Claude Opus 4.6 vs Gemma 4 12B

Compare Claude Opus 4.6 and Gemma 4 12B side-by-side.

Compare Claude Opus 4.6 vs Gemma 4 12B live

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

These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.

Models in this comparison

Claude Opus 4.6 vs Gemma 4 12B: Overview

Claude Opus 4.6

Claude Opus 4.6 is the flagship large language model from Anthropic, released on 2026-02-05 for advanced reasoning, complex coding, and enterprise agent workflows. It supports text and image inputs via API, offers a 200K-token standard context window with a 1M-token beta option, and enables outputs up to 128K tokens, with adaptive reasoning and context compaction for sustained tasks.

As of 2026-02-17, Anthropic also released Claude Sonnet 4.6, extending the 1M-token context window to a broader tier. Opus remains positioned for maximum depth and benchmark performance, while Sonnet 4.6 brings long-context capability to more cost- and latency-sensitive production use cases.

Gemma 4 12B

Gemma 4 12B is an open-weight multimodal model from Google in the Gemma 4 family. It is intended for text and image understanding tasks such as visual question answering, OCR, captioning, and document understanding, with a smaller parameter footprint than the larger Gemma 4 variants.

This entry is connected to Roboflow Playground vision evals for comparison. No runnable Playground workflow is configured yet, so the model page is used for discovery and benchmark context rather than direct hosted inference.

Claude Opus 4.6 vs Gemma 4 12B Comparison Table

PropertyClaude Opus 4.6 Gemma 4 12B
OrganizationAnthropicGoogle
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateFeb 2026Jun 2026
Context Window1.0M
Parameters12B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$5.00
Output $/1M$25.00
Vision Tasks
CaptioningDemo
OCRDemo
Vision Language
Visual Question AnsweringDemo
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
64.18%
62.69%
Avg Response Time23.35s6.88s
Median input tokensincl. image tokens2.2K
Median output tokens130
Est. cost / taskon this benchmark$0.014
Defect Detection
73.3%(11/15)
73.3%(11/15)
Document Understanding
77.8%(7/9)
88.9%(8/9)
Object Counting
20%(2/10)
10%(1/10)
Object Understanding
71.4%(10/14)
78.6%(11/14)
Spatial Understanding
68.4%(13/19)
57.9%(11/19)

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