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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Claude Opus 4 is deprecated and can no longer be run. Details and evals are still available on its model page.
Models in this comparison
Claude Opus 4 vs Llama 4 Maverick: Overview
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, 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
| Property | Claude Opus 4 | Llama 4 Maverick |
|---|---|---|
| Organization | Anthropic | Meta |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | May 2025 | Apr 2025 |
| Context Window | 200K | 1.0M |
| Parameters | 400B | |
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $15.00 | $0.150 |
| Output $/1M | $75.00 | $0.600 |
| Vision Tasks | ||
| Captioning | Demo | |
| Object Detection | ||
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | 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 | 56.72% | 59.7% |
| Avg Response Time | 19.74s | 2.30s |
| Median input tokensincl. image tokens | 2.4K | |
| Median output tokens | 7 | |
| 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 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