Claude Opus 4.6 vs Qwen3.5 397B A17B

Compare Claude Opus 4.6 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 Opus 4.6
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QwenQwen3.5 397B A17B
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Claude Opus 4.6 vs Qwen3.5 397B A17B: 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.

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 Opus 4.6 vs Qwen3.5 397B A17B Comparison Table

PropertyClaude Opus 4.6 Qwen3.5 397B A17B
OrganizationAnthropicQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateFeb 2026Feb 2026
Context Window1.0M262K
Parameters397B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$5.00$0.385
Output $/1M$25.00$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%
Overall Score
64.18%
58.21%
Avg Response Time23.35s56.61s
Median input tokensincl. image tokens2.2K1.1K
Median output tokens13054
Est. cost / taskon this benchmark$0.014$0.0006
Defect Detection
73.3%(11/15)
66.7%(10/15)
Document Understanding
77.8%(7/9)
77.8%(7/9)
Object Counting
20%(2/10)
20%(2/10)
Object Understanding
71.4%(10/14)
64.3%(9/14)
Spatial Understanding
68.4%(13/19)
57.9%(11/19)

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