Gemma 4 31B vs Llama 4 Maverick

Compare Gemma 4 31B and Llama 4 Maverick side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

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GoogleGemma 4 31B
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MetaLlama 4 Maverick
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Gemma 4 31B vs Llama 4 Maverick: Overview

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.

Llama 4 Maverick

Llama 4 Maverick, introduced on April 5, 2025, is one of the first models in Meta’s Llama 4 family, designed as a natively multimodal model supporting text + image inputs with text outputs. It employs a Mixture-of-Experts (MoE) architecture with 128 experts, activating ~17B parameters per token out of a pool of ~400B total parameters. This design improves scalability, efficiency, and reasoning capacity. Maverick has a 1M-token context window, enabling it to handle large documents, extended conversations, and multimodal reasoning. Its knowledge cutoff is August 2024.

The model is released under the Llama 4 Community License and comes in both base and instruction-tuned (“Instruct”) versions. Maverick is widely deployed via Hugging Face, Google Vertex AI, Amazon Bedrock, and Oracle Cloud, making it one of the most accessible large open-weight models. However, it outputs text only (no image/audio generation) and, while input capacity is huge, output limits are typically much smaller. The MoE design also raises hardware demands, as maintaining 128 experts requires significant compute resources, and Meta’s license introduces restrictions around commercial-scale use.

Gemma 4 31B vs Llama 4 Maverick Comparison Table

PropertyGemma 4 31BLlama 4 Maverick
OrganizationGoogleMeta
Categoryopenopen
Modalitymultimodalmultimodal
Release DateApr 2026Apr 2025
Context Window256K1.0M
Parameters31B400B
LicenseApache 2.0Proprietary
Pricing per 1M tokens
Input $/1M$0.120$0.150
Output $/1M$0.350$0.600
Vision Tasks
CaptioningDemoDemo
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
classificationDemo
Model Features
Multimodal Vision
LLMs with Vision Capabilities
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
67.16%
Avg Response Time34.59s
Median input tokensincl. image tokens294
Median output tokens169
Est. cost / taskon this benchmark$0.0001
Defect Detection
80%(12/15)
Document Understanding
88.9%(8/9)
Object Counting
10%(1/10)
Object Understanding
71.4%(10/14)
Spatial Understanding
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