Tencent: Hunyuan A13B Instruct

Server-rendered model summary page for indexing/share previews. Use the interactive explorer for full filtering and comparison.

Match confidence: UnmatchedSource type: openrouter_only

Context window

131.1K

Arena overall rank

—

Input price

$0.000 / 1M

Output price

$0.000 / 1M

Identifiers & provenance

Primary ID: tencent/hunyuan-a13b-instruct
OpenRouter ID: tencent/hunyuan-a13b-instruct
Canonical slug: tencent/hunyuan-a13b-instruct

Source semantics

Arena rank is a human-preference leaderboard signal, not a universal truth metric.
OpenRouter usage/popularity reflects adoption/traffic, not benchmark quality.
Pricing fields may differ by provider and can include extra modes beyond prompt/completion.

Read more on Methodology & data sources.

Description

Hunyuan-A13B is a 13B active parameter Mixture-of-Experts (MoE) language model developed by Tencent, with a total parameter count of 80B and support for reasoning via Chain-of-Thought. It offers competitive benchmark performance across mathematics, science, coding, and multi-turn reasoning tasks, while maintaining high inference efficiency via Grouped Query Attention (GQA) and quantization support (FP8, GPTQ, etc.).

Raw fields snapshot

{
  "id": "tencent/hunyuan-a13b-instruct",
  "name": "Tencent: Hunyuan A13B Instruct",
  "description": "Hunyuan-A13B is a 13B active parameter Mixture-of-Experts (MoE) language model developed by Tencent, with a total parameter count of 80B and support for reasoning via Chain-of-Thought. It offers competitive benchmark performance across mathematics, science, coding, and multi-turn reasoning tasks, while maintaining high inference efficiency via Grouped Query Attention (GQA) and quantization support (FP8, GPTQ, etc.).",
  "created": 1751987664,
  "canonical_slug": "tencent/hunyuan-a13b-instruct",
  "hugging_face_id": "tencent/Hunyuan-A13B-Instruct",
  "source_type": "openrouter_only",
  "context_length": 131072,
  "max_completion_tokens": 131072,
  "is_moderated": false,
  "architecture": {
    "modality": "text->text",
    "input_modalities": [
      "text"
    ],
    "output_modalities": [
      "text"
    ],
    "tokenizer": "Other",
    "instruct_type": null
  },
  "input_modalities": [
    "text"
  ],
  "output_modalities": [
    "text"
  ],
  "modality": "text->text",
  "tokenizer": "Other",
  "instruct_type": null,
  "supported_parameters": [
    "frequency_penalty",
    "include_reasoning",
    "reasoning",
    "response_format",
    "structured_outputs",
    "temperature",
    "top_k",
    "top_p"
  ],
  "default_parameters": {},
  "per_request_limits": null,
  "top_provider": {
    "context_length": 131072,
    "max_completion_tokens": 131072,
    "is_moderated": false
  },
  "pricing": {
    "prompt": "0.00000014",
    "completion": "0.00000057"
  },
  "PPM": {
    "prompt": 0.14,
    "completion": 0.57
  },
  "openrouter_raw": {
    "id": "tencent/hunyuan-a13b-instruct",
    "canonical_slug": "tencent/hunyuan-a13b-instruct",
    "hugging_face_id": "tencent/Hunyuan-A13B-Instruct",
    "name": "Tencent: Hunyuan A13B Instruct",
    "created": 1751987664,
    "description": "Hunyuan-A13B is a 13B active parameter Mixture-of-Experts (MoE) language model developed by Tencent, with a total parameter count of 80B and support for reasoning via Chain-of-Thought. It offers competitive benchmark performance across mathematics, science, coding, and multi-turn reasoning tasks, while maintaining high inference efficiency via Grouped Query Attention (GQA) and quantization support (FP8, GPTQ, etc.).",
    "context_length": 131072,
    "architecture": {
      "modality": "text->text",
      "input_modalities": [
        "text"
      ],
      "output_modalities": [
        "text"
      ],
      "tokenizer": "Other",
      "instruct_type": null
    },
    "pricing": {
      "prompt": "0.00000014",
      "completion": "0.00000057"
    },
    "top_provider": {
      "context_length": 131072,
      "max_completion_tokens": 131072,
      "is_moderated": false
    },
    "per_request_limits": null,
    "supported_parameters": [
      "frequency_penalty",
      "include_reasoning",
      "reasoning",
      "response_format",
      "structured_outputs",
      "temperature",
      "top_k",
      "top_p"
    ],
    "default_parameters": {},
    "expiration_date": null
  }
}