Claude Opus 5.5 vs Qwen3.5 9b
Compare Claude Opus 5.5 and Qwen3.5 9b side-by-side.
Compare Claude Opus 5.5 vs Qwen3.5 9b live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
Claude Opus 5.5 vs Qwen3.5 9b on Vision Evals
Claude Opus 5.5 scores higher on all six Vision Evals tasks.
The widest gap is Reasoning, where Claude Opus 5.5 leads 83.0% to 47.7%.
Overall, Claude Opus 5.5 averages 85.5% (#3 of 57) against 64.3% (#44 of 57) for Qwen3.5 9b.
Qwen3.5 9b is cheaper ($0.0017 vs $0.014 per sample), while Claude Opus 5.5 is faster (12.8s vs 33.6s per sample).
Claude Opus 5.5 vs Qwen3.5 9b Comparison Table
Evals updated September 22, 2026Pricing updated September 22, 2026
| Property | Claude Opus 5.5 | Qwen3.5 9b |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Mar 2026 |
| Context Window | 1.0M | 262K |
| Parameters | 9B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $4.00 | $0.100 |
| Output $/1M | $20.00 | $0.150 |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | ||
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | ||
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 85.5% | 64.3% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.014 | $0.0017 |
| Avg speed / sample | 12.76s | 33.65s |
| By task | ||
| Object Detection (low) | 74.4% ±0.5, Mean of 3 runs, range 73.9 to 74.8 | 46.6% ±0.6, Mean of 3 runs, range 45.8 to 47.0 |
| Object Detection (high) | 76.8% ±1.2, Mean of 3 runs, range 75.4 to 77.8 | – |
| Counting (low) | 80.6% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 51.8% ±2.7, Mean of 3 runs, range 48.6 to 54.0 |
| 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 | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 95.8% ±1.6, Mean of 3 runs, range 93.8 to 96.9 | – |
| OCR (low) | 87.8% ±0.6, Mean of 3 runs, range 87.0 to 88.2 | 77.5% ±7.3, Mean of 3 runs, range 68.4 to 83.0 |
| OCR (high) | 87.2% ±0.6, Mean of 3 runs, range 86.5 to 87.8 | – |
| Data Extraction (low) | 93.5% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | 78.7% ±2.1, Mean of 3 runs, range 76.3 to 80.4 |
| Data Extraction (high) | 93.5% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | – |
| Reasoning (low) | 83.0% ±1.0, Mean of 3 runs, range 82.1 to 84.1 | 47.7% ±2.3, Mean of 3 runs, range 45.7 to 50.3 |
| Reasoning (high) | 85.9% ±2.6, Mean of 3 runs, range 82.8 to 88.1 | – |
Claude Opus 5.5 vs Qwen3.5 9b: 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. Published results include 89.0% on Chartography with tools and 81.8% on OSWorld 2.0 under partial credit scoring, alongside 48.7% under strict scoring reported in the system card. 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.
Qwen3.5-9B is a 9-billion-parameter multimodal foundation model developed by Alibaba Cloud's Qwen team, released on March 2, 2026 as part of the Qwen3.5 model family. Designed for efficient multimodal reasoning and long-context language tasks, it notably outperforms the older Qwen3-30B, a model more than three times its size, on key benchmarks including GPQA Diamond, IFEval, and LongBench.
The model supports vision-language inputs through an early-fusion multimodal architecture built on a dense hybrid foundation of Gated Delta Networks and Gated Attention. It can also operate in a text-only mode by skipping the vision encoder during inference. It provides a 262,144-token context window (extensible to ~1M tokens via YaRN) and is released under the Apache License 2.0. Within the current AI landscape, Qwen3.5-9B offers a strong balance of capability and efficiency, making it well-suited for multimodal assistants, document analysis, long-context reasoning, and developer-deployed agentic systems.
Frequently Asked Questions
On Roboflow's Vision Evals, Claude Opus 5.5 performed better. It scores higher on all six vision tasks and averages 85.5% (#3 of 57) against 64.3% (#44 of 57) for Qwen3.5 9b. 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 47.7%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.5 9b is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0017 per sample against $0.014. Actual costs depend on your image sizes, prompts, and output length.
Claude Opus 5.5 is faster. Across Roboflow's Vision Evals it averaged 12.8s per inference against 33.6s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.