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Claude Sonnet 5 vs Qwen3.7 Flash

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

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AnthropicClaude Sonnet 5
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QwenQwen3.7 Flash
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Models in this comparison

Claude Sonnet 5 vs Qwen3.7 Flash on Vision Evals

Claude Sonnet 5 scores higher on 3 of the five Vision Evals tasks.

The widest gap is Counting, where Claude Sonnet 5 leads 56.8% to 46.0%.

Overall, Claude Sonnet 5 averages 55.3% (#47 of 63) against 52.4% (#53 of 63) for Qwen3.7 Flash.

Qwen3.7 Flash is both cheaper ($0.0001 vs $0.0074 per sample) and faster (5.8s vs 5.9s per sample).

Claude Sonnet 5Qwen3.7 Flash

Claude Sonnet 5 vs Qwen3.7 Flash Comparison Table

Evals updated October 13, 2026Pricing updated October 26, 2026

PropertyClaude Sonnet 5Qwen3.7 Flash
OrganizationAnthropicQwen
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJun 2026Jul 2026
Context Window1.0M1.0M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$2.00$0.030
Output $/1M$10.00$0.130
Vision Tasks
CaptioningDemoDemo
Chart Question AnsweringSupportedSupported
ClassificationDemoDemo
Document Question AnsweringSupportedSupported
Image TaggingSupportedSupported
Multi-Label ClassificationSupportedSupported
Object DetectionDemoDemo
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
55.3%
52.4%
Avg cost / sample$0.0074$0.0001
Avg speed / sample5.86s5.77s
By task
Object Detection
36.1%
$0.011
42.8%
$0.0001
Counting
56.8%
$0.0030
46.0%
<$0.0001
Identification
81.3%
$0.0027
84.4%
<$0.0001
OCR (low)
59.4%
$0.0078
54.3%
$0.0001
by category
Single value
52.2%
Transcription
88.6%
Structured JSON
78.3%
Text localization
13.1%
Single value
52.2%
Transcription
84.4%
Structured JSON
67.3%
Text localization
9.9%
OCR (high)
59.5%
$0.015
62.3%
$0.0004
by category
Single value
54.8%
Transcription
88.3%
Structured JSON
76.6%
Text localization
11.6%
Single value
56.1%
Transcription
83.8%
Structured JSON
75.7%
Text localization
31.7%
Reasoning (low)
43.0%
$0.0032
34.4%
<$0.0001
Reasoning (high)
43.0%
$0.0043
61.6%
$0.0005

Claude Sonnet 5 vs Qwen3.7 Flash: Overview

Claude Sonnet 5

Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.

The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.

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.