Claude Opus 4.7 vs Gemini 3.5 Flash

Compare Claude Opus 4.7 and Gemini 3.5 Flash side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Object Detection, and Open Prompt.

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AnthropicClaude Opus 4.7
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GoogleGemini 3.5 Flash
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Models in this comparison

Claude Opus 4.7 vs Gemini 3.5 Flash: Overview

Claude Opus 4.7

Claude Opus 4.7 is a proprietary multimodal language model developed by Anthropic, released on April 16, 2026. It is designed for agentic coding, long-horizon task execution, and enterprise knowledge work. The model supports text and vision inputs and operates with a context window of up to 1,000,000 tokens. It introduces adaptive thinking, which dynamically allocates reasoning based on task complexity, along with configurable effort controls including a new xhigh setting that sits between the existing high and max levels. It achieves 87.6% on SWE-bench Verified and 78.0% on OSWorld-Verified, reflecting strong performance on autonomous software engineering and computer use tasks respectively.

Compared to Claude Opus 4.6, version 4.7 shows improved instruction following and higher reliability in extended agentic tasks. Vision capabilities now support high-resolution inputs up to 2,576px on the long edge (~3.75 megapixels), more than three times the resolution of prior Claude models, enabling finer interpretation of dense diagrams, UI screenshots, and document layouts. These improvements, combined with self-verification on long-running tasks and a new task budget system for controlling agentic loops, make it well-suited for complex software engineering, technical analysis, and multimodal vision workflows.

Gemini 3.5 Flash

Gemini 3.5 Flash is a multimodal language model developed by Google DeepMind and released at Google I/O 2026. It is built on the Gemini 3 Flash reasoning foundation and introduces configurable thinking levels (minimal, low, medium, and high) that allow developers to tune the depth of internal reasoning before a response is generated. The model accepts text, image, video, audio, and PDF inputs and produces text output, with a 1 million token context window and up to 65,000 output tokens per request. It is natively multimodal, processing visual inputs alongside text to support tasks such as image captioning, classification, optical character recognition, object detection, and visual grounding, where the model references specific regions within an image or video frame.

Its vision capabilities extend to interpreting UI screenshots, diagrams, charts, and real-world scenes, as well as understanding video and live frame sequences for activity and scene recognition. The model supports combined tool use, including Google Search, URL context, code execution, and custom functions, within a single request, and it uses reasoning context from previous turns when thought signatures are present in the conversation history, enabling persistent multi-turn reasoning chains. Gemini 3.5 Flash carries a knowledge cutoff of January 2026 and is available via the Gemini API, Google AI Studio, Google Antigravity, and the Gemini Enterprise Agent Platform.

Claude Opus 4.7 vs Gemini 3.5 Flash Comparison Table

PropertyClaude Opus 4.7Gemini 3.5 Flash
OrganizationAnthropicGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateApr 2026May 2026
Context Window1.0M1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$5.00$1.50
Output $/1M$25.00$9.00
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Visual Question AnsweringDemoDemo
Chart Question Answering
Document Question Answering
Multi-Label Classification
Vision Language
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
67.16%
79.1%
Avg Response Time4.85s6.71s
Median input tokensincl. image tokens2.4K1.1K
Median output tokens110294
Est. cost / taskon this benchmark$0.015$0.0043
Defect Detection
73.3%(11/15)
80%(12/15)
Document Understanding
77.8%(7/9)
77.8%(7/9)
Object Counting
20%(2/10)
60%(6/10)
Object Understanding
85.7%(12/14)
92.9%(13/14)
Spatial Understanding
68.4%(13/19)
78.9%(15/19)

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