Roboflow

Claude Opus 4 vs Qwen3.5 35B A3B

Compare Claude Opus 4 and Qwen3.5 35B A3B side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

Compare Claude Opus 4 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 Opus 4

Claude Opus 4 is deprecated and can no longer be run. Details and evals are still available on its model page.

QwenQwen3.5 35B A3B
Run to compare this model.

Models in this comparison

Anthropic

Claude Opus 4 vs Qwen3.5 35B A3B: Overview

Claude Opus 4

Claude 4 Opus, released by Anthropic in May 2025, is the flagship model of the Claude 4 family, built for complex, long-horizon reasoning and advanced coding workflows. It is multimodal, supporting text (including voice), images, and tool use, and operates as a hybrid reasoning model—able to deliver quick answers in fast mode or switch to extended thinking for deeper, multi-step problem solving. With a ~200,000-token context window and a training cutoff around March 2025, it is optimized for handling large documents, long conversations, and sophisticated agentic tasks.

Positioned at the high end of Anthropic’s offerings, Opus 4 achieves state-of-the-art results on coding benchmarks like SWE-Bench (72.5%) and Terminal-Bench (43.2%). It is best suited for research, enterprise automation, and software development at scale. The model is classified at Anthropic’s ASL-3 safety level, denoting advanced oversight and safety features.

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 Opus 4 vs Qwen3.5 35B A3B Comparison Table

PropertyClaude Opus 4Qwen3.5 35B A3B
OrganizationAnthropicQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateMay 2025Feb 2026
Context Window200K262K
Parameters35B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$15.00$0.140
Output $/1M$75.00$1.00
Vision Tasks
CaptioningDemo
Object Detection
OCRDemo
Vision Language
Visual Question AnsweringDemo
Classification
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
56.72%
79.1%
Avg Response Time19.74s20.94s
Defect Detection
66.7%(10/15)
93.3%(14/15)
Document Understanding
88.9%(8/9)
77.8%(7/9)
Object Counting
0%(0/10)
40%(4/10)
Object Understanding
64.3%(9/14)
85.7%(12/14)
Spatial Understanding
57.9%(11/19)
84.2%(16/19)