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Grok 4 vs Llama 4 Maverick

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

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GrokGrok 4
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MetaLlama 4 Maverick
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Models in this comparison

Grok

Grok 4 vs Llama 4 Maverick: Overview

Grok 4

Grok 4, released by xAI on July 9, 2025, is the fourth-generation model in the Grok family and the most advanced to date. It is multimodal, supporting text, vision, tool use, and real-time web search, with a reported 256,000-token context window for long-form reasoning and document analysis. Its training data extends through November 2024, making it the most up-to-date Grok model at launch.

The lineup includes Grok 4 Generalist for broad tasks, Grok 4 Heavy for higher-capacity reasoning, and Grok 4 Code optimized for programming and debugging. A notable feature is its always-on “Think” mode, designed for deeper multi-step reasoning. While xAI has not disclosed parameter counts, Grok 4 is positioned to compete with frontier models like GPT-5 and Claude 4, balancing real-time knowledge via web integration with structured tool use. It is best suited for coding, complex reasoning, and multimodal AI assistants.

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.

Grok 4 vs Llama 4 Maverick Comparison Table

PropertyGrok 4Llama 4 Maverick
OrganizationxAIMeta
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateJul 2025Apr 2025
Context Window256K1.0M
Parameters400B
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$0.150
Output $/1M$0.600
Vision Tasks
CaptioningDemoDemo
Object Detection
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Classification
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
52.24%
59.7%
Avg Response Time85.24s2.30s
Median input tokensincl. image tokens2.4K
Median output tokens7
Est. cost / taskon this benchmark$0.0004
Defect Detection
80%(12/15)
66.7%(10/15)
Document Understanding
44.4%(4/9)
66.7%(6/9)
Object Counting
10%(1/10)
30%(3/10)
Object Understanding
57.1%(8/14)
64.3%(9/14)
Spatial Understanding
52.6%(10/19)
63.2%(12/19)
OCR
Overall Score
78.6%
Avg Response Time0.87s
Median input tokensincl. image tokens472
Median output tokens10
Est. cost / taskon this benchmark$0.0001
Focused Scene OCR
76.8%(76/99)
Handwritten Math
60%(6/10)
License Plate Recognition
93.3%(28/30)
Text Recognition
83.3%(25/30)
VQA & Extraction
75%(45/60)

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