Llama 4 Maverick is a high-performance, multimodal LLM from Meta, excelling in speed and intelligence with competitive pricing.
Llama 4 Maverick emerges as a formidable contender in the large language model landscape, particularly for applications demanding high throughput and multimodal capabilities. Developed by Meta, this model distinguishes itself with an exceptional balance of speed, intelligence, and a generous context window, making it a versatile choice for a wide array of generative AI tasks. Positioned as an open-weight, non-reasoning model, Maverick offers developers and enterprises significant flexibility and control over deployment and fine-tuning.
Our comprehensive benchmarking reveals Llama 4 Maverick's strong performance across key metrics. It scores an impressive 36 on the Artificial Analysis Intelligence Index, placing it above average among comparable models. This indicates a robust understanding and generation capability, suitable for complex content creation, summarization, and question-answering. While it exhibits a tendency towards verbosity, generating 15 million tokens during evaluation compared to an average of 11 million, this can often be managed through careful prompt engineering.
Speed is where Llama 4 Maverick truly shines, achieving an outstanding 128.8 tokens per second in our benchmarks, ranking it #1 in its class. This makes it an ideal candidate for real-time applications and scenarios requiring rapid response times. Pricing is also competitive, with an average input token cost of $0.27 per million and output tokens at $0.85 per million. These figures, especially for input, are significantly below the market average, offering a cost-effective solution for many use cases.
Beyond its core performance, Llama 4 Maverick boasts multimodal input capabilities, supporting both text and image inputs to generate text outputs. This feature unlocks new possibilities for applications like visual content analysis, image captioning, and multimodal conversational agents. With a substantial 1 million token context window and knowledge up to July 2024, the model can process and retain extensive information, facilitating deeper and more coherent interactions over long sessions or complex documents.
36 (14 / 30 / 30)
128.8 tokens/s
$0.27 /M tokens
$0.85 /M tokens
15M tokens
0.19 s
| Spec | Details |
|---|---|
| Model Name | Llama 4 Maverick |
| Owner | Meta |
| License | Open |
| Model Type | Large Language Model (LLM) |
| Architecture | Transformer-based |
| Input Modalities | Text, Image |
| Output Modalities | Text |
| Context Window | 1M tokens |
| Knowledge Cutoff | July 2024 |
| Training Data | Proprietary dataset |
| Fine-tuning | Instruction-tuned |
| Primary Use Cases | Content generation, summarization, Q&A, multimodal applications |
| Performance Class | High-speed, general-purpose |
| Quantization Support | FP8 (from select providers) |
Choosing the right API provider for Llama 4 Maverick is crucial for optimizing performance and cost. Our benchmarks highlight significant variations across providers in terms of output speed, latency, and pricing. Your ideal provider will depend heavily on your primary use case and priorities.
For maximum efficiency, consider providers that leverage advanced quantization like FP8, as these often deliver superior speed and cost benefits. Enterprise users might prioritize reliability and support, while developers focused on cost-efficiency will look for the lowest blended rates.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Prioritize Speed | SambaNova, Groq | SambaNova (697 t/s) and Groq (435 t/s) are the undisputed leaders for raw output speed. | May not offer the absolute lowest latency or blended price. |
| Prioritize Latency | Groq, Deepinfra (FP8) | Groq (0.19s) and Deepinfra (FP8) (0.30s) provide the quickest time to first token. | Slightly higher output speed than the absolute fastest, but still excellent. |
| Prioritize Blended Cost | Deepinfra (FP8), Groq | Deepinfra (FP8) ($0.26/M) and Groq ($0.30/M) offer the most cost-effective overall pricing. | Other providers might offer better specific metrics (e.g., latency) at a slightly higher blended cost. |
| Prioritize Input Cost | Deepinfra (FP8), Novita (FP8) | Deepinfra (FP8) ($0.15/M) and Novita (FP8) ($0.17/M) are cheapest for input tokens. | Output token prices might be higher, impacting total cost for verbose outputs. |
| Prioritize Output Cost | Deepinfra (Turbo, FP8), Snowflake | Deepinfra (Turbo, FP8) ($0.50/M) and Snowflake ($0.50/M) lead for output token pricing. | Input token prices might be slightly higher, but ideal for applications with high output volume. |
| Balanced Performance | Together.ai, Google Vertex | Together.ai offers a good balance of speed (0.33s latency, $0.41/M blended) and Google Vertex provides enterprise-grade reliability. | Not the absolute best in any single metric, but a strong all-rounder. |
| Enterprise Scale | Microsoft Azure (FP8), Amazon Bedrock | These platforms offer robust infrastructure, security, and support for large-scale deployments. | Often come with higher pricing compared to specialized API providers. |
Note: Prices and performance are subject to change and may vary based on specific API configurations, region, and usage patterns.
