-04-29
  11. ArticleTencent Hy3 Preview: The 295B Open MoE That Topped OpenRouter — and What Builders Should Actually Know — ChatForestchatforest.com · 2026-06-14

Where to run it

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

Prices for the same weights vary 2×

6 providers with a live price, from $0.083 to $0.20 per million input tokens; the median is $0.14. One dot per provider, cheapest in teal.

$0.010$0.10$1.00$10.00Tencent: $0.083 in / $0.33 out per 1M · observed 2026-10-24 · https://openrouter.ai/tencent/hy3DeepInfra: $0.13 in / $0.53 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/tencent/Hy3?expand[]=inferenceProviderMappingNovita: $0.14 in / $0.58 out per 1M · observed 2026-10-24 · https://openrouter.ai/tencent/hy3GMICloud: $0.14 in / $0.58 out per 1M · observed 2026-10-24 · https://openrouter.ai/tencent/hy3Phala: $0.15 in / $0.64 out per 1M · observed 2026-10-24 · https://openrouter.ai/tencent/hy3AtlasCloud: $0.20 in / $0.80 out per 1M · observed 2026-10-24 · https://openrouter.ai/tencent/hy3cheapest $0.083median $0.14
$0.010$0.10$1.00$10.00Tencent: $0.083 in / $0.33 out per 1M · observed 2026-10-24 · https://openrouter.ai/tencent/hy3DeepInfra: $0.13 in / $0.53 out per 1M · observed 2026-10-25 · https://huggingface.co/api/models/tencent/Hy3?expand[]=inferenceProviderMappingNovita: $0.14 in / $0.58 out per 1M · observed 2026-10-24 · https://openrouter.ai/tencent/hy3GMICloud: $0.14 in / $0.58 out per 1M · observed 2026-10-24 · https://openrouter.ai/tencent/hy3Phala: $0.15 in / $0.64 out per 1M · observed 2026-10-24 · https://openrouter.ai/tencent/hy3AtlasCloud: $0.20 in / $0.80 out per 1M · observed 2026-10-24 · https://openrouter.ai/tencent/hy3cheapest $0.083 · median $0.14 (white tick)
#Cheapest 5In /1MOut /1MContextQuantSeen
1 Tencent $0.083 $0.33 256K fp8 Oct 24
2 DeepInfra $0.13 $0.53 256K — Oct 25
3 GMICloud $0.14 $0.58 256K bf16 Oct 24
4 Novita $0.14 $0.58 256K unknown Oct 24
5 Phala $0.15 $0.64 256K unknown Oct 24

All 7 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 (7 API providers); heavily run locally (66 quantized builds).

  • As a service7AtlasCloud, DeepInfra, Featherless, GMICloud +3
  • On own hardware66quantized builds
  • Built on1313 fine-tunes · 0 adapters · 0 merges
  • In apps27Hugging Face Spaces

Top Spaces running it: DeepImagix/self-trained2 ♥ 1 · akhaliq/Hy3-new ♥ 1 · vora-sonnet/aurora-ai-os ♥ 1 · abonaie222263/hf-openai-proxy ♥ 1

Will it fit?

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

Needs an 8×80 GB node even at INT4

About 179 GB at INT4, 359 GB at FP8, 717 GB 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 179 GB of weights (estimate; KV cache and activations extra)INT4179 GBFP8: about 359 GB of weights (estimate; KV cache and activations extra)FP8359 GBBF16: about 717 GB of weights (estimate; KV cache and activations extra)BF16717 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 179 GB of weights (estimate; KV cache and activations extra)INT4179 GBFP8: about 359 GB of weights (estimate; KV cache and activations extra)FP8359 GBBF16: about 717 GB of weights (estimate; KV cache and activations extra)BF16717 GB

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 (2026-07-02), amber triangles are 10 dated articles (each links to it), grey ticks are repo commits.

What people built on it

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

79 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.

