Qwen VL Max vs Qwen3.5 27B
Compare Qwen VL Max and Qwen3.5 27B side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Qwen VL Max vs Qwen3.5 27B: Overview
Qwen-VL-Max is a proprietary vision-language model developed by Alibaba’s QwenLM team. Released on February 1, 2025, it is the flagship offering in the Qwen-VL family and sits above the VL-Plus tier in capability.
The model supports text and image inputs and provides a context window of up to 131,072 tokens (with a maximum input size of 129,024 tokens), according to Alibaba Cloud Model Studio. While the parameter count for VL-Max has not been publicly disclosed, the broader Qwen2.5-VL series includes open-weight models scaling up to 72B parameters.
Qwen-VL-Max is optimized for advanced multimodal applications such as document parsing, visual reasoning, multilingual analysis, and structured data extraction. Unlike the open Qwen2.5-VL variants, VL-Max is not available as open weights.
Qwen3.5-27B is a multimodal dense hybrid model developed by Alibaba Cloud’s Qwen team and released in February 2026 as a high-precision entry in the Qwen3.5 "Medium" series. Unlike its Mixture-of-Experts (MoE) siblings, the 27B model utilizes a dense architecture combining Gated Delta Networks with a feed-forward structure, activating its full parameter suite for every inference to maximize reliability. This design provides the highest instruction-following and coding accuracy in its class, with a notable IFEval score of 95.0. The model features a native 262K-token context window, extensible to 1M tokens via YaRN (RoPE scaling), and is released under the Apache-2.0 license.
Optimized for agentic workflows, Qwen3.5-27B employs an early-fusion architecture that treats visual and textual data as a unified stream for deep cross-modal reasoning. This unified approach allows the model to excel in technical analysis and software engineering, matching GPT-5-mini with a 72.4% score on SWE-bench Verified. While the larger MoE variants in the family lead in raw knowledge benchmarks, the 27B model offers a stable and high-density alternative for structured data extraction and spatial perception, contributing to the Qwen3.5 family’s generational leap in OCR accuracy over the previous Qwen3-VL series.
Qwen VL Max vs Qwen3.5 27B Comparison Table
| Property | Qwen VL Max | Qwen3.5 27B |
|---|---|---|
| Organization | Qwen | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2025 | Feb 2026 |
| Context Window | 131K | 262K |
| Parameters | 27B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.195 | |
| Output $/1M | $1.56 | |
| 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 | 71.64% | |
| Avg Response Time | 1.98s | |
| Median input tokensincl. image tokens | 1.2K | |
| Median output tokens | 7 | |
| Est. cost / taskon this benchmark | $0.0002 | |
| Defect Detection | 80%(12/15) | |
| Document Understanding | 77.8%(7/9) | |
| Object Counting | 40%(4/10) | |
| Object Understanding | 78.6%(11/14) | |
| Spatial Understanding | 73.7%(14/19) | |
| OCR | ||
| Overall Score | 85.59% | |
| Avg Response Time | 8.51s | |
| Median input tokensincl. image tokens | 126 | |
| Median output tokens | 107 | |
| Est. cost / taskon this benchmark | $0.0002 | |
| Focused Scene OCR | 84.8%(84/99) | |
| Handwritten Math | 100%(10/10) | |
| License Plate Recognition | 93.3%(28/30) | |
| Text Recognition | 80%(24/30) | |
| VQA & Extraction | 83.3%(50/60) | |
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