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Claude 3.7 Sonnet vs Qwen3.5 397B A17B

Compare Claude 3.7 Sonnet and Qwen3.5 397B A17B side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.

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AnthropicClaude 3.7 Sonnet

Claude 3.7 Sonnet is deprecated and can no longer be run. Details and evals are still available on its model page.

QwenQwen3.5 397B A17B
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Claude 3.7 Sonnet vs Qwen3.5 397B A17B Comparison Table

Evals updated August 6, 2026Pricing updated August 7, 2026

PropertyClaude 3.7 SonnetQwen3.5 397B A17B
OrganizationAnthropicQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateFeb 2024Feb 2026
Context Window200K262K
Parameters397B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$0.390
Output $/1M$2.34
Vision Tasks
CaptioningDemo
Chart Question Answering
Classification
Document Question Answering
Image Tagging
Multi-Label Classification
Object Detection
OCRDemo
Vision Language
Visual Question AnsweringDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision

Claude 3.7 Sonnet vs Qwen3.5 397B A17B: Overview

Claude 3.7 Sonnet

Claude 3.7 Sonnet, released by Anthropic in February 2025, is the company’s first hybrid reasoning model, combining fast response generation with an optional “extended thinking mode” that reveals longer, step-by-step reasoning. Like its predecessors, it is multimodal, handling both text and images, but expands its usability with up to 200,000 input tokens and up to 128,000 output tokens (64K generally available, 128K in beta). This makes it well-suited for analyzing large documents, codebases, or multi-turn conversations.

Typical applications include software development, research workflows, extended reasoning tasks, and enterprise-scale knowledge work where a trade-off between speed and visible reasoning is valuable.

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.