66 quantized (Hugging Face model tree)13 fine-tunes (Hugging Face model tree)66 quantized13 fine-tunes
66 quantized (Hugging Face model tree)13 fine-tunes (Hugging Face model tree)66 quantized13 fine-tunes

All variants →

Reference

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

Benchmarks

1 row

Benchmarks 1 row

measured third-party leaderboard rowauthor-reported number from the model card or eval files (1)No confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.
BenchmarkValueSetting / notesReported byDate
author-reported blind evaluation with 270 experts2.67/4—model author2026-10-07

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

22 fields

Listed in LLMs · MoE

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesHYV3ForCausalLM
Model typehy_v3
Dtypebfloat16
Params by dtypeBF16: 298,786,140,416 · F32: 15,360
Size bandXXL
MoEyes
Experts192
Experts per token8
Context length262,144 (config.json max_position_embeddings)
LicenseApache 2.0 (apache-2.0)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · HYV3ForCausalLM · 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 Hy3: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)717 GB · Cluster (≥640 GB)
FP8 (1 byte/param)359 GB · Multi-GPU node (<640 GB)
INT4 (0.5 bytes/param)179 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

40 variants

Lineage

Original model Family: tencent/Hy3 79 derivatives on the Hub

quantized: 66finetune: 13

Top variants (40 shown, ranked by downloads)

Relation
Quant
Size
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
AngelSlim/Hy3-GGUF quantized GGUF — — apache-2.0 AngelSlim 187 1.2M
bartowski/Hy3-GGUF quantized GGUF — — apache-2.0 bartowski 10 95.2k
YanissAmz/Hy3-295B-A21B-GGUF quantized GGUF — — apache-2.0 YanissAmz 21 54.6k
satgeze/Hy3-1M-GGUF quantized GGUF — — apache-2.0 satgeze 45 33.2k
giannisan/Hy3-ds4-gguf quantized gguf — — — giannisan 30 19.8k
kodelow/Hy3-NVFP4-W4A16 quantized NVFP4 172B XXL apache-2.0 kodelow 15 9.7k
rdtand/Hy3-295B-A21B-PrismaQuant-2.8bit-gguf-vllm quantized gguf — — apache-2.0 rdtand 2 5.8k
avlp12/Hy3-Alis-MLX-Dynamic quantized MLX 295B XXL apache-2.0 avlp12 10 5.3k
RedHatAI/Hy3-NVFP4-FP8 quantized NVFP4 299B XXL mit RedHatAI 7 5.3k
AngelSlim/Hy3-GPTQ-Int4 quantized GPTQ 299B XXL apache-2.0 AngelSlim 10 4.5k
olka-fi/Hy3-MXFP4 quantized MXFP4 163B XXL apache-2.0 olka-fi 6 4.7k
mlx-community/Hy3-oQ2 quantized — 295B XXL apache-2.0 mlx-community 9 4.1k
jcbtc/Hy3-Chadrock-FPX-IFP2-MTP quantized GGUF — — apache-2.0 jcbtc 3 4.3k
cyankiwi/Hy3-AWQ-INT4 quantized AWQ 49.6B XL apache-2.0 cyankiwi 4 4.2k
cyankiwi/Hy3-AWQ-NVFP4 quantized AWQ 167B XXL apache-2.0 cyankiwi 2 4.2k
LibertAIDAI/Hy3-NVFP4 quantized NVFP4 172B XXL other LibertAIDAI 2 3.9k
ox-ox/Hy3-295B-Instruct-w2q3exp-AProjQ8-SExpQ8-OutQ8-MTP-mlx quantized mlx — — apache-2.0 ox-ox 3 3k
Jiunsong/SuperHY3-abliterated-NVFP4 finetune NVFP4 172B XXL apache-2.0 Jiunsong 16 2.1k
mlx-community/Hy3-oQ2e quantized — 295B XXL apache-2.0 mlx-community 4 2.7k
canada-quant/hy3-w4a16-mtp quantized -w4a16 46.8B XL apache-2.0 canada-quant 5 2.6k
LordNeel/Hy3-GGUF quantized GGUF — — apache-2.0 LordNeel 4 2.3k
mlx-community/Hy3-OptiQ-2bit quantized — 295B XXL apache-2.0 mlx-community 6 2k
AtomicChat/Hy3-GGUF quantized GGUF — — apache-2.0 AtomicChat 5 2k
philipjohnbasile/hy3-demolition-mlx-lite-v1 quantized mlx 295B XXL apache-2.0 philipjohnbasile 1 1.8k
tiny-random/hy-v3 finetune — 2.57M S — tiny-random 1 1.8k
philipjohnbasile/hy3-demolition-mlx-reap25-v1 quantized mlx 223B XXL apache-2.0 philipjohnbasile 2 1.7k
rdtand/Hy3-295B-A21B-PrismaQuant-5.3bit-2xSpark-vllm quantized — 194B XXL apache-2.0 rdtand 1 1.7k
yujiepan/hy-v3-tiny-random finetune — 2.57M S — yujiepan 0 1.7k
JANGQ-AI/Hy3-JANG_2K-MTP quantized — 299B XXL other JANGQ-AI 4 1.5k
INCModel2/Hy3-MXFP4-Mixed-CT-AutoRound-Preview quantized MXFP4 163B XXL other INCModel2 1 1.6k
yujiepan/hy3-tiny-random finetune — 2.57M S — yujiepan 0 1.6k
tiyuvta/Hy3-NVFP4 quantized NVFP4 154B XXL apache-2.0 tiyuvta 1 1.5k
pipenetwork/Hy3-REAP75-MLX-4bit quantized MLX 80.3B XL apache-2.0 pipenetwork 4 1.2k
cloudyu/hy3-gguf quantized gguf — — apache-2.0 cloudyu 1 1.1k
philipjohnbasile/hy3-demolition-mlx-reap25-v1-mtp quantized mlx 223B XXL apache-2.0 philipjohnbasile 2 971
unigilby/Hy3-oQ4e quantized — 295B XXL apache-2.0 unigilby 1 978
pipenetwork/Hy3-REAP62-MLX-4bit quantized MLX 116B XXL apache-2.0 pipenetwork 3 857
OpensourceWTF/Hy3-oQ2e-MTPLX-streaming quantized — 8.75B M apache-2.0 OpensourceWTF 3 787
pipenetwork/Hy3-2bit quantized — 295B XXL apache-2.0 pipenetwork 3 771
vroomfondel/Hy3-NVFP4-W4A4 quantized NVFP4 148B XXL apache-2.0 vroomfondel 1 855

