Claude Opus 4.1 vs Gemma 3 4B

Compare Claude Opus 4.1 and Gemma 3 4B side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.

Compare Claude Opus 4.1 vs Gemma 3 4B live

Run the same image across every model that supports a task and compare their outputs side-by-side.

Extract and compare text from images across multiple models.

Open OCR in the full playground
AnthropicClaude Opus 4.1
Run to compare this model.
GoogleGemma 3 4B
Run to compare this model.

Models in this comparison

Claude Opus 4.1 vs Gemma 3 4B: Overview

Claude Opus 4.1

Claude 4.1 Opus, released by Anthropic in August 2025, is the upgraded flagship of the Claude 4 family, building on Opus 4 with stronger reasoning and agentic capabilities. Like its predecessor, it is multimodal and optimized for text, code, and tool use, with support for large context windows suited to multi-file codebases, technical workflows, and long-horizon problem solving.

On benchmarks, Opus 4.1 improves coding performance, reaching ~74.5% on SWE-Bench Verified compared to Opus 4’s ~72.5%. It demonstrates more precise debugging, refactoring, and orchestration of agentic tasks while maintaining similar safety and alignment safeguards. It is best suited for enterprise-scale software development, research automation, and advanced reasoning workflows where reliability and depth of analysis are critical.

Gemma 3 4B

Gemma 3 4B, released on March 12, 2025, is the mid-sized member of Google DeepMind’s open-weight Gemma 3 family. With about 4 billion parameters, it is multimodal—supporting text and image inputs and generating text outputs. Like the larger Gemma 3 models, it features a 128,000-token input context window with an output capacity of ~8,192 tokens, enabling it to handle long documents and mixed text–image reasoning tasks.

The 4B variant is designed as a balance between efficiency and capability: it offers multilingual support across 140+ languages, strong summarization and reasoning performance, and compatibility with moderate hardware. Inference can run with ~6.4 GB VRAM in BF16, or significantly less in quantized 8-bit (~4.4 GB) or 4-bit (~3.4 GB) modes, making it accessible to developers outside large-scale infrastructure. While it lags behind the 12B and 27B versions on the most complex reasoning and multimodal benchmarks, its lower compute footprint makes it ideal for research, prototyping, and practical deployment where efficiency matters.

Claude Opus 4.1 vs Gemma 3 4B Comparison Table

PropertyClaude Opus 4.1Gemma 3 4B
OrganizationAnthropicGoogle
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateAug 2025Mar 2025
Context Window200K128K
Parameters4B
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$15.00$0.050
Output $/1M$75.00$0.100
Vision Tasks
CaptioningDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
ClassificationDemo
Object DetectionDemo
Model Features
Multimodal Vision
Foundation Vision
LLMs with Vision Capabilities
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
59.7%
37.31%
Avg Response Time7.09s16.80s
Median input tokensincl. image tokens2.0K
Median output tokens140
Est. cost / taskon this benchmark$0.040
Defect Detection
73.3%(11/15)
60%(9/15)
Document Understanding
88.9%(8/9)
55.6%(5/9)
Object Counting
0%(0/10)
0%(0/10)
Object Understanding
64.3%(9/14)
42.9%(6/14)
Spatial Understanding
63.2%(12/19)
26.3%(5/19)

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