Roboflow

Claude Sonnet 5 vs Gemini 3 Flash

Compare Claude Sonnet 5 and Gemini 3 Flash side-by-side. See how these vision models stack up in Object Detection, Open Prompt, OCR, Classification, and Image Captioning.

Compare Claude Sonnet 5 vs Gemini 3 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 Sonnet 5
Run to compare this model.
GoogleGemini 3 Flash
Run to compare this model.

Models in this comparison

Claude Sonnet 5 vs Gemini 3 Flash on Vision Evals

Gemini 3 Flash scores higher on all five Vision Evals tasks.

The widest gap is Reasoning, where Gemini 3 Flash leads 64.9% to 43.0%.

Overall, Claude Sonnet 5 averages 55.3% (#47 of 63) against 65.7% (#27 of 63) for Gemini 3 Flash.

Gemini 3 Flash is both cheaper ($0.0024 vs $0.0074 per sample) and faster (5.8s vs 5.9s per sample).

Claude Sonnet 5Gemini 3 Flash

Claude Sonnet 5 vs Gemini 3 Flash Comparison Table

Evals updated October 13, 2026Pricing updated October 19, 2026

PropertyClaude Sonnet 5Gemini 3 Flash
OrganizationAnthropicGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateJun 2026Dec 2025
Context Window1.0M1.0M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Commercial usecommercial use per vendor termscommercial use per vendor terms
Pricing per 1M tokens
Input $/1M$2.00$0.500
Output $/1M$10.00$3.00
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%
65.7%
Avg cost / sample$0.0074$0.0024
Avg speed / sample5.86s5.83s
By task
Object Detection
36.1%
$0.011
38.6%
$0.0031
Counting
56.8%
$0.0030
67.6%
$0.0012
Identification
81.3%
$0.0027
93.8%
$0.0009
OCR (low)
59.4%
$0.0078
63.9%
$0.0024
by category
Single value
52.2%
Transcription
88.6%
Structured JSON
78.3%
Text localization
13.1%
Single value
63.9%
Transcription
90.5%
Structured JSON
80.2%
Text localization
7.6%
OCR (high)
59.5%
$0.015
73.1%
$0.013
by category
Single value
54.8%
Transcription
88.3%
Structured JSON
76.6%
Text localization
11.6%
Single value
69.1%
Transcription
90.0%
Structured JSON
87.5%
Text localization
36.9%
Reasoning (low)
43.0%
$0.0032
64.9%
$0.0020
Reasoning (high)
43.0%
$0.0043
74.2%
$0.0040

Claude Sonnet 5 vs Gemini 3 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.

Gemini 3 Flash

Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.

The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.

Frequently Asked Questions

On Roboflow's Vision Evals, Gemini 3 Flash performed better. It scores higher on all five vision tasks and averages 65.7% (#27 of 63) against 55.3% (#47 of 63) for Claude Sonnet 5. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

No. On the Vision Evals Reasoning benchmark at low effort, Gemini 3 Flash leads with 64.9% against 43.0%. This is the widest gap between the two models across the benchmark's tasks.

Gemini 3 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0024 per sample against $0.0074. Claude Sonnet 5 is priced at $2.00 per 1M input tokens and $10.00 per 1M output; Gemini 3 Flash is priced at $0.50 per 1M input tokens and $3.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Gemini 3 Flash is faster. Across Roboflow's Vision Evals it averaged 5.8s per inference against 5.9s. 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 object detection and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.