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Qwen: Qwen3 VL 30B A3B Instruct

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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
qwen/qwen3-vl-30b-a3b-instruct
OpenRouter ID
qwen/qwen3-vl-30b-a3b-instruct
Canonical slug
qwen/qwen3-vl-30b-a3b-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

Qwen3-VL-30B-A3B-Instruct is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Instruct variant optimizes instruction-following for general multimodal tasks. It excels in perception of real-world/synthetic categories, 2D/3D spatial grounding, and long-form visual comprehension, achieving competitive multimodal benchmark results. For agentic use, it handles multi-image multi-turn instructions, video timeline alignments, GUI automation, and visual coding from sketches to debugged UI. Text performance matches flagship Qwen3 models, suiting document AI, OCR, UI assistance, spatial tasks, and agent research.

Raw fields snapshot

{
  "id": "qwen/qwen3-vl-30b-a3b-instruct",
  "name": "Qwen: Qwen3 VL 30B A3B Instruct",
  "description": "Qwen3-VL-30B-A3B-Instruct is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Instruct variant optimizes instruction-following for general multimodal tasks. It excels in perception of real-world/synthetic categories, 2D/3D spatial grounding, and long-form visual comprehension, achieving competitive multimodal benchmark results. For agentic use, it handles multi-image multi-turn instructions, video timeline alignments, GUI automation, and visual coding from sketches to debugged UI. Text performance matches flagship Qwen3 models, suiting document AI, OCR, UI assistance, spatial tasks, and agent research.",
  "created": 1759794476,
  "canonical_slug": "qwen/qwen3-vl-30b-a3b-instruct",
  "hugging_face_id": "Qwen/Qwen3-VL-30B-A3B-Instruct",
  "source_type": "openrouter_only",
  "context_length": 131072,
  "max_completion_tokens": 32768,
  "is_moderated": false,
  "architecture": {
    "modality": "text+image->text",
    "input_modalities": [
      "text",
      "image"
    ],
    "output_modalities": [
      "text"
    ],
    "tokenizer": "Qwen3",
    "instruct_type": null
  },
  "input_modalities": [
    "text",
    "image"
  ],
  "output_modalities": [
    "text"
  ],
  "modality": "text+image->text",
  "tokenizer": "Qwen3",
  "instruct_type": null,
  "supported_parameters": [
    "frequency_penalty",
    "max_tokens",
    "min_p",
    "presence_penalty",
    "repetition_penalty",
    "response_format",
    "seed",
    "stop",
    "structured_outputs",
    "temperature",
    "tool_choice",
    "tools",
    "top_k",
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  ],
  "default_parameters": {
    "temperature": 0.7,
    "top_p": 0.8,
    "frequency_penalty": null
  },
  "per_request_limits": null,
  "top_provider": {
    "context_length": 131072,
    "max_completion_tokens": 32768,
    "is_moderated": false
  },
  "pricing": {
    "prompt": "0.00000013",
    "completion": "0.00000052"
  },
  "PPM": {
    "prompt": 0.13,
    "completion": 0.52
  },
  "openrouter_raw": {
    "id": "qwen/qwen3-vl-30b-a3b-instruct",
    "canonical_slug": "qwen/qwen3-vl-30b-a3b-instruct",
    "hugging_face_id": "Qwen/Qwen3-VL-30B-A3B-Instruct",
    "name": "Qwen: Qwen3 VL 30B A3B Instruct",
    "created": 1759794476,
    "description": "Qwen3-VL-30B-A3B-Instruct is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Instruct variant optimizes instruction-following for general multimodal tasks. It excels in perception of real-world/synthetic categories, 2D/3D spatial grounding, and long-form visual comprehension, achieving competitive multimodal benchmark results. For agentic use, it handles multi-image multi-turn instructions, video timeline alignments, GUI automation, and visual coding from sketches to debugged UI. Text performance matches flagship Qwen3 models, suiting document AI, OCR, UI assistance, spatial tasks, and agent research.",
    "context_length": 131072,
    "architecture": {
      "modality": "text+image->text",
      "input_modalities": [
        "text",
        "image"
      ],
      "output_modalities": [
        "text"
      ],
      "tokenizer": "Qwen3",
      "instruct_type": null
    },
    "pricing": {
      "prompt": "0.00000013",
      "completion": "0.00000052"
    },
    "top_provider": {
      "context_length": 131072,
      "max_completion_tokens": 32768,
      "is_moderated": false
    },
    "per_request_limits": null,
    "supported_parameters": [
      "frequency_penalty",
      "max_tokens",
      "min_p",
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    ],
    "default_parameters": {
      "temperature": 0.7,
      "top_p": 0.8,
      "frequency_penalty": null
    },
    "expiration_date": null
  }
}
Qwen: Qwen3 VL 30B A3B Instruct · NNZen