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Claude Opus 5.5 vs Qwen3.5 9b

Compare Claude Opus 5.5 and Qwen3.5 9b side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

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AnthropicClaude Opus 5.5
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QwenQwen3.5 9b
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Models in this comparison

Claude Opus 5.5 vs Qwen3.5 9b on Vision Evals

Claude Opus 5.5 scores higher on all 4 Vision Evals tasks.

The widest gap is Reasoning, where Claude Opus 5.5 leads 83.0% to 45.9%.

Overall, Claude Opus 5.5 averages 82.0% (#3 of 63) against 56.0% (#42 of 63) for Qwen3.5 9b.

Qwen3.5 9b is cheaper ($0.0021 vs $0.015 per sample), while Claude Opus 5.5 is faster (7.6s vs 41.4s per sample).

Claude Opus 5.5Qwen3.5 9b

Claude Opus 5.5 vs Qwen3.5 9b Comparison Table

Evals updated October 13, 2026Pricing updated October 17, 2026

PropertyClaude Opus 5.5Qwen3.5 9b
OrganizationAnthropicQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateSep 2026Mar 2026
Context Window1.0M262K
ParametersUnknown9B
LicenseProprietaryApache 2.0
Commercial usecommercial use per vendor termsfree for commercial use
Pricing per 1M tokens
Input $/1M$4.00$0.100
Output $/1M$20.00$0.150
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoSupported
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoSupported
OCRDemoDemo
Vision LanguageSupportedSupported
Visual Question AnsweringDemoDemo
Model Features
Foundation VisionSupportedSupported
LLMs with Vision CapabilitiesSupportedSupported
Multimodal VisionSupportedSupported
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort
Overall
82.0%
56.0%
4/5 tasks
Quantizationsself-hosted
BF1656.0%FP854.8%AWQ-INT457.4%hardware →
Avg cost / sample$0.015$0.0021
Avg speed / sample7.63s41.36s
By task
Object Detection (low)
74.5%
±0.5, Mean of 3 runs, range 74.0 to 75.0
$0.021
38.1%
±5.7, Mean of 3 runs, range 33.5 to 44.9
$0
Object Detection (high)
76.0%
±1.1, Mean of 3 runs, range 74.7 to 77.0
$0.030
–
Counting (low)
80.6%
±2.0, Mean of 3 runs, range 78.4 to 82.4
$0.0081
56.8%
±1.4, Mean of 3 runs, range 55.4 to 58.1
$0
Counting (high)
82.0%
±2.0, Mean of 3 runs, range 79.7 to 83.8
$0.0098
–
Identification (low)
93.8%
±0.0, Mean of 3 runs, range 93.8 to 93.8
$0.0058
83.3%
±1.6, Mean of 3 runs, range 81.3 to 84.4
$0
Identification (high)
95.8%
±1.6, Mean of 3 runs, range 93.8 to 96.9
$0.0067
–
OCR (low)
78.3%
$0.016
–
by category
Single value
67.8%
Transcription
96.7%
Structured JSON
91.5%
Text localization
61.0%
OCR (high)
79.9%
$0.023
–
by category
Single value
69.6%
Transcription
96.1%
Structured JSON
94.1%
Text localization
61.1%
Reasoning (low)
83.0%
±1.0, Mean of 3 runs, range 82.1 to 84.1
$0.0090
45.9%
±1.7, Mean of 3 runs, range 44.4 to 47.7
$0
Reasoning (high)
85.9%
±2.6, Mean of 3 runs, range 82.8 to 88.1
$0.011
–

Claude Opus 5.5 vs Qwen3.5 9b: Overview

Claude Opus 5.5

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

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 4 vision tasks and averages 82.0% (#3 of 63) against 56.0% (#42 of 63) 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 45.9%. 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 $0.0021 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 41.4s. 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.