Repository changelog

15 commits
changed in the last 7 daysPrice moves: 2026-10-24Other: 2026-10-24all changes

Repository changelog 15 commits

DateCommitSHA
2026-07-20 Update config.json a960ebc
2026-07-17 Upload folder using huggingface_hub 02b95e9
2026-07-16 Upload folder using huggingface_hub 89ffd53
2026-07-16 Upload folder using huggingface_hub e4e6605
2026-07-06 Upload folder using huggingface_hub 716aa72
2026-07-06 Update README.md 6acfeb6
2026-07-06 Update README_CN.md 24fe753
2026-07-06 Update vllm recipes and sglang codebook 45b92d8
2026-07-06 Upload folder using huggingface_hub 62b972e
2026-07-06 Upload folder using huggingface_hub 5137deb
2026-07-05 Upload chat_template.jinja with huggingface_hub 3917583
2026-07-04 Add files using upload-large-folder tool 9feb605
2026-07-04 Add files using upload-large-folder tool d4bf2b3
2026-07-04 Add files using upload-large-folder tool c1131ba
2026-07-02 initial commit bee39e3

All providers

7 providers · 7 endpoints

Where to run 7 providers · 7 endpoints

Hosted endpoints observed for Hy3, 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: $0.08/M.

ProviderModel refContextQuant$/M in$/M outtok/sTTFTToolsStatusObserved
Tencent
openrouter
tencent/hy3 256K fp8 $0.08 $0.33 — — yes live 2026-10-24
DeepInfra
hf
tencent/Hy3 256K — $0.13 $0.53 52.1 674 ms yes live 2026-10-25
Novita
openrouter
tencent/hy3 256K unknown $0.14 $0.58 — — yes live 2026-10-24
GMICloud
openrouter
tencent/hy3 256K bf16 $0.14 $0.58 — — yes live 2026-10-24
Phala
openrouter
tencent/hy3 256K unknown $0.15 $0.64 — — yes live 2026-10-24
AtlasCloud
openrouter
tencent/hy3 256K fp8 $0.20 $0.80 — — yes live 2026-10-24
Featherless
hf
tencent/Hy3 — — — — — — — live 2026-10-25

Articles and coverage

15 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

hy.tencent.com2026-07-07officialHN front pageHN 561 pts · 120 comments197 words
Tencent released Hy3 on July 6, 2026, as an updated version of its Hunyan model available under the Apache 2.0 license. Internal testing shows the model reduced hallucination rates to 5.4% and increased task resolution to 90% in WorkBuddy. Users report significant efficiency gains, with document processing saving 47.4% in token consumption compared to GLM-5.2.written, reviewed, cited

