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Claude Opus 4.8 vs Qwen3.7 Flash

Compare Claude Opus 4.8 and Qwen3.7 Flash side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Object Detection, and Open Prompt.

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AnthropicClaude Opus 4.8
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QwenQwen3.7 Flash
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Models in this comparison

Claude Opus 4.8 vs Qwen3.7 Flash on Vision Evals

Claude Opus 4.8 scores higher on 4 of the six Vision Evals tasks.

The widest gap is Reasoning, where Claude Opus 4.8 leads 53.0% to 34.4%.

Overall, Claude Opus 4.8 averages 66.8% (#16 of 25) against 61.7% (#24 of 25) for Qwen3.7 Flash.

Qwen3.7 Flash is cheaper ($0.0001 vs $0.016 per sample), while Claude Opus 4.8 is faster (5.2s vs 6.3s per sample).

Claude Opus 4.8Qwen3.7 Flash

Claude Opus 4.8 vs Qwen3.7 Flash Comparison Table

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

PropertyClaude Opus 4.8Qwen3.7 Flash
OrganizationAnthropicQwen
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMay 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
Image Tagging
Multi-Label Classification
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort
Overall
66.8%
61.7%
Avg cost / sample$0.016$0.0001
Avg speed / sample5.20s6.32s
By task
Object Detection
38.6%
$0.026
42.8%
$0.0001
Counting
52.7%
$0.0076
46.0%
<$0.0001
Identification
75.0%
$0.0067
84.4%
<$0.0001
OCR
93.8%
$0.020
84.1%
$0.0001
Data Extraction
87.6%
$0.0076
78.3%
<$0.0001
Reasoning (low)
53.0%
$0.0078
34.4%
<$0.0001
Reasoning (high)
52.3%
$0.0078
60.9%
$0.0005

Claude Opus 4.8 vs Qwen3.7 Flash: Overview

Claude Opus 4.8

Claude Opus 4.8 is Anthropic's most capable generally available large language model, released on May 28, 2026 as an incremental upgrade to Claude Opus 4.7. The model accepts text and image inputs and produces text outputs, with a 1 million token context window on the Claude API, Amazon Bedrock, and Google Cloud Vertex AI (200k tokens on Microsoft Foundry) and up to 128k max output tokens. It uses adaptive thinking and supports adjustable effort tiers — high by default, with extra and max tiers available for more demanding tasks. A fast mode operates at approximately 2.5x standard speed. The model is described by Anthropic as a hybrid reasoning model designed for advanced coding, agentic workflows, long-context reasoning, and professional knowledge work.

Key behavioral improvements over Opus 4.7 include substantially reduced rates of unreported code flaws, improved honesty in self-assessment, and better tool-calling reliability. On Anthropic's Super-Agent benchmark, Opus 4.8 completes every case end-to-end, and it scores 84% on Online-Mind2Web for computer-use and browser-agent tasks. It achieves 88.6% on SWE-bench Verified and 69.2% on SWE-bench Pro. Alongside the model, Anthropic launched Dynamic Workflows in Claude Code (research preview), which enables Claude to orchestrate hundreds of parallel subagents for codebase-scale tasks such as large migrations. The Messages API was also updated to accept mid-task system messages without breaking prompt caching, improving support for long-running agentic pipelines.

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 4.8 performed better. It scores higher on 4 of the six vision tasks and averages 66.8% (#16 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 4.8 leads with 53.0% 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 $0.016. Claude Opus 4.8 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.

Claude Opus 4.8 is faster. Across Roboflow's Vision Evals it averaged 5.2s per inference against 6.3s. 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 image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.