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Claude Opus 5.5 vs Gemini 3 Flash

Compare Claude Opus 5.5 and Gemini 3 Flash side-by-side. See how these vision models stack up in Image Captioning, OCR, Classification, Object Detection, and Open Prompt.

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AnthropicClaude Opus 5.5
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GoogleGemini 3 Flash
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Models in this comparison

Claude Opus 5.5 vs Gemini 3 Flash on Vision Evals

Claude Opus 5.5 scores higher on 4 of the five Vision Evals tasks.

The widest gap is Object Detection, where Claude Opus 5.5 leads 74.5% to 38.6%.

Overall, Claude Opus 5.5 averages 82.0% (#2 of 61) against 65.7% (#25 of 61) for Gemini 3 Flash.

Gemini 3 Flash is both cheaper ($0.0024 vs $0.015 per sample) and faster (5.8s vs 7.6s per sample).

Claude Opus 5.5Gemini 3 Flash

Claude Opus 5.5 vs Gemini 3 Flash Comparison Table

Evals updated October 8, 2026Pricing updated October 10, 2026

PropertyClaude Opus 5.5Gemini 3 Flash
OrganizationAnthropicGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Dec 2025
Context Window1.0M1.0M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$4.00$0.500
Output $/1M$20.00$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
82.0%
65.7%
Avg cost / sample$0.015$0.0024
Avg speed / sample7.63s5.83s
By task
Object Detection (low)
74.5%
±0.5, Mean of 3 runs, range 74.0 to 75.0
$0.021
38.6%
$0.0031
Object Detection (high)
76.0%
±1.1, Mean of 3 runs, range 74.7 to 77.0
$0.030
–
Counting (low)
80.6%
±2.0, Mean of 3 runs, range 78.4 to 82.4
$0.0081
67.6%
$0.0012
Counting (high)
82.0%
±2.0, Mean of 3 runs, range 79.7 to 83.8
$0.0098
–
Identification (low)
93.8%
±0.0, Mean of 3 runs, range 93.8 to 93.8
$0.0058
93.8%
$0.0009
Identification (high)
95.8%
±1.6, Mean of 3 runs, range 93.8 to 96.9
$0.0067
–
OCR (low)
78.3%
$0.016
63.9%
$0.0024
by category
Single value
67.8%
Transcription
96.7%
Structured JSON
91.5%
Text localization
61.0%
Single value
63.9%
Transcription
90.5%
Structured JSON
80.2%
Text localization
7.6%
OCR (high)
79.9%
$0.023
73.1%
$0.013
by category
Single value
69.6%
Transcription
96.1%
Structured JSON
94.1%
Text localization
61.1%
Single value
69.1%
Transcription
90.0%
Structured JSON
87.5%
Text localization
36.9%
Reasoning (low)
83.0%
±1.0, Mean of 3 runs, range 82.1 to 84.1
$0.0090
64.9%
$0.0020
Reasoning (high)
85.9%
±2.6, Mean of 3 runs, range 82.8 to 88.1
$0.011
74.2%
$0.0040

Claude Opus 5.5 vs Gemini 3 Flash: Overview

Claude Opus 5.5

Claude Opus 5.5 is a proprietary multimodal reasoning model from Anthropic and the first entry in the Claude 5.5 family. It accepts interleaved text and image input and returns text, with a one million token context window and up to 128,000 output tokens per response. Adaptive thinking is always enabled on this model and cannot be disabled; thinking depth is instead governed by an effort parameter with five levels, where medium is the default, a change from the high default used by Claude Opus 5 and earlier Opus models. Anthropic reports a knowledge cutoff of June 2026.

On the visual side, Anthropic characterizes Opus 5.5 as its strongest Opus release for vision and computer use, describing improved reading of dense documents, charts, screenshots, and diagrams for document extraction and visual analysis tasks. Published results include 89.0% on Chartography with tools and 81.8% on OSWorld 2.0 under partial credit scoring, alongside 48.7% under strict scoring reported in the system card. The accompanying system card states that Opus 5.5 scored higher than Opus 5 on every evaluation in its capability summary, with the largest gains concentrated in agentic coding, visual reasoning, computer use, and long-horizon knowledge work. The model ships with safety classifiers covering biology and cybersecurity that can route blocked requests to earlier Claude models.

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, Claude Opus 5.5 performed better. It scores higher on 4 of the five vision tasks and averages 82.0% (#2 of 61) against 65.7% (#25 of 61) for Gemini 3 Flash. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.

Yes. On the Vision Evals Object Detection benchmark at low effort, Claude Opus 5.5 leads with 74.5% against 38.6%. 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 $0.015. Claude Opus 5.5 is priced at $4.00 per 1M input tokens and $20.00 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 7.6s. 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.