Analysis8

minimaxir.com2026-05-26HN front pageHN 150 pts · 112 comments1,633 words
Hy3 preview from Tencent tops OpenRouter rankings despite DeepSeek V4 Flash offering significantly lower effective pricing of $0.018/1M input tokens via a 2% cache read cost. Hy3 costs $0.066/1M tokens input via SiliconFlow with a restrictive license that could prevent providers from adopting the model. Usage data shows a 98% input, 2% output token breakdown since switching from free to paid status on May 8.written, reviewed, cited
github.com2026-07-06authorityHN 6 pts · 0 comments963 words
Tencent released Hy3, a 295B-parameter Mixture-of-Experts model with 21B active parameters and a 256K context length designed for high cost efficiency. The model reduced its hallucination rate from 12.5% to 5.4% through fine-grained data cleaning and is released under the Apache License 2.0. It outperforms similar-size open-source models and rivals flagship models with 2-5x parameters while showing significant gains in utility across various productivity tasks.written, reviewed, cited
  • Hy3 requires H20-3e or GPUs with larger memory capacity to serve its 295B parameters on 8 GPUs.
  • Hy3 supports GRPO reinforcement learning training via verl or Megatron-LM conversion using NVIDIA Megatron-Bridge.
opencode.ai2026-08-21HN 2 pts · 0 comments1,027 words
OpenCode Go is a $10 per month subscription providing reliable access to curated coding models for agentic workflows. Go Plus costs $40 per month with higher limits, while quotas enforce 20% of the monthly allowance per 5 hours. Some models like Grok 4.7 retain data for 30 days, whereas others retain zero days.written, reviewed, cited
  • Muse Spark 1.3 and 1.2 Contributor models require permission to use prompts for training future Meta models.
  • Grok 4.7 disables the stateful Responses API, Files and Collections, and the Batch API due to ZDR.
  • DeepSeek V4.1 Flash operates under a ZDR agreement valid through October 31, 2026.
kie.ai2026-07-161,864 words
forbes.com2026-07-13
aimadetools.com2026-07-111,553 words
happyrock.cloud2026-07-081,030 words
dev.to2026-07-081,396 words

Community1

twitter.com2026-07-07HN 2 pts · 1 comments
All coverage tracked for this model (15) — append-only, nothing is ever removed
TitleSourcePublishedFirst seenSignals
OpenCode go provides Hy3 with 8X usageopencode.ai2026-08-212026-10-07hn
What Is Hy3? Tencent's 295B Open-Source MoEkie.ai2026-07-162026-10-07exa
Tencent's Hy3 Bets On AI Agents Over Model Sizeforbes.com2026-07-132026-10-07exa
Tencent Hy3: 74.4% SWE-bench With Only 21B Active Parameters (2026)aimadetools.com2026-07-112026-10-07exa
Tencent Hunyuan Hy3 Deep Dive: 295B MoE Fast-Slow Thinking Fusion, Apache 2.0 Open Source, 90% Agent Task Success Rate | HappyRockhappyrock.cloud2026-07-082026-10-07exa
Tencent Hy3: 295B Params, 21B Active — Can You Run It? - DEV Communitydev.to2026-07-082026-10-07exa
Tencent Hunyuan Hy3: Does Smaller Actually Beat Flagship Open Weights · Groundygroundy.com2026-07-082026-10-07exa
Hy3 – 295B MoE model (21B active, 192 experts)twitter.com2026-07-072026-10-07hn
Hy3hy.tencent.com official2026-07-072026-10-07hn
Hy3 (295B A21B), a reasoning and agent model with great cost efficiencygithub.com2026-07-062026-10-07hn
Tencent Hunyuan Hy3 Developer Guide & Benchmarks | Lushbinarylushbinary.com2026-07-062026-10-07exa
Tencent's Hunyuan Hy3: Open-Weight Reasoning Arrivesdigitalapplied.com2026-07-052026-10-07exa
Tencent Hy3 Preview: The 295B Open MoE That Topped OpenRouter — and What Builders Should Actually Know — ChatForestchatforest.com2026-06-142026-10-07exa
The mysterious Hy3 LLM is topping OpenRouter Model Rankings by a large marginminimaxir.com2026-05-262026-10-07hn
Tencent used Anthropic's Claude to fine-tune it's new Hy3 AI modelreuters.com2026-04-292026-10-07hn

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Sources

4 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:35 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 15:03 UTC · Last verified 2026-10-25

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