What are the strengths of this model?

Massive 2.95 trillion parameters Long 256K context support Designed with advanced reasoning capabilities

What are the weaknesses of this model?

High computational load from massive model Limited use licensing Incomplete features in preview version

What are the best use cases?

Complex logical reasoning tasks Analysis and summarization of large document sets Building advanced knowledge bases

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Tencent AI LabConditional Open

Tencent HunYuan 3 Preview

Name: Tencent HunYuan 3 Preview
Author: Tencent AI Lab

Tencent Hunyuan 3 Preview is a large-scale reasoning model developed by Tencent AI Lab. It has an extremely large scale of approximately 2.95 trillion parameters and supports a wide 256K context window.

Parameters

2950.0B

Context Window

256K

License

https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/LICENSE

Release Date

2026-04-23

API Pricing

API pricing for this model is not yet available

Strengths

・Massive 2.95 trillion parameters
・Long 256K context support
・Designed with advanced reasoning capabilities

Weaknesses

・High computational load from massive model
・Limited use licensing
・Incomplete features in preview version

Use Cases

・Complex logical reasoning tasks
・Analysis and summarization of large document sets
・Building advanced knowledge bases

Deep Analysis

Total Parameters

295B

Mixture-of-Experts (MoE) Architecture

Active Parameters

21B

Optimized for cost-efficiency

Context Window

256K tokens

Supports long-context processing

MATH Benchmark (4-shot)

76.28

Pre-trained model, best among peers

API Input Price

¥1.2 / million tokens

~$0.17, aggressive pricing on Tencent Cloud

Deployment Scale

8x H20-3e GPUs

Recommended for self-hosting

Strengths

・Exceptional STEM reasoning and math performance, outperforming DeepSeek-V3 on key benchmarks with far fewer total parameters.
・Pioneering 'fast & slow thinking' fusion in a single model, with configurable reasoning effort for complex agent tasks.
・Deeply integrated into Tencent's product ecosystem (Yuanbao, WorkBuddy) and optimized for real-world agent workflows of up to 495 steps.

Weaknesses

・As a preview model, it exhibits weak error recovery during tool calls and is sensitive to inference hyperparameters.
・Internal tests show instability in long-chain agent execution, with tasks sometimes being only partially completed.
・No direct API access from major global cloud providers (AWS, GCP, Azure) yet, requiring self-hosting or use of Tencent Cloud/OpenRouter.

Competitor Comparison

Model	Arena	SWE	GPQA	Price
DeepSeek-V3	N/A	N/A	N/A	~$0.27/$1.10 (DeepSeek API)
Kimi-K2	N/A	N/A	N/A	Approx. similar tier
GLM-4.5	N/A	N/A	N/A	Tiered pricing on Zhipu API

Overview

Tencent Hunyuan 3 (Hy3) Preview is Tencent's most powerful open-weights large language model to date, released in April 2026. It represents a strategic pivot for Tencent's AI efforts, rebuilt from the ground up in 90 days under the leadership of Chief AI Scientist Yao Shunyu. As a 295-billion-parameter Mixture-of-Experts model with only 21 billion active parameters, Hy3 prioritizes practical utility and cost-efficiency over sheer scale, aiming to deliver performance competitive with larger models at a fraction of the operational cost. The model's core innovation is its 'fusion reasoning' architecture, which integrates both 'fast' (direct response) and 'slow' (deep chain-of-thought) thinking modes into a single system, controllable via an API parameter. It shows significant strengths in complex STEM reasoning, coding, and—most notably—agentic tasks. Tencent has tightly co-designed the model with its own product ecosystem, including the Yuanbao chatbot and WorkBuddy AI assistant, and has demonstrated its ability to power stable, multi-hundred-step agent workflows in real enterprise scenarios. Positioned as a cost-efficient powerhouse in the competitive Chinese open-weights landscape, Hy3 directly competes with DeepSeek-V3 and Qwen 3. Its release is coupled with aggressive API pricing on Tencent Cloud and immediate integration into Tencent's vast product portfolio, leveraging the company's unparalleled distribution through WeChat and QQ to drive adoption. It is a preview release, signaling more refined and capable versions to come.

