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Claude Sonnet 4.5 vs Qwen3.5 397B A17B

Compare Claude Sonnet 4.5 and Qwen3.5 397B A17B side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

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AnthropicClaude Sonnet 4.5
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QwenQwen3.5 397B A17B
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Claude Sonnet 4.5 vs Qwen3.5 397B A17B: Overview

Claude Sonnet 4.5

Claude Sonnet 4.5, released by Anthropic in September 2025, is the company’s most advanced Sonnet-series model, built for high-performance reasoning, coding, and long-horizon agentic workflows. It is a multimodal system that accepts both text and images, with a 200,000-token context window designed for handling large documents and extended interactions. Anthropic highlights its improvements in reliability, reduced sycophancy, and alignment, making it suitable for sustained enterprise use.

The model delivers strong results in coding and autonomous workflows, achieving 61.4% on the OSWorld benchmark and leading performance on SWE-bench Verified. It introduces infrastructure features such as a memory tool (beta), checkpointing for Claude Code, parallel tool use, and tighter integration with VS Code. Compared to Opus, which targets broader reasoning, Sonnet 4.5 is optimized for structured, long-duration tasks. Positioned against leading offerings from OpenAI and Google, it is aimed at enterprise automation, software engineering, and research-intensive applications.

Qwen3.5 397B A17B

Qwen3.5-397B-A17B is a 397B-parameter (17B active) open-weight multimodal model developed by Alibaba’s Qwen team, released on 2026-02-16 under Apache-2.0. It supports text and image inputs with text outputs, combining a sparse Mixture-of-Experts architecture with Gated Delta Networks for efficient scaling. The model provides native vision-language reasoning and a large ~262K token context window, extendable to ~1M tokens.

As the first open-weight release in the Qwen3.5 family, it positions itself as a high-capacity, long-context alternative in the large vision-language space, balancing scale and efficiency via sparse activation. It is designed for advanced reasoning, coding, agent workflows, and multimodal understanding tasks.

Claude Sonnet 4.5 vs Qwen3.5 397B A17B Comparison Table

PropertyClaude Sonnet 4.5Qwen3.5 397B A17B
OrganizationAnthropicQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateSep 2025Feb 2026
Context Window200K262K
Parameters397B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$3.00$0.385
Output $/1M$15.00$2.45
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%
Visual Understanding
Overall Score
59.7%
58.21%
Avg Response Time5.67s56.61s
Median input tokensincl. image tokens2.2K1.1K
Median output tokens18254
Est. cost / taskon this benchmark$0.0092$0.0006
Defect Detection
73.3%(11/15)
66.7%(10/15)
Document Understanding
77.8%(7/9)
77.8%(7/9)
Object Counting
10%(1/10)
20%(2/10)
Object Understanding
64.3%(9/14)
64.3%(9/14)
Spatial Understanding
63.2%(12/19)
57.9%(11/19)
OCR
Overall Score
67.25%
68.56%
Avg Response Time3.93s7.45s
Median input tokensincl. image tokens735122
Median output tokens11520
Est. cost / taskon this benchmark$0.0039$0.0001
Focused Scene OCR
71.7%(71/99)
57.6%(57/99)
Handwritten Math
20%(2/10)
80%(8/10)
License Plate Recognition
53.3%(16/30)
100%(30/30)
Text Recognition
66.7%(20/30)
70%(21/30)
VQA & Extraction
75%(45/60)
68.3%(41/60)

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