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Claude Sonnet 5 vs Gemini 3.1 Pro+ 1 other

Compare Claude Sonnet 5, Gemini 3.1 Pro, and 1 other vision model side-by-side. Test these models on Object Detection, Open Prompt, OCR, Classification, and Image Captioning in the Playground.

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AnthropicClaude Sonnet 5
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GoogleGemini 3.1 Pro
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OpenAIGPT-5.5
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Models in this comparison

OpenAI

Model Overviews

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.

Claude Sonnet 5 vs Gemini 3.1 Pro Comparison Table + 1 other

PropertyClaude Sonnet 5Gemini 3.1 ProGPT-5.5
OrganizationAnthropicGoogleOpenAI
Categoryclosedclosedclosed
Modalitymultimodalmultimodalmultimodal
Release DateJun 2026Feb 2026Apr 2026
Context Window1.0M1.0M1.0M
Parameters
LicenseProprietaryProprietaryProprietary
Pricing per 1M tokens
Input $/1M$2.00$2.00$5.00
Output $/1M$10.00$12.00$30.00
Vision Tasks
CaptioningDemoDemoDemo
ClassificationDemoDemoDemo
Object DetectionDemoDemoDemo
OCRDemoDemoDemo
Vision Language
Visual Question AnsweringDemoDemoDemo
Document Question Answering
Multi-Label Classification
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
70.15%
75.76%
77.61%
Avg Response Time3.90s6.13s30.12s
Median input tokensincl. image tokens2.1K1.1K1.4K
Median output tokens6111138
Est. cost / taskon this benchmark$0.0048$0.0024$0.011
Defect Detection
73.3%(11/15)
73.3%(11/15)
86.7%(13/15)
Document Understanding
66.7%(6/9)
88.9%(8/9)
88.9%(8/9)
Object Counting
20%(2/10)
44.4%(4/9)
30%(3/10)
Object Understanding
92.9%(13/14)
92.9%(13/14)
92.9%(13/14)
Spatial Understanding
78.9%(15/19)
73.7%(14/19)
78.9%(15/19)
OCR
Overall Score
83.84%
89.52%
81.22%
Avg Response Time2.77s3.11s5.16s
Median input tokensincl. image tokens6421.1K105
Median output tokens641283
Est. cost / taskon this benchmark$0.0019$0.0024$0.0030
Focused Scene OCR
88.9%(88/99)
94.9%(94/99)
77.8%(77/99)
Handwritten Math
50%(5/10)
90%(9/10)
40%(4/10)
License Plate Recognition
90%(27/30)
90%(27/30)
93.3%(28/30)
Text Recognition
80%(24/30)
86.7%(26/30)
83.3%(25/30)
VQA & Extraction
80%(48/60)
81.7%(49/60)
86.7%(52/60)

Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology