Claude Opus 4 vs Qwen3.5 27B
Compare Claude Opus 4 and Qwen3.5 27B side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.
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Claude Opus 4 is deprecated and can no longer be run. Details and evals are still available on its model page.
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Claude Opus 4 vs Qwen3.5 27B: Overview
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.
Qwen3.5-27B is a multimodal dense hybrid model developed by Alibaba Cloud’s Qwen team and released in February 2026 as a high-precision entry in the Qwen3.5 "Medium" series. Unlike its Mixture-of-Experts (MoE) siblings, the 27B model utilizes a dense architecture combining Gated Delta Networks with a feed-forward structure, activating its full parameter suite for every inference to maximize reliability. This design provides the highest instruction-following and coding accuracy in its class, with a notable IFEval score of 95.0. The model features a native 262K-token context window, extensible to 1M tokens via YaRN (RoPE scaling), and is released under the Apache-2.0 license.
Optimized for agentic workflows, Qwen3.5-27B employs an early-fusion architecture that treats visual and textual data as a unified stream for deep cross-modal reasoning. This unified approach allows the model to excel in technical analysis and software engineering, matching GPT-5-mini with a 72.4% score on SWE-bench Verified. While the larger MoE variants in the family lead in raw knowledge benchmarks, the 27B model offers a stable and high-density alternative for structured data extraction and spatial perception, contributing to the Qwen3.5 family’s generational leap in OCR accuracy over the previous Qwen3-VL series.
Claude Opus 4 vs Qwen3.5 27B Comparison Table
| Property | Claude Opus 4 | Qwen3.5 27B |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | May 2025 | Feb 2026 |
| Context Window | 200K | 262K |
| Parameters | 27B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $15.00 | $0.195 |
| Output $/1M | $75.00 | $1.56 |
| Vision Tasks | ||
| Captioning | Demo | |
| Object Detection | ||
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| 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% | 71.64% |
| Avg Response Time | 19.74s | 1.98s |
| Median input tokensincl. image tokens | 1.2K | |
| Median output tokens | 7 | |
| Est. cost / taskon this benchmark | $0.0002 | |
| Defect Detection | 66.7%(10/15) | 80%(12/15) |
| Document Understanding | 88.9%(8/9) | 77.8%(7/9) |
| Object Counting | 0%(0/10) | 40%(4/10) |
| Object Understanding | 64.3%(9/14) | 78.6%(11/14) |
| Spatial Understanding | 57.9%(11/19) | 73.7%(14/19) |
| OCR | ||
| Overall Score | 85.59% | |
| Avg Response Time | 8.51s | |
| Median input tokensincl. image tokens | 126 | |
| Median output tokens | 107 | |
| Est. cost / taskon this benchmark | $0.0002 | |
| Focused Scene OCR | 84.8%(84/99) | |
| Handwritten Math | 100%(10/10) | |
| License Plate Recognition | 93.3%(28/30) | |
| Text Recognition | 80%(24/30) | |
| VQA & Extraction | 83.3%(50/60) | |
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