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Claude Opus 4 vs Llama 4 Maverick

Compare Claude Opus 4 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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AnthropicClaude Opus 4

Claude Opus 4 is deprecated and can no longer be run. Details and evals are still available on its model page.

MetaLlama 4 Maverick
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Models in this comparison

Anthropic

Claude Opus 4 vs Llama 4 Maverick: Overview

Claude Opus 4

Claude 4 Opus, released by Anthropic in May 2025, is the flagship model of the Claude 4 family, built for complex, long-horizon reasoning and advanced coding workflows. It is multimodal, supporting text (including voice), images, and tool use, and operates as a hybrid reasoning model—able to deliver quick answers in fast mode or switch to extended thinking for deeper, multi-step problem solving. With a ~200,000-token context window and a training cutoff around March 2025, it is optimized for handling large documents, long conversations, and sophisticated agentic tasks.

Positioned at the high end of Anthropic’s offerings, Opus 4 achieves state-of-the-art results on coding benchmarks like SWE-Bench (72.5%) and Terminal-Bench (43.2%). It is best suited for research, enterprise automation, and software development at scale. The model is classified at Anthropic’s ASL-3 safety level, denoting advanced oversight and safety features.

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.

Claude Opus 4 vs Llama 4 Maverick Comparison Table

PropertyClaude Opus 4Llama 4 Maverick
OrganizationAnthropicMeta
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateMay 2025Apr 2025
Context Window200K1.0M
Parameters400B
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$15.00$0.150
Output $/1M$75.00$0.600
Vision Tasks
CaptioningDemo
Object Detection
OCRDemo
Vision Language
Visual Question AnsweringDemo
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
56.72%
59.7%
Avg Response Time19.74s2.30s
Median input tokensincl. image tokens2.4K
Median output tokens7
Est. cost / taskon this benchmark$0.0004
Defect Detection
66.7%(10/15)
66.7%(10/15)
Document Understanding
88.9%(8/9)
66.7%(6/9)
Object Counting
0%(0/10)
30%(3/10)
Object Understanding
64.3%(9/14)
64.3%(9/14)
Spatial Understanding
57.9%(11/19)
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