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Claude Opus 5.5 vs Gemini 3.1 Pro

Compare Claude Opus 5.5 and Gemini 3.1 Pro 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 3.1 Pro
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Models in this comparison

Claude Opus 5.5 vs Gemini 3.1 Pro on Vision Evals

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

The widest gap is Reasoning, where Claude Opus 5.5 leads 83.0% to 72.2%.

Overall, Claude Opus 5.5 averages 82.0% (#2 of 61) against 76.8% (#11 of 61) for Gemini 3.1 Pro.

Gemini 3.1 Pro is cheaper ($0.010 vs $0.015 per sample), while Claude Opus 5.5 is faster (7.6s vs 8.4s per sample).

Claude Opus 5.5Gemini 3.1 Pro

Claude Opus 5.5 vs Gemini 3.1 Pro Comparison Table

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

PropertyClaude Opus 5.5Gemini 3.1 Pro
OrganizationAnthropicGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateSep 2026Feb 2026
Context Window1.0M1.0M
ParametersUnknownUnknown
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$4.00$2.00
Output $/1M$20.00$12.00
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%
76.8%
Avg cost / sample$0.015$0.010
Avg speed / sample7.63s8.44s
By task
Object Detection (low)
74.5%
±0.5, Mean of 3 runs, range 74.0 to 75.0
$0.021
67.4%
$0.010
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
71.6%
$0.0071
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
100.0%
$0.0070
Identification (high)
95.8%
±1.6, Mean of 3 runs, range 93.8 to 96.9
$0.0067
–
OCR (low)
78.3%
$0.016
72.6%
$0.010
by category
Single value
67.8%
Transcription
96.7%
Structured JSON
91.5%
Text localization
61.0%
Single value
67.8%
Transcription
88.5%
Structured JSON
82.0%
Text localization
52.0%
OCR (high)
79.9%
$0.023
73.1%
$0.026
by category
Single value
69.6%
Transcription
96.1%
Structured JSON
94.1%
Text localization
61.1%
Single value
68.7%
Transcription
90.8%
Structured JSON
83.9%
Text localization
47.1%
Reasoning (low)
83.0%
±1.0, Mean of 3 runs, range 82.1 to 84.1
$0.0090
72.2%
$0.012
Reasoning (high)
85.9%
±2.6, Mean of 3 runs, range 82.8 to 88.1
$0.011
74.8%
$0.021

Claude Opus 5.5 vs Gemini 3.1 Pro: 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 3.1 Pro

Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.

The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.

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 76.8% (#11 of 61) for Gemini 3.1 Pro. 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 5.5 leads with 83.0% against 72.2%. This is the widest gap between the two models across the benchmark's tasks.

Gemini 3.1 Pro is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.010 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 3.1 Pro is priced at $2.00 per 1M input tokens and $12.00 per 1M output. Actual costs depend on your image sizes, prompts, and output length.

Claude Opus 5.5 is faster. Across Roboflow's Vision Evals it averaged 7.6s per inference against 8.4s. 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.