Gemini 3 Flash vs Llama 4 Maverick

Compare Gemini 3 Flash and Llama 4 Maverick side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.

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GoogleGemini 3 Flash
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MetaLlama 4 Maverick
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Models in this comparison

Gemini 3 Flash vs Llama 4 Maverick: Overview

Gemini 3 Flash

Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.

The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.

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.

Gemini 3 Flash vs Llama 4 Maverick Comparison Table

PropertyGemini 3 FlashLlama 4 Maverick
OrganizationGoogleMeta
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateDec 2025Apr 2025
Context Window1.0M1.0M
Parameters400B
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.500$0.150
Output $/1M$3.00$0.600
Vision Tasks
CaptioningDemoDemo
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
ClassificationDemo
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
74.63%
Avg Response Time9.85s
Median input tokensincl. image tokens1.1K
Median output tokens290
Est. cost / taskon this benchmark$0.0014
Defect Detection
73.3%(11/15)
Document Understanding
88.9%(8/9)
Object Counting
30%(3/10)
Object Understanding
85.7%(12/14)
Spatial Understanding
84.2%(16/19)

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