Tencent’s open-source MoE language model: 389B parameters (52B active), context up to 256K, strong at reasoning, math and code; comparable to LLaMA 3.1-405B.
Context window
256K (pre-trained) / 128K (instruct)
tokens
Parameters
389B (52B aktywne)
parameters
Release date
5 November 2024
Access:DownloadAPIHostedDeployment:💻 Local☁ Cloud
Overview
Applications
Access & deployment
DownloadAPIHosted
LocalCloud
Weights: Open weights
Key parameters
📏 Context: 256K (pre-trained) / 128K (instruct)
🧩 Parameters: 389B (52B aktywne)
✓ Tools · ✓ Fine-tuning
📥 Input: text
Technical specification
Context window
256K (pre-trained) / 128K (instruct)
tokens
Parameters
389B (52B aktywne)
parameters
License
Tencent Hunyuan Community License Agreement
Hardware requirements
An open-weights model (Hugging Face) with code on GitHub; given its 389B total parameters, local inference requires a multi-GPU cluster with very large VRAM. Also available via Tencent Cloud.
Features:✓ Tool use✓ Fine-tuning
Modalities
⬇ Input
text
⬆ Output
textcode
Capabilities and applications
Native model capabilities
Reasoning
The model's ability to reason logically and solve complex problems.
Category: reasoning
Advanced reasoning
The ability to perform multi-step, structured reasoning: analysing problems, planning steps, and drawing conclusions from hypotheses. Reasoning-first models (e.g. GPT-5.1 Thinking) dedicate a portion of inference to chains of thought before responding.
Category: reasoning
Multi-step reasoning
Carrying out multi-step chains of reasoning across long, complex tasks.
Category: reasoning
Mathematical reasoning
The model's ability to solve mathematical tasks requiring multi-step reasoning — equations, proofs, combinatorics, geometry, calculus and competition-level problems.
Category: reasoning
Coding
Generating, analysing and modifying code in many programming languages. Covers writing functions, debugging, refactoring, code review, and creating tests. Measured by benchmarks such as HumanEval and SWE-bench.
Category: coding
Long context
Support for large context windows — tens to hundreds of thousands (or millions) of input tokens. Enables analysis of entire codebases, long documents, and many parallel conversations without losing earlier information. GPT-5.1 supports 400,000 tokens.
Category: language
Multilingual
Competence in many natural languages (from a few to over a hundred): understanding, generation, translation, and code-switching within a single conversation. Frontier models support a wide range of languages with comparable quality.
Category: language
Tool use
The model's ability to call external functions, APIs and tools during a conversation: calculator, search engine, code editor, database. The model decides when and how to use a tool and interprets its result.
Category: planning
Instruction following
Precisely following instructions contained in the prompt: response format, length, style, constraints (e.g. 'reply in six words'). GPT-5.1 significantly improved this capability compared to GPT-5.
Category: language
Structured output
Producing data in structured formats such as JSON.
Category: structured_generation
Natural conversation
Conducting a conversation with a tone close to human: a warmer voice, empathy in emotional responses, humour, and avoiding the stiff 'AI assistant jargon'. Introduced as a deliberate improvement in GPT-5.1 Instant.
Category: language
Benchmark results
4 benchmarks
MMLU
accuracy · 5-shot (instruct version)
89.9%
📄 Raport techniczny Hunyuan-Large (arXiv:2411.02265) / GitHub
Score for the instruct version.
MATH
accuracy · instruct version
77.4%
📄 Raport techniczny Hunyuan-Large (arXiv:2411.02265) / GitHub
Score for the instruct version.
GSM8K
accuracy · pre-trained version
92.8%
📄 Raport techniczny Hunyuan-Large (arXiv:2411.02265) / GitHub
Score for the base (pre-trained) model.
HumanEval
pass@1 · code generation
90.0%
📄 Raport techniczny Hunyuan-Large (arXiv:2411.02265) / GitHub
Code generation result.
Technical architecture
Core Architecture
Training Techniques