Benchmarks & Performance

Hy3 Preview demonstrates leading performance among comparable open-weights models, particularly in math and coding, despite having significantly fewer total parameters than competitors. The benchmarks below compare the *pre-trained base models*. | Benchmark (Metric) | Kimi-K2 Base (1043B) | DeepSeek-V3 Base (671B) | GLM-4.5 Base (355B) | **Hy3 Preview-Base (295B)** | | :--- | :---: | :---: | :---: | :---: | | **MMLU (5-shot)** | **88.24** | 87.68 | 87.73 | 87.42 | | **MATH (4-shot)** | 71.20 | 59.37 | 61.00 | **76.28** | | **GSM8K (4-shot)** | 93.46 | 88.15 | 90.06 | **95.37** | | **LiveCodeBench-v6 (1-shot)** | 30.86 | 29.31 | 27.43 | **34.86** | | **MMMLU (5-shot)** | 77.63 | 79.54 | 79.26 | **80.15** | | **INCLUDE (5-shot)** | 75.66 | 77.86 | 76.27 | **78.64** | *For the instruction-tuned model, Tencent reports strong results on agent-focused benchmarks:* * **SWE-bench Verified (Coding Agent):** 74.4% * **Terminal-Bench 2.0 (Terminal Agent):** 54.4 * **BrowseComp (Search Agent):** 67.1% *Note: Direct scores for Arena Elo, GPQA, and SWE-Bench for the direct competitors (DeepSeek-V3, etc.) are not provided in the source materials for a direct side-by-side, limiting a complete quantitative comparison table.*

Detailed Comparison

**1. vs. DeepSeek-V3:** * **Positioning:** DeepSeek-V3 (405B active parameters in its dense variant) is the current open-weights king on raw reasoning benchmarks. Hy3 Preview is Tencent's efficiency-first challenger. * **Performance:** Hy3's pre-trained base beats DeepSeek-V3's base on critical math and multilingual benchmarks (MATH: 76.28 vs. 59.37, MMMLU: 80.15 vs. 79.54) with less than half the total parameters. * **Cost:** Hy3's API pricing on Tencent Cloud (~$0.17 input/M tokens) is highly competitive, aiming to undercut established players. * **Strengths:** DeepSeek likely retains an edge on absolute knowledge and some pure reasoning tasks. Hy3's advantage is its fusion reasoning and deep product integration for agents. **2. vs. Kimi-K2:** * **Positioning:** Both are Chinese MoE models focused on practical applications. Kimi-K2 has a larger total parameter count (1043B). * **Performance:** The models are closely matched on many English benchmarks (MMLU). Hy3 shows a clear lead in math (MATH, GSM8K) and multilingual (MMMLU, INCLUDE) tasks. * **Ecosystem:** Hy3 benefits from immediate, massive distribution through Tencent's consumer products (Yuanbao, WeChat), an advantage Kimi lacks. **3. vs. Qwen 3:** * **Positioning:** The primary domestic rivalry. Both are seen as the top tier of Chinese open-weights MoE models. * **Performance:** Performance is comparable on common benchmarks, with each model showing strengths in different areas. Hy3's claim is stronger efficiency (fewer activated parameters for similar results). * **Strategy:** Qwen focuses on a broad model family and developer ecosystem. Tencent leverages Hy3 to power and enhance its own vast product suite, creating a unique 'co-design' advantage.

Community Feedback

Developer and industry reaction to Hy3 Preview has been a mix of strategic interest and cautious evaluation: * **Technical Focus:** Articles from 36Kr and Hugging Face blogs highlight the architectural novelty of the 'fusion reasoning' MoE and the significance of achieving strong math/coding scores with only 21B active parameters. The 90-day rebuild timeline is noted as impressive. * **Practical Skepticism:** Early hands-on tests (like those from 36Kr's GeekPark) show the model has clear agentic capabilities but is not yet fully stable for complex, real-world automation, often getting stuck or delivering partial results—consistent with Tencent's 'preview' label. * **Strategic Importance:** The release is widely viewed as Tencent's major move to reclaim its position in the AI race, driven by the high-profile hire of Yao Shunyu. The focus on product integration over pure leaderboard chasing is a noted strategic shift. * **Adoption:** Early adoption is being driven by its integration into Tencent's ecosystem and availability on OpenRouter. Its standing in the broader developer community will depend on the stability and features of the upcoming official release.

