Roboflow

Claude Sonnet 5 vs Qwen3.5 35B A3B

Compare Claude Sonnet 5 and Qwen3.5 35B A3B side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.

Compare Claude Sonnet 5 vs Qwen3.5 35B A3B live

Run the same image across every model that supports a task and compare their outputs side-by-side.

Extract and compare text from images across multiple models.

Open OCR in the full playground
AnthropicClaude Sonnet 5
Run to compare this model.
QwenQwen3.5 35B A3B
Run to compare this model.

Models in this comparison

Claude Sonnet 5 vs Qwen3.5 35B A3B: 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 35B A3B

The Qwen3.5-35B-A3B is a native vision-language model developed by Alibaba Cloud’s Qwen team, released on February 24, 2026, as a high-efficiency entry in the Qwen 3.5 family. It utilizes a sophisticated hybrid architecture that integrates Gated Delta Networks with a sparse Mixture-of-Experts (MoE) system. While the model houses 35 billion total parameters, its routing mechanism activates only 8 routed experts and 1 shared expert per token, totaling approximately 3 billion active parameters. This design achieves cross-generational parity with the previous flagship Qwen3-235B dense model, delivering comparable reasoning and multimodal intelligence with significantly reduced inference latency and compute requirements. Available under the Apache 2.0 license, it is released in both base and instruction-tuned variants for seamless integration with open-source stacks like vLLM and Hugging Face Transformers.

Designed for the emerging era of agentic AI, the model utilizes a unified multimodal foundation built through early-fusion training. This approach allows it to outperform the prior Qwen3-VL series in spatial grounding, document analysis, and UI/GUI interaction. It features a native context window of 262,144 tokens, which is extensible up to 1,010,000 tokensvia RoPE scaling, and provides global support for 201 languages and dialects. This combination of a compact active parameter count and frontier-level visual comprehension makes it a versatile tool for developers requiring a balance of high-throughput speed and sophisticated visual reasoning for long-context workflows.

Claude Sonnet 5 vs Qwen3.5 35B A3B Comparison Table

PropertyClaude Sonnet 5Qwen3.5 35B A3B
OrganizationAnthropicQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateJun 2026Feb 2026
Context Window1.0M262K
Parameters35B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$2.00$0.140
Output $/1M$10.00$1.00
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%
79.1%
Avg Response Time3.90s20.94s
Median input tokensincl. image tokens2.1K
Median output tokens61
Est. cost / taskon this benchmark$0.0048
Defect Detection
73.3%(11/15)
93.3%(14/15)
Document Understanding
66.7%(6/9)
77.8%(7/9)
Object Counting
20%(2/10)
40%(4/10)
Object Understanding
92.9%(13/14)
85.7%(12/14)
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