Roboflow

Claude Opus 5 vs Qwen3.7 Flash

Compare Claude Opus 5 and Qwen3.7 Flash side-by-side. See how these vision models stack up in Image Captioning, OCR, Open Prompt, Object Detection, and Classification.

Compare Claude Opus 5 vs Qwen3.7 Flash live

Run the same image across every model that supports a task and compare their outputs side-by-side.

Detect and compare bounding boxes across models on the same image.

Open Object Detection in the full playground
AnthropicClaude Opus 5
Run to compare this model.
QwenQwen3.7 Flash
Run to compare this model.

Models in this comparison

Anthropic

Claude Opus 5 vs Qwen3.7 Flash on Vision Evals

Claude Opus 5 scores higher on 5 of the six Vision Evals tasks.

The widest gap is Reasoning, where Claude Opus 5 leads 71.5% to 34.4%.

Overall, Claude Opus 5 averages 77.0% (#8 of 25) against 61.7% (#24 of 25) for Qwen3.7 Flash.

Qwen3.7 Flash is both cheaper ($0.0001 vs $0.017 per sample) and faster (6.3s vs 7.4s per sample).

Claude Opus 5Qwen3.7 Flash

Claude Opus 5 vs Qwen3.7 Flash Comparison Table

Evals updated August 6, 2026Pricing updated August 11, 2026

PropertyClaude Opus 5Qwen3.7 Flash
OrganizationAnthropicQwen
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJul 2026Jul 2026
Context Window1.0M1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$5.00$0.030
Output $/1M$25.00$0.130
Vision Tasks
CaptioningDemoDemo
Chart Question Answering
ClassificationDemoDemo
Document Question Answering
Multi-Label Classification
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Image Tagging
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
77.0%
61.7%
Avg cost / sample$0.017$0.0001
Avg speed / sample7.38s6.32s
By task
Object Detection
54.0%
$0.027
42.8%
$0.0001
Counting
70.3%
$0.0096
46.0%
<$0.0001
Identification
84.4%
$0.0072
84.4%
<$0.0001
OCR
93.2%
$0.020
84.1%
$0.0001
Data Extraction
88.7%
$0.0080
78.3%
<$0.0001
Reasoning (low)
71.5%
$0.010
34.4%
<$0.0001
Reasoning (high)
74.2%
$0.018
60.9%
$0.0005

Claude Opus 5 vs Qwen3.7 Flash: Overview

Claude Opus 5

Claude Opus 5 is a large language model with multimodal vision capabilities developed by Anthropic, released on July 24, 2026 as the fourth model in the Claude 5 family. It sits in the Opus tier of Anthropic's lineup, positioned below the Mythos-class Fable 5 and Mythos 5 models, and is framed by Anthropic as the go-to model for most knowledge work and automation tasks. The model approaches Fable 5's capabilities at roughly half the cost, priced at $5 per million input tokens and $25 per million output tokens. It becomes the default model on Claude Max and the strongest model available on Claude Pro. The model ships with a 1 million token context window and an adjustable "effort" parameter that allows users to trade reasoning depth for speed and token savings. Early enterprise customers reported that Opus 5 achieved comparable performance to Opus 4.8's maximum-reasoning mode while generating significantly fewer tokens on average, and demonstrated higher accuracy on financial modeling tasks with fewer tool calls and less time.

Claude Opus 5 supports multimodal inputs including images and text, and is designed for agentic workflows, coding, scientific research, and complex enterprise tasks. Anthropic reports the model scores 10.2 percentage points higher than Opus 4.8 on an internal chemistry benchmark, making it the most capable generally available model for scientific research in the Claude lineup. Cyber classifiers on Opus 5 are designed to intervene approximately 85 percent less often than those on Fable 5, with fallback to Opus 4.8 when a classifier triggers. The model does not retain user data for 30 days, unlike Fable 5. It is available across Anthropic's platforms including Claude Code and Claude Cowork, as well as cloud partners.

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 performed better. It scores higher on 5 of the six vision tasks and averages 77.0% (#8 of 25) against 61.7% (#24 of 25) 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 leads with 71.5% 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 $0.017. Claude Opus 5 is priced at $5.00 per 1M input tokens and $25.00 per 1M output; Qwen3.7 Flash is priced at $0.03 per 1M input tokens and $0.13 per 1M output. 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 7.4s. 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.