Claude Opus 4.6 vs Qwen VL Max

Compare Claude Opus 4.6 and Qwen VL Max side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.

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AnthropicClaude Opus 4.6
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QwenQwen VL Max
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Models in this comparison

Claude Opus 4.6 vs Qwen VL Max: Overview

Claude Opus 4.6

Claude Opus 4.6 is the flagship large language model from Anthropic, released on 2026-02-05 for advanced reasoning, complex coding, and enterprise agent workflows. It supports text and image inputs via API, offers a 200K-token standard context window with a 1M-token beta option, and enables outputs up to 128K tokens, with adaptive reasoning and context compaction for sustained tasks.

As of 2026-02-17, Anthropic also released Claude Sonnet 4.6, extending the 1M-token context window to a broader tier. Opus remains positioned for maximum depth and benchmark performance, while Sonnet 4.6 brings long-context capability to more cost- and latency-sensitive production use cases.

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.

Claude Opus 4.6 vs Qwen VL Max Comparison Table

PropertyClaude Opus 4.6 Qwen VL Max
OrganizationAnthropicQwen
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateFeb 2026Feb 2025
Context Window1.0M131K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$5.00
Output $/1M$25.00
Vision Tasks
CaptioningDemoDemo
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
ClassificationDemo
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
64.18%
Avg Response Time23.35s
Median input tokensincl. image tokens2.2K
Median output tokens130
Est. cost / taskon this benchmark$0.014
Defect Detection
73.3%(11/15)
Document Understanding
77.8%(7/9)
Object Counting
20%(2/10)
Object Understanding
71.4%(10/14)
Spatial Understanding
68.4%(13/19)

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