Qwen3.6 Plus vs Qwen3 VL 235B A22B Instruct
Compare Qwen3.6 Plus and Qwen3 VL 235B A22B Instruct side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Qwen3.6 Plus vs Qwen3 VL 235B A22B Instruct: Overview
Qwen3.6 Plus is a flagship model in Alibaba’s Qwen Plus series, designed for agentic workflows, coding, and multi-step reasoning. It supports a 1 million token context window and up to 65,536 output tokens, with built-in reasoning capabilities. The model is available as a hosted, proprietary API through Alibaba Cloud.
Compared to Qwen3.5, it improves reliability in multi-step execution and frontend code generation, with stronger performance on agentic coding tasks. It also supports document and image understanding, though its vision capabilities are more limited than dedicated Qwen-VL models. Qwen3.6 Plus is part of a broader Qwen ecosystem that includes both closed-source APIs and open-weight models.
Qwen3 VL 235B A22B Instruct is a flagship multimodal vision-language model developed by Qwen (Alibaba Cloud), designed for instruction-following tasks that combine advanced text generation with visual understanding. It serves as a high-end open-weight model for developers and researchers building multimodal AI systems that require strong reasoning, perception, and long-context capabilities.
The model supports interleaved text and image inputs, very long context windows (up to roughly 256K tokens), and efficient inference through a mixture-of-experts architecture with about 22B active parameters out of 235B total. In today’s landscape, it competes with top-tier proprietary vision-language models while offering the advantages of open weights and flexible deployment. Typical applications include multimodal assistants, document and image analysis, visual reasoning, and large-context instruction-based workflows.
Qwen3.6 Plus vs Qwen3 VL 235B A22B Instruct Comparison Table
| Property | Qwen3.6 Plus | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Sep 2025 |
| Context Window | 1.0M | 256K |
| Parameters | 235B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.325 | $0.200 |
| Output $/1M | $1.95 | $0.880 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | ||
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 68.66% | |
| Avg Response Time | 34.17s | |
| Median input tokensincl. image tokens | 1.2K | |
| Median output tokens | 47 | |
| Est. cost / taskon this benchmark | $0.0005 | |
| Defect Detection | 86.7%(13/15) | |
| Document Understanding | 77.8%(7/9) | |
| Object Counting | 20%(2/10) | |
| Object Understanding | 78.6%(11/14) | |
| Spatial Understanding | 68.4%(13/19) | |
| OCR | ||
| Overall Score | 58.52% | |
| Avg Response Time | 5.49s | |
| Median input tokensincl. image tokens | 124 | |
| Median output tokens | 18 | |
| Est. cost / taskon this benchmark | $0.0001 | |
| Focused Scene OCR | 76.8%(76/99) | |
| Handwritten Math | 80%(8/10) | |
| License Plate Recognition | 13.3%(4/30) | |
| Text Recognition | 50%(15/30) | |
| VQA & Extraction | 51.7%(31/60) | |
Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology