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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Grok 4 vs Llama 4 Maverick: Overview
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, 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
| Property | Grok 4 | Llama 4 Maverick |
|---|---|---|
| Organization | xAI | Meta |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2025 | Apr 2025 |
| Context Window | 256K | 1.0M |
| Parameters | 400B | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.150 | |
| Output $/1M | $0.600 | |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | ||
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| 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 Time | 85.24s | 2.30s |
| Median input tokensincl. image tokens | 2.4K | |
| Median output tokens | 7 | |
| 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 Time | 0.87s | |
| Median input tokensincl. image tokens | 472 | |
| Median output tokens | 10 | |
| 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