Roboflow

Claude Opus 4 vs Gemini 2.5 Pro

Compare Claude Opus 4 and Gemini 2.5 Pro side-by-side. See how these vision models stack up in Image Captioning, OCR, Object Detection, Open Prompt, and Classification.

Compare Claude Opus 4 vs Gemini 2.5 Pro 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 Opus 4

Claude Opus 4 is deprecated and can no longer be run. Details and evals are still available on its model page.

GoogleGemini 2.5 Pro
Run to compare this model.

Models in this comparison

Anthropic

Claude Opus 4 vs Gemini 2.5 Pro: Overview

Claude Opus 4

Claude 4 Opus, released by Anthropic in May 2025, is the flagship model of the Claude 4 family, built for complex, long-horizon reasoning and advanced coding workflows. It is multimodal, supporting text (including voice), images, and tool use, and operates as a hybrid reasoning model—able to deliver quick answers in fast mode or switch to extended thinking for deeper, multi-step problem solving. With a ~200,000-token context window and a training cutoff around March 2025, it is optimized for handling large documents, long conversations, and sophisticated agentic tasks.

Positioned at the high end of Anthropic’s offerings, Opus 4 achieves state-of-the-art results on coding benchmarks like SWE-Bench (72.5%) and Terminal-Bench (43.2%). It is best suited for research, enterprise automation, and software development at scale. The model is classified at Anthropic’s ASL-3 safety level, denoting advanced oversight and safety features.

Gemini 2.5 Pro

Gemini 2.5 Pro, released on June 17, 2025, is Google DeepMind’s most capable model in the Gemini 2.5 family, optimized for deep reasoning, coding, and complex multimodal tasks. It accepts text, images, audio, video, and PDFs as input and outputs text. The model supports 1 million input tokens with an output capacity of up to 65K tokens, enabling large-scale comprehension of datasets, codebases, and technical documents. Its training knowledge extends to January 2025.

Pro outperforms earlier Gemini 2.0 models across benchmarks, including agentic coding tasks where it achieved ~63.8% on SWE-Bench Verified. It supports structured outputs, function calling, code execution, search grounding, and URL context, making it well-suited for enterprise, STEM, and developer workflows. However, it does not currently support image or audio generation in its stable release, and its higher computational cost and latency make it less efficient than Flash or Flash-Lite. It is available via the Gemini API, Google AI Studio, and Vertex AI.

Claude Opus 4 vs Gemini 2.5 Pro Comparison Table

PropertyClaude Opus 4Gemini 2.5 Pro
OrganizationAnthropicGoogle
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateMay 2025Jun 2025
Context Window200K1.0M
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$15.00$1.25
Output $/1M$75.00$10.00
Vision Tasks
CaptioningDemo
ClassificationDemo
Object DetectionDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
Model Features
Foundation Vision
LLMs with Vision Capabilities
Multimodal Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
56.72%
70.15%
Avg Response Time19.74s11.87s
Median input tokensincl. image tokens294
Median output tokens565
Est. cost / taskon this benchmark$0.0060
Defect Detection
66.7%(10/15)
73.3%(11/15)
Document Understanding
88.9%(8/9)
88.9%(8/9)
Object Counting
0%(0/10)
20%(2/10)
Object Understanding
64.3%(9/14)
78.6%(11/14)
Spatial Understanding
57.9%(11/19)
78.9%(15/19)
OCR
Overall Score
78.6%
Avg Response Time4.91s
Median input tokensincl. image tokens290
Median output tokens323
Est. cost / taskon this benchmark$0.0036
Focused Scene OCR
78.8%(78/99)
Handwritten Math
80%(8/10)
License Plate Recognition
90%(27/30)
Text Recognition
73.3%(22/30)
VQA & Extraction
75%(45/60)

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