Meta Unveils Llama 4: The Open-Source Multimodal Milestone

In terms of raw performance, Llama 4 has set new benchmarks across standard evaluation suites. On the MMLU benchmark, the Maverick model achieved a score of 88.5%, surpassing previous industry leaders by a significant margin. For coding tasks, the HumanEval score reached 92.1%, demonstrating exceptional proficiency in generating functional Python code without external libraries. These results indicate that the model is not only capable of general knowledge retrieval but also possesses deep reasoning skills necessary for complex software engineering tasks.

Safety and alignment have also been prioritized in the Llama 4 release. The model demonstrates a reduction in hallucination rates compared to Llama 3, particularly in long-context scenarios. On the SWE-bench evaluation, Llama 4 Maverick successfully resolved 78% of the issues in a single attempt, a metric that is critical for enterprise adoption. The multimodal capabilities were tested on specialized video reasoning tasks, where the model outperformed competitors by correctly identifying causal relationships in video sequences that text-only models missed entirely.

• MMLU Score: 88.5% (Maverick)
• HumanEval: 92.1%
• SWE-bench: 78% Single Attempt
• Multimodal Video Reasoning: Top Tier

API Pricing

While the weights are open-source, Meta is also offering API access for developers who prefer managed infrastructure. The pricing structure is designed to be competitive with other major providers. Input tokens are priced at $0.002 per million tokens, while output tokens cost $0.006 per million tokens. This pricing model makes Llama 4 highly cost-effective for high-volume applications where context length is extensive. For developers running the open weights locally, the cost is entirely zero, provided they have the necessary hardware infrastructure.

A free tier is available for developers to test the API capabilities without immediate commitment. This tier allows for 10,000 tokens per month in input and output combined. This is sufficient for prototyping and small-scale applications. The value comparison against competitors shows that for tasks requiring 10M token context, Llama 4 is significantly cheaper than proprietary alternatives that charge per token regardless of context depth. This pricing strategy is intended to encourage widespread adoption and community contribution.

• Input Price: $0.002 / Million Tokens
• Output Price: $0.006 / Million Tokens
• Free Tier: 10k Tokens/Month
• Open Weights: Free (Self-Hosted)

Comparison Table

To contextualize the performance and pricing of Llama 4 against the current market leaders, we have compiled a direct comparison table. This analysis highlights the advantages of Llama 4's open-weight nature and multimodal fusion. Developers can use this data to decide whether to host the model locally or utilize the managed API. The table below details the context windows, output limits, and pricing structures for the top three models in the current landscape.

• Direct comparison of top models
• Focus on context and cost efficiency
• Highlights open-weight advantages

Use Cases

Llama 4 is versatile enough to support a wide range of applications, from enterprise knowledge bases to creative coding assistants. For RAG (Retrieval-Augmented Generation) systems, the 10M token context window allows developers to ingest entire company documentation without truncation. This is particularly useful for legal and medical domains where context precision is paramount. The multimodal capabilities also open doors for video analysis tools, allowing security systems to summarize footage or educational platforms to analyze student interactions in real-time.

In the realm of software engineering, the Maverick model is ideal for autonomous agents that need to reason over codebases. These agents can read documentation, write tests, and fix bugs within the context of a full repository. Additionally, the early fusion architecture supports advanced chat interfaces that can interpret images and videos alongside text prompts. This makes it suitable for customer support bots that can analyze screenshots of errors to provide more accurate troubleshooting steps than text-only models.

• Enterprise RAG Systems
• Autonomous Coding Agents
• Video Analysis & Summarization
• Multimodal Customer Support

Getting Started

Accessing Llama 4 is straightforward for both API users and local developers. For API access, developers can sign up via the Meta AI developer portal using standard OAuth credentials. The SDK supports Python, JavaScript, and Go, ensuring compatibility with most modern tech stacks. For those preferring self-hosting, the model weights are available on Hugging Face under the official Meta repository. This allows for full control over the deployment environment, data privacy, and fine-tuning processes.

Documentation is extensive, covering installation guides, inference optimization, and fine-tuning tutorials. The official blog post provides a deep dive into the architecture changes. To begin, developers should clone the repository, install the dependencies, and run the provided inference script. For the Maverick model, ensure you have access to an H100 DGX system before attempting deployment. The community is actively contributing to the ecosystem, so checking the GitHub issues page is recommended for the latest updates and optimization tips.

• Access: Meta AI Developer Portal
• SDK Support: Python, JS, Go
• Weights: Hugging Face Official Repo
• Docs: Official Meta AI Blog

Comparison

API Pricing — Input: $0.002 / Output: $0.006 / Context: 10M Tokens

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