Claude Opus 4.6 vs Gemma 4 31B

Compare Claude Opus 4.6 and Gemma 4 31B side-by-side. See how these vision models stack up in Open Prompt, OCR, Object Detection, Classification, and Image Captioning.

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AnthropicClaude Opus 4.6
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GoogleGemma 4 31B
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Models in this comparison

Claude Opus 4.6 vs Gemma 4 31B: 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 31B

Gemma 4 31B is the largest dense model in Google's Gemma 4 family, built from the same research as Gemini 3 and released as open weights under the Apache 2.0 license. It supports a 256K token context window with text and image input, configurable thinking mode for step-by-step reasoning, and multilingual support across 140+ languages. The unquantized model fits on a single 80GB GPU.

For vision tasks, Gemma 4 31B supports image understanding with variable aspect ratios and resolutions, and can output structured bounding boxes for UI element detection, making it useful for document parsing and UI understanding. Compared to Gemma 3, it delivers stronger reasoning and multimodal performance. It is part of a four-size family alongside the 26B A4B MoE variant and two on-device models (E2B, E4B), with the 31B dense variant optimized for output quality and fine-tuning over inference speed.

Claude Opus 4.6 vs Gemma 4 31B Comparison Table

PropertyClaude Opus 4.6 Gemma 4 31B
OrganizationAnthropicGoogle
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateFeb 2026Apr 2026
Context Window1.0M256K
Parameters31B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$5.00$0.120
Output $/1M$25.00$0.350
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
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%
67.16%
Avg Response Time23.35s34.59s
Median input tokensincl. image tokens2.2K294
Median output tokens130169
Est. cost / taskon this benchmark$0.014$0.0001
Defect Detection
73.3%(11/15)
80%(12/15)
Document Understanding
77.8%(7/9)
88.9%(8/9)
Object Counting
20%(2/10)
10%(1/10)
Object Understanding
71.4%(10/14)
71.4%(10/14)
Spatial Understanding
68.4%(13/19)
73.7%(14/19)

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