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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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QwenQwen VL Max
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Qwen VL Max vs Qwen3.5 27B: Overview

Qwen VL Max

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

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

PropertyQwen VL MaxQwen3.5 27B
OrganizationQwenQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateFeb 2025Feb 2026
Context Window131K262K
Parameters27B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$0.195
Output $/1M$1.56
Vision Tasks
CaptioningDemoDemo
Object Detection
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
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 Time1.98s
Median input tokensincl. image tokens1.2K
Median output tokens7
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 Time8.51s
Median input tokensincl. image tokens126
Median output tokens107
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