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Qwen VL Max vs Qwen3.6 27B

Compare Qwen VL Max and Qwen3.6 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 Qwen3.6 27B Comparison Table

Evals updated July 24, 2026Pricing updated July 26, 2026

PropertyQwen VL MaxQwen3.6 27B
OrganizationQwenQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateFeb 2025Apr 2026
Context Window131K262K
Parameters27B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$0.300
Output $/1M$2.00
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
Classification
Document Question Answering
Image Tagging
Multi-Label Classification
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Object Detection
Video Classification
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision

Qwen VL Max vs Qwen3.6 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.

Qwen3.6 27B

Qwen3.6-27B is a dense 27-billion-parameter multimodal language model developed by Alibaba's Qwen team and released on April 22, 2026. It combines a causal language model with an integrated vision encoder, supporting text, image, and video inputs natively. The architecture employs a hybrid attention design that interleaves Gated DeltaNet linear attention blocks with standard Gated Attention layers across 64 transformer layers with a hidden dimension of 5,120. Unlike Mixture-of-Experts variants in the Qwen3.6 family, all 27 billion parameters are active on every inference pass, simplifying deployment and quantization. The model supports a native context window of 262,144 tokens, extensible to approximately 1,010,000 tokens via YaRN scaling. It is released under the Apache 2.0 license with open weights available on Hugging Face and ModelScope.

The model introduces two notable capabilities relative to prior Qwen releases: enhanced agentic coding support covering frontend workflows and repository-level reasoning, and a Thinking Preservation mechanism that retains chain-of-thought reasoning context across multi-turn conversation history to reduce redundant token generation in iterative agent sessions. It supports both a thinking mode for multi-step reasoning and a non-thinking mode for faster responses within a single model. On coding benchmarks, Qwen reports scores of 77.2 on SWE-bench Verified, 59.3 on Terminal-Bench 2.0, and 48.2 on SkillsBench. Vision capabilities include chart understanding (CharXiv RQ: 78.4), OCR (CC-OCR: 81.2), and video understanding (VideoMME with subtitles: 87.7).