Qwen Qwen3 Vl 235B A22B Instruct, developed by Alibaba Qwen, features 235B parameters and 262k-token context window. 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. Available at $0.2/1M tokens.
Visit Qwen: Qwen3 VL 235B A22B InstructAI-Powered
Leverages advanced AI technology to deliver cutting-edge capabilities and results.
Fast & Efficient
Optimized performance ensures quick results without compromising on quality.
Purpose-Built
Specifically designed for llms tasks and workflows.
Qwen Model Timeline
262k tokens context
256k tokens context
131k tokens context
131k tokens context
262k tokens context
262k tokens context
262k tokens context
256k tokens context
128k tokens context
128k tokens context
128k tokens context
262k tokens context
262k tokens context
1,000k tokens context
1,000k tokens context
33k tokens context
160k tokens context
262k tokens context
262k tokens context
262k tokens context
262k tokens context
262k tokens context
262k tokens context
41k tokens context
41k tokens context
32k tokens context
41k tokens context
41k tokens context
41k tokens context
33k tokens context
16k tokens context
33k tokens context
8k tokens context
131k tokens context
1,000k tokens context
33k tokens context
131k tokens context
33k tokens context
33k tokens context
33k tokens context
33k tokens context
33k tokens context
33k tokens context
Specifications
AI Evaluation
Combines language understanding with search capabilities. Excels at finding and synthesizing information from various sources.
Pros
- Competitive pricing ($0.2/1M)
- 262k token context window
- Large-scale 235B architecture
- Strong code generation and debugging
Cons
- Requires substantial compute
- May lack creative flair
Related Tools
FLUX
FLUX, from Black Forest Labs, is a family of high-quality open and commercial image generation models prized for photorealism and prompt adherence. Widely integrated across third-party tools and APIs, it has become a default backbone for image generation.
Claude Opus 5.5
Claude Opus 5.5, launched 22 September 2026, is Anthropic's current Opus-line flagship, replacing Opus 5. It performs at roughly the level of Claude Fable 5.1 on most work while costing 40% less to run than Opus 5, at $4 input / $20 output per million tokens (both 20% down, cache reads cut 60% to $0.20). It leads on Terminal-Bench 4.0, GDPval-AA v2.1 and Humanity's Last Exam, and is the first Opus model to ship with Fable-5.1-class cybersecurity, biology and distillation safeguards.
Claude Sonnet 5.5
Claude Sonnet 5.5, released 28 September 2026, is the second model in Anthropic's Claude 5.5 family and the faster, lower-cost complement to Claude Opus 5.5. It keeps Sonnet 5's $2 input / $10 output per million tokens ($0.20 cache reads) while generating output over 30% faster, and scores 70.6% on Terminal-Bench 4.0, above Opus 5.5's 66.4%. It has a 1M-token context window, 128K max output, adaptive thinking, and is the first Sonnet with cyber and anti-distillation classifiers.
