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Claude Sonnet 5 vs Qwen3.5 9b

Compare Claude Sonnet 5 and Qwen3.5 9b side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.

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AnthropicClaude Sonnet 5
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QwenQwen3.5 9b
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Claude Sonnet 5 vs Qwen3.5 9b: Overview

Claude Sonnet 5

Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.

The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.

Qwen3.5 9b

Qwen3.5-9B is a 9-billion-parameter multimodal foundation model developed by Alibaba Cloud's Qwen team, released on March 2, 2026 as part of the Qwen3.5 model family. Designed for efficient multimodal reasoning and long-context language tasks, it notably outperforms the older Qwen3-30B, a model more than three times its size, on key benchmarks including GPQA Diamond, IFEval, and LongBench.

The model supports vision-language inputs through an early-fusion multimodal architecture built on a dense hybrid foundation of Gated Delta Networks and Gated Attention. It can also operate in a text-only mode by skipping the vision encoder during inference. It provides a 262,144-token context window (extensible to ~1M tokens via YaRN) and is released under the Apache License 2.0. Within the current AI landscape, Qwen3.5-9B offers a strong balance of capability and efficiency, making it well-suited for multimodal assistants, document analysis, long-context reasoning, and developer-deployed agentic systems.

Claude Sonnet 5 vs Qwen3.5 9b Comparison Table

PropertyClaude Sonnet 5Qwen3.5 9b
OrganizationAnthropicQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateJun 2026Mar 2026
Context Window1.0M262K
Parameters9B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$2.00$0.100
Output $/1M$10.00$0.150
Vision Tasks
CaptioningDemoDemo
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
ClassificationDemo
Document Question Answering
Multi-Label Classification
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
70.15%
71.64%
Avg Response Time3.90s8.99s
Median input tokensincl. image tokens2.1K
Median output tokens61
Est. cost / taskon this benchmark$0.0048
Defect Detection
73.3%(11/15)
86.7%(13/15)
Document Understanding
66.7%(6/9)
66.7%(6/9)
Object Counting
20%(2/10)
30%(3/10)
Object Understanding
92.9%(13/14)
71.4%(10/14)
Spatial Understanding
78.9%(15/19)
84.2%(16/19)
OCR
Overall Score
83.84%
Avg Response Time2.77s
Median input tokensincl. image tokens642
Median output tokens64
Est. cost / taskon this benchmark$0.0019
Focused Scene OCR
88.9%(88/99)
Handwritten Math
50%(5/10)
License Plate Recognition
90%(27/30)
Text Recognition
80%(24/30)
VQA & Extraction
80%(48/60)

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