Claude Opus 4.1 vs Qwen3.5 122B A10B
Compare Claude Opus 4.1 and Qwen3.5 122B A10B side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.
Compare Claude Opus 4.1 vs Qwen3.5 122B A10B 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.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
Claude Opus 4.1 vs Qwen3.5 122B A10B: Overview
Claude 4.1 Opus, released by Anthropic in August 2025, is the upgraded flagship of the Claude 4 family, building on Opus 4 with stronger reasoning and agentic capabilities. Like its predecessor, it is multimodal and optimized for text, code, and tool use, with support for large context windows suited to multi-file codebases, technical workflows, and long-horizon problem solving.
On benchmarks, Opus 4.1 improves coding performance, reaching ~74.5% on SWE-Bench Verified compared to Opus 4’s ~72.5%. It demonstrates more precise debugging, refactoring, and orchestration of agentic tasks while maintaining similar safety and alignment safeguards. It is best suited for enterprise-scale software development, research automation, and advanced reasoning workflows where reliability and depth of analysis are critical.
Qwen3.5-122B-A10B is a high-capacity multimodal Mixture-of-Experts (MoE) model developed by Alibaba’s Qwen team as part of the Qwen3.5 model family. The architecture contains 122 billion total parameters while activating roughly 10 billion per token through sparse expert routing, allowing the model to balance large-scale reasoning ability with relatively efficient inference compared to dense models of similar size.
The model is designed to process both text and visual inputs within a unified multimodal framework, enabling tasks that require reasoning across images, documents, charts, and natural language. This makes it suitable for applications such as document understanding, diagram interpretation, and complex visual question answering.
Qwen3.5-122B-A10B supports a native context window of approximately 256,000 tokens, which can be extended further through techniques such as YaRN scaling to support very long-context workloads. Released under the Apache 2.0 license, it builds on earlier Qwen multimodal systems and provides developers with an open-weight model capable of handling demanding multimodal reasoning and analysis tasks.
Claude Opus 4.1 vs Qwen3.5 122B A10B Comparison Table
| Property | Claude Opus 4.1 | Qwen3.5 122B A10B |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Aug 2025 | Feb 2026 |
| Context Window | 200K | 256K |
| Parameters | 122B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $15.00 | $0.260 |
| Output $/1M | $75.00 | $2.08 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Classification | Demo | |
| 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 | 59.7% | 76.12% |
| Avg Response Time | 7.09s | 1.77s |
| Median input tokensincl. image tokens | 2.0K | 1.2K |
| Median output tokens | 140 | 7 |
| Est. cost / taskon this benchmark | $0.040 | $0.0003 |
| Defect Detection | 73.3%(11/15) | 86.7%(13/15) |
| Document Understanding | 88.9%(8/9) | 77.8%(7/9) |
| Object Counting | 0%(0/10) | 40%(4/10) |
| Object Understanding | 64.3%(9/14) | 92.9%(13/14) |
| Spatial Understanding | 63.2%(12/19) | 73.7%(14/19) |
| OCR | ||
| Overall Score | 68.56% | |
| Avg Response Time | 5.08s | |
| Median input tokensincl. image tokens | 552 | |
| Median output tokens | 97 | |
| Est. cost / taskon this benchmark | $0.016 | |
| Focused Scene OCR | 73.7%(73/99) | |
| Handwritten Math | 30%(3/10) | |
| License Plate Recognition | 53.3%(16/30) | |
| Text Recognition | 80%(24/30) | |
| VQA & Extraction | 68.3%(41/60) | |
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