Claude Opus 4.1 vs Qwen3.6 Plus

Compare Claude Opus 4.1 and Qwen3.6 Plus side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.

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AnthropicClaude Opus 4.1
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QwenQwen3.6 Plus
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Claude Opus 4.1 vs Qwen3.6 Plus: Overview

Claude Opus 4.1

Claude 4.1 Opus, released by Anthropic in August 2025, is the upgraded flagship of the Claude 4 family, building on Opus 4 with stronger reasoning and agentic capabilities. Like its predecessor, it is multimodal and optimized for text, code, and tool use, with support for large context windows suited to multi-file codebases, technical workflows, and long-horizon problem solving.

On benchmarks, Opus 4.1 improves coding performance, reaching ~74.5% on SWE-Bench Verified compared to Opus 4’s ~72.5%. It demonstrates more precise debugging, refactoring, and orchestration of agentic tasks while maintaining similar safety and alignment safeguards. It is best suited for enterprise-scale software development, research automation, and advanced reasoning workflows where reliability and depth of analysis are critical.

Qwen3.6 Plus

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.

Claude Opus 4.1 vs Qwen3.6 Plus Comparison Table

PropertyClaude Opus 4.1Qwen3.6 Plus
OrganizationAnthropicQwen
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateAug 2025Apr 2026
Context Window200K1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$15.00$0.325
Output $/1M$75.00$1.95
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
59.7%
68.66%
Avg Response Time7.09s34.17s
Median input tokensincl. image tokens2.0K1.2K
Median output tokens14047
Est. cost / taskon this benchmark$0.040$0.0005
Defect Detection
73.3%(11/15)
86.7%(13/15)
Document Understanding
88.9%(8/9)
77.8%(7/9)
Object Counting
0%(0/10)
20%(2/10)
Object Understanding
64.3%(9/14)
78.6%(11/14)
Spatial Understanding
63.2%(12/19)
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