Claude Opus 5.5 vs Qwen3.5-27B
Claude Opus 5.5 averages 82.0% across five Vision Evals tasks and Qwen3.5-27B 64.1% across four (#4 and #32 of 65 models), so Claude Opus 5.5 leads overall.
- Claude Opus 5.5 wins all 4 tasks both ran, with its widest lead on reasoning (83.0% vs 58.1%).
- Qwen3.5-27B is about a quarter of the cost: $0.0043 vs $0.015 overall average cost per sample.
- Claude Opus 5.5 is faster: 7.6s vs 80.4s per sample.
Roboflow Vision Evals. Full breakdown below.
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Claude Opus 5.5 vs Qwen3.5-27B on Vision Evals
Roboflow Vision Evals. Full breakdown below.
Claude Opus 5.5 vs Qwen3.5-27B Comparison Table
Evals updated October 26, 2026Pricing updated November 5, 2026
| Property | Claude Opus 5.5 | Qwen3.5-27B |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Feb 2026 |
| Context Window | 1.0M | 262K |
| Parameters | Unknown | 27B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $4.00 | $0.260 |
| Output $/1M | $20.00 | $2.60 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 82.0% | 64.1%* |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.015 | $0.0043 |
| Avg speed / sample | 7.63s | 80.37s |
| By task | ||
| Object Detection (low) | 74.5% ±0.5, Mean of 3 runs, range 74.0 to 75.0 | 50.5% ±3.5, Mean of 3 runs, range 46.1 to 53.0 |
| Object Detection (high) | 76.0% ±1.1, Mean of 3 runs, range 74.7 to 77.0 | – |
| Counting (low) | 80.6% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 67.6% ±1.4, Mean of 3 runs, range 66.2 to 68.9 |
| Counting (high) | 82.0% ±2.0, Mean of 3 runs, range 79.7 to 83.8 | – |
| Identification (low) | 93.8% ±0.0, Mean of 3 runs, range 93.8 to 93.8 | 80.2% ±4.7, Mean of 3 runs, range 75.0 to 84.4 |
| Identification (high) | 95.8% ±1.6, Mean of 3 runs, range 93.8 to 96.9 | – |
| OCR (low) | 78.3% | – |
| by category |
| |
| OCR (high) | 79.9% | – |
| by category |
| |
| Reasoning (low) | 83.0% ±1.0, Mean of 3 runs, range 82.1 to 84.1 | 58.1% ±2.3, Mean of 3 runs, range 55.6 to 60.3 |
| Reasoning (high) | 85.9% ±2.6, Mean of 3 runs, range 82.8 to 88.1 | – |
Claude Opus 5.5 vs Qwen3.5-27B: Overview
Claude Opus 5.5 is a proprietary multimodal reasoning model from Anthropic and the first entry in the Claude 5.5 family. It accepts interleaved text and image input and returns text, with a one million token context window and up to 128,000 output tokens per response. Adaptive thinking is always enabled on this model and cannot be disabled; thinking depth is instead governed by an effort parameter with five levels, where medium is the default, a change from the high default used by Claude Opus 5 and earlier Opus models. Anthropic reports a knowledge cutoff of June 2026.
On the visual side, Anthropic characterizes Opus 5.5 as its strongest Opus release for vision and computer use, describing improved reading of dense documents, charts, screenshots, and diagrams for document extraction and visual analysis tasks. The accompanying system card states that Opus 5.5 scored higher than Opus 5 on every evaluation in its capability summary, with the largest gains concentrated in agentic coding, visual reasoning, computer use, and long-horizon knowledge work. The model ships with safety classifiers covering biology and cybersecurity that can route blocked requests to earlier Claude models.
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. 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. 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.
Frequently Asked Questions
On Roboflow's Vision Evals, Claude Opus 5.5 performed better. It scores higher on all 4 vision tasks and averages 82.0% (#4 of 65) against 64.1% (#32 of 65) for Qwen3.5-27B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, Claude Opus 5.5 leads with 83.0% against 58.1%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.5-27B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0043 per sample against $0.015. Actual costs depend on your image sizes, prompts, and output length.
Claude Opus 5.5 is faster. Across Roboflow's Vision Evals it averaged 7.6s per inference against 80.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for image captioning and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.