Use Cases

1. **AI-Powered Coding & DevOps Assistant (CodeBuddy):** Hy3 is integrated into Tencent's CodeBuddy, where it assists with code generation, debugging, and automation. Its configurable 'high' reasoning mode allows it to tackle complex multi-file tasks and infrastructure-as-code problems. *Choose Hy3 over alternatives when you need a model deeply integrated into an IDE-like environment with support for long-context, multi-step coding workflows and are operating within or targeting the Tencent ecosystem.* 2. **Complex Enterprise Agent Workflows (WorkBuddy):** Hy3 powers WorkBuddy to handle multi-hundred-step office automation: parsing long reports, executing data analysis via Python, coordinating tool calls (email, calendar, docs), and generating structured outputs. Its long context window and instruction following are critical here. *Choose Hy3 for internal enterprise tools where agents must follow intricate company-specific rules and operate on lengthy internal documents, especially if leveraging Tencent Cloud's infrastructure.* 3. **Contextual Learning and Knowledge Retrieval:** Using its dedicated 'context learning' capabilities, Hy3 can be used to build systems that learn from and act upon very large provided contexts (e.g., entire manuals, legal documents, or codebases). It can generate quizzes, extract structured data, or answer complex questions grounded in the provided text. *Choose Hy3 for applications requiring high-fidelity information extraction and instruction following over large, unstructured reference material provided at inference time.* 4. **Cost-Efficient STEM Reasoning & Tutoring:** With its leading performance on math benchmarks (MATH, GSM8K) and configurable reasoning depth, Hy3 can serve as the core engine for a low-cost, high-capability AI tutor for mathematics and sciences. *Choose Hy3 over larger, more expensive models when building applications where mathematical reasoning and step-by-step explanation quality are paramount, and cost-per-query is a major constraint.*

Latest News

* **April 23, 2026:** Tencent officially released and open-sourced the Hy3 Preview model weights on Hugging Face, ModelScope, and GitCode. * **April 23, 2026:** The Hy3 Preview API was launched on Tencent Cloud's TokenHub platform and made available on OpenRouter with a limited-time free access period. * **April 23, 2026:** Pricing was announced: Input at ¥1.2/M tokens, cached input at ¥0.4/M tokens, output at ¥4.0/M tokens. A monthly Token Plan subscription starting at ¥28 was also introduced. * **April 2026:** Hy3 Preview was immediately integrated into multiple Tencent flagship products including Yuanbao (AI chatbot), WorkBuddy (AI agent), CodeBuddy (coding assistant), QQ Browser, and Tencent Docs.

The model's core innovation is its 'fusion reasoning' architecture, which integrates both 'fast' (direct response) and 'slow' (deep chain-of-thought) thinking modes into a single system, controllable via an API parameter. It shows significant strengths in complex STEM reasoning, coding, and—most notably—agentic tasks. Tencent has tightly co-designed the model with its own product ecosystem, including the Yuanbao chatbot and WorkBuddy AI assistant, and has demonstrated its ability to power stable, multi-hundred-step agent workflows in real enterprise scenarios.

Positioned as a cost-efficient powerhouse in the competitive Chinese open-weights landscape, Hy3 directly competes with DeepSeek-V3 and Qwen 3. Its release is coupled with aggressive API pricing on Tencent Cloud and immediate integration into Tencent's vast product portfolio, leveraging the company's unparalleled distribution through WeChat and QQ to drive adoption. It is a preview release, signaling more refined and capable versions to come.

Sources

Analysis generated: 2026-05-23