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Claude Opus 5.5 vs Qwen3.7 Flash

Compare Claude Opus 5.5 and Qwen3.7 Flash side-by-side.

Compare Claude Opus 5.5 vs Qwen3.7 Flash 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 Qwen3.7 Flash 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 34.4%.

Overall, Claude Opus 5.5 averages 85.5% (#3 of 57) against 61.5% (#49 of 57) for Qwen3.7 Flash.

Qwen3.7 Flash is both cheaper ($0.0001 vs $0.014 per sample) and faster (6.3s vs 12.8s per sample).

Claude Opus 5.5Qwen3.7 Flash

Claude Opus 5.5 vs Qwen3.7 Flash Comparison Table

Evals updated September 22, 2026Pricing updated September 22, 2026

PropertyClaude Opus 5.5Qwen3.7 Flash
OrganizationAnthropicQwen
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Jul 2026
Context Window1.0M1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.030
Output $/1M$0.130
Vision Tasks
CaptioningDemo
Chart Question Answering
ClassificationDemo
Document Question Answering
Image Tagging
Multi-Label Classification
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
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%
61.5%
Avg cost / sample$0.014$0.0001
Avg speed / sample12.76s6.28s
By task
Object Detection (low)
74.4%
±0.5, Mean of 3 runs, range 73.9 to 74.8
$0.022
42.8%
$0.0001
Object Detection (high)
76.8%
±1.2, Mean of 3 runs, range 75.4 to 77.8
$0.030
Counting (low)
80.6%
±2.0, Mean of 3 runs, range 78.4 to 82.4
$0.0081
46.0%
<$0.0001
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
84.4%
<$0.0001
Identification (high)
95.8%
±1.6, Mean of 3 runs, range 93.8 to 96.9
$0.0067
OCR (low)
87.8%
±0.6, Mean of 3 runs, range 87.0 to 88.2
$0.017
84.1%
$0.0001
OCR (high)
87.2%
±0.6, Mean of 3 runs, range 86.5 to 87.8
$0.024
Data Extraction (low)
93.5%
±0.5, Mean of 3 runs, range 92.8 to 93.8
$0.0066
77.3%
<$0.0001
Data Extraction (high)
93.5%
±0.5, Mean of 3 runs, range 92.8 to 93.8
$0.0075
Reasoning (low)
83.0%
±1.0, Mean of 3 runs, range 82.1 to 84.1
$0.0090
34.4%
<$0.0001
Reasoning (high)
85.9%
±2.6, Mean of 3 runs, range 82.8 to 88.1
$0.011
61.6%
$0.0005

Claude Opus 5.5 vs Qwen3.7 Flash: 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.

Qwen3.7 Flash

Qwen3.7 Flash is the low-latency, cost-oriented tier of Alibaba's Qwen3.7 series, a vision-language reasoning model that accepts interleaved text and image input and returns text. It is built as a hybrid thinking model: like the rest of the Qwen3.7, Qwen3.6, and Qwen3.5 families served through Alibaba Cloud Model Studio, it can either emit an explicit reasoning trace before answering or respond directly, with thinking behavior controlled by an enable_thinking switch that defaults to on for the Qwen3.7 generation. The model exposes a context window of roughly one million tokens and a maximum generation length of 65,536 tokens, which allows long multi-image sequences, long documents, and extended agent trajectories to be held in a single request.

Functionally, Qwen3.7 Flash targets multimodal agent workloads rather than pure chat. Reported strengths include object recognition, spatial understanding, and perception of real-world scenes, alongside visual coding, search, and computer-use style interaction where the model reads screen content and reasons over interface state. Weights are not published; the model is a proprietary endpoint positioned below Qwen3.7 Plus and Qwen3.7 Max in the same series, and it supports function calling and tool use for agentic pipelines.

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 61.5% (#49 of 57) for Qwen3.7 Flash. 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 34.4%. This is the widest gap between the two models across the benchmark's tasks.

Qwen3.7 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0001 per sample against $0.014. Actual costs depend on your image sizes, prompts, and output length.

Qwen3.7 Flash is faster. Across Roboflow's Vision Evals it averaged 6.3s per inference against 12.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.