Roboflow

Claude Opus 4.6 vs Qwen3.5 35B A3B

Compare Claude Opus 4.6 and Qwen3.5 35B A3B side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.

Compare Claude Opus 4.6 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 4.6
Run to compare this model.
QwenQwen3.5 35B A3B
Run to compare this model.

Models in this comparison

Claude Opus 4.6 vs Qwen3.5 35B A3B: 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 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 4.6 vs Qwen3.5 35B A3B Comparison Table

PropertyClaude Opus 4.6 Qwen3.5 35B A3B
OrganizationAnthropicQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateFeb 2026Feb 2026
Context Window1.0M262K
Parameters35B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$5.00$0.140
Output $/1M$25.00$1.00
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%
Visual Understanding
Overall Score
64.18%
79.1%
Avg Response Time23.35s20.94s
Median input tokensincl. image tokens2.2K
Median output tokens130
Est. cost / taskon this benchmark$0.014
Defect Detection
73.3%(11/15)
93.3%(14/15)
Document Understanding
77.8%(7/9)
77.8%(7/9)
Object Counting
20%(2/10)
40%(4/10)
Object Understanding
71.4%(10/14)
85.7%(12/14)
Spatial Understanding
68.4%(13/19)
84.2%(16/19)
OCR
Overall Score
82.53%
Avg Response Time5.05s
Median input tokensincl. image tokens736
Median output tokens99
Est. cost / taskon this benchmark$0.0062
Focused Scene OCR
85.9%(85/99)
Handwritten Math
70%(7/10)
License Plate Recognition
90%(27/30)
Text Recognition
80%(24/30)
VQA & Extraction
76.7%(46/60)

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