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qwen/qwen3-vl-235b-a22b-instruct
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Qwen3 VL 235B A22B Instruct

Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table extraction, multilingual OCR). The series emphasizes robust perception (recognition of diverse real-world and synthetic categories), spatial understanding (2D/3D grounding), and long-form visual comprehension, with competitive results on public multimodal benchmarks for both perception and reasoning. Beyond analysis, Qwen3-VL supports agentic interaction and tool use: it can follow complex instructions over multi-image, multi-turn dialogues; align text to video timelines for precise temporal queries; and operate GUI elements for automation tasks. The models also enable visual coding workflows—turning sketches or mockups into code and assisting with UI debugging—while maintaining strong text-only performance comparable to the flagship Qwen3 language models. This makes Qwen3-VL suitable for production scenarios spanning document AI, multilingual OCR, software/UI assistance, spatial/embodied tasks, and research on vision-language agents.

9/23/2025
262,144 tokens
#65 Vision (Overall)

Specifications

Modalities

Input
text
image
Output
text

Supported Parameters

frequency_penalty
logit_bias
max_tokens
min_p
presence_penalty
repetition_penalty
response_format
seed
stop
structured_outputs
temperature
tool_choice
tools
top_k
top_p

Frequently asked questions

Is Qwen3 VL 235B A22B Instruct available on FastMetal?
Not at the moment. Qwen3.8 Max (0902), from the same lab, is available on the FastMetal API today.
What is the context window of Qwen3 VL 235B A22B Instruct?
262,144 tokens, shared between the prompt and the response.
How does Qwen3 VL 235B A22B Instruct rank?
#100 on the public arena's Japanese board (ELO 1,371). Ranks move as the leaderboard is updated.

Leaderboard

Text
OverallELO: 1,414
#128
JapaneseELO: 1,371
#100
ChineseELO: 1,454
#118
KoreanELO: 1,377
#90
EnglishELO: 1,426
#126
frenchELO: 1,444
#100
germanELO: 1,410
#105
spanishELO: 1,418
#99
russianELO: 1,405
#127
CodingELO: 1,465
#118
MathELO: 1,408
#132
Creative WritingELO: 1,357
#156
Instruction FollowingELO: 1,410
#117
Hard PromptsELO: 1,438
#120
Multi-TurnELO: 1,425
#113
Vision
OverallELO: 1,215
#65