Understanding the real-world cost of Llama 4 Maverick involves more than just looking at per-token prices. It depends heavily on your application's specific input and output patterns, the complexity of the tasks, and the chosen API provider. Here, we illustrate estimated costs for common scenarios using average pricing for Llama 4 Maverick ($0.27/M input, $0.85/M output).
These estimates provide a general idea; actual costs will fluctuate based on provider, quantization, and the exact token counts generated by the model for your specific prompts.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Short Q&A | 100 tokens (question) | 50 tokens (answer) | User asks a concise question, model provides a brief, direct answer. | ~$0.00007 |
| Document Summarization | 50,000 tokens (long article) | 1,000 tokens (summary) | Condensing a detailed report or research paper into a digestible summary. | ~$0.01435 |
| Multimodal Content Gen. | Image + 200 tokens (prompt) | 500 tokens (description) | Generating a detailed text description or story based on an image and a short prompt. | ~$0.00048 |
| Chatbot Interaction (Avg.) | 500 tokens (user turns) | 750 tokens (model turns) | A typical back-and-forth conversation over several turns, averaged per interaction. | ~$0.00077 |
| Code Generation/Refactoring | 2,000 tokens (code + instructions) | 3,000 tokens (generated code) | Providing existing code or requirements for the model to generate new code or refactor. | ~$0.00309 |
| Data Extraction (Large Text) | 100,000 tokens (raw data) | 200 tokens (structured output) | Extracting specific entities or facts from a very large document into a concise format. | ~$0.02717 |
For Llama 4 Maverick, applications with high input token counts (like summarization or data extraction) will see costs dominated by input pricing, while highly verbose output-generating tasks (like creative writing or extensive code generation) will be more sensitive to output token rates. Its competitive input pricing makes it particularly attractive for processing large amounts of user-provided data.
Optimizing costs for Llama 4 Maverick involves a multi-faceted approach, leveraging its strengths and mitigating its verbosity. Given its open-weight nature and diverse API provider ecosystem, you have significant control over your expenditure.
The key is to align your technical implementation with your business objectives, ensuring you're not overpaying for capabilities you don't need or under-optimizing for high-volume scenarios.
The choice of API provider dramatically impacts cost and performance. Benchmark providers for your specific use case.
Careful prompt design can reduce both input and output token counts, directly impacting costs.
Given Llama 4 Maverick's tendency towards verbosity, actively managing its output is crucial for cost control.
Reduce redundant API calls and optimize throughput with intelligent caching and batching.
While powerful, multimodal inputs can be more resource-intensive. Use them strategically.
Llama 4 Maverick is a large language model developed by Meta. It is an open-weight, non-reasoning model known for its high speed, above-average intelligence, multimodal capabilities (text and image input), and a substantial 1 million token context window.
While specific comparisons to other Llama versions (e.g., Llama 3) are not detailed here, Llama 4 Maverick distinguishes itself by its exceptional speed and multimodal input support. It builds upon the Llama family's reputation for strong performance and open-weight accessibility, offering advancements in efficiency and versatility.
Its primary strengths include industry-leading output speed, above-average intelligence for generative tasks, competitive input token pricing, multimodal input (text and image), a large 1M token context window, and an open license that fosters broad adoption and customization.
Llama 4 Maverick can be somewhat verbose, potentially leading to higher output token costs if not managed. It is classified as a 'non-reasoning' model, meaning complex logical inference might require more deliberate prompting. Performance and cost also vary significantly across different API providers.
Yes, Llama 4 Maverick supports multimodal input, meaning it can take both text and image inputs to generate text outputs. This enables applications like image captioning, visual question answering, and content generation based on visual cues.
Llama 4 Maverick features a generous 1 million token context window. This allows the model to process and maintain coherence over very long documents, extensive conversations, or complex datasets within a single interaction.
Llama 4 Maverick is an open-weight model, which means its weights are publicly available for download and use, often under a permissive license. This allows for greater transparency, research, and the ability for developers to run and fine-tune the model on their own infrastructure.
Llama 4 Maverick can be accessed through various API providers such as Parasail, Novita, Google Vertex, Deepinfra, Together.ai, Microsoft Azure, Amazon Bedrock, Databricks, SambaNova, Groq, and Snowflake. You can also download and run the open-weight model on your own hardware, subject to its license terms.