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Model details

Hy3

Hy3 is a large language model developed by Tencent, reaching general availability on July 6, 2026 after a preview phase in which it became widely used as a coding assistant on developer platforms. The model's design centers on a Mixture-of-Experts architecture that combines a 295B-parameter total capacity with a much smaller active subset, which independent analyses describe as keeping the model light enough to run on a single multi-GPU node while still projecting a strong overall presence. This sparse-activation approach is the defining technical choice behind Hy3, trading the depth of a fully dense model for the efficiency of selecting only a fraction of the experts per token.

In third-party comparisons with competing open-weight models such as GLM-5.2, Hy3 is positioned primarily as a coding-oriented system aimed at engineers evaluating open-weight large language models for internal deployment. Its preview reception highlighted practical developer value, with reviewers describing it as a go-to assistant for building software more efficiently, and that coding focus carried forward into the general availability release. The combination of a large expert pool, modest active parameters, and a coding-first emphasis makes Hy3 a fit for teams that want an open-weight model capable of substantial reasoning capacity without the compute footprint of a fully dense 295B-parameter model.

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Provider
AIHubMix
Model key
hy3
Release date
Jul 6, 2026
Last updated
Jul 6, 2026
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.1562
Output token cost
$0.6248

Limits

Input tokens
192,000 tokens
Output tokens
128,000 tokens
Context window
256,000 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about Hy3

AIHubMix

CoveragePreview

Tencent's Hy Team released Hy3 preview as the first model trained on their rebuilt infrastructure, published on the official tencent/Hy3-preview Hugging Face model card page. The model is a Mixture-of-Experts (MoE) architecture with 295B total parameters and 21B activated parameters (plus 3.8B MTP layer parameters), bu Per the model card's own highlights, Hy3 preview targets systematic gains across reasoning, instruction following, context learning, coding, and agent tasks. It reports competitive performance on STEM/reasoning benchmarks such as FrontierScience-Olympiad, IMOAnswerBench, the Tsinghua Qiuzhen College Math PhD qualifying

AIHubMix

CoveragePreview

Tencent Cloud's techpedia entry titled "Hy3 preview: The First Step of Tencent Hunyuan's Rebuild" confirms that Tencent Hunyuan open-sourced the Hy3 preview language model on 2026-04-23, after rebuilding its pretraining and reinforcement learning infrastructure in February. The page describes Hy3 preview as a hybrid fa The same entry lists three guiding principles for practicality: systematic capabilities (avoiding lopsided models by jointly coordinating reasoning, long-form, instruction, dialogue, code, and tools), evaluation authenticity (using self-built questions, latest exams, and human evaluation rather than easily gamed leader

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