Llama 4 Scout stands out as a leading open-weight multimodal model, offering exceptional intelligence, speed, and competitive pricing for a wide range of applications.
Llama 4 Scout, developed by Meta, emerges as a formidable contender in the large language model landscape, particularly for its balanced blend of high intelligence, impressive speed, and accessible pricing. Positioned as an open-weight model, it offers developers and enterprises significant flexibility and control, making it an attractive option for diverse AI-powered applications. Its multimodal capabilities, supporting both text and image input, further broaden its utility, enabling more sophisticated and context-aware interactions.
Benchmarked across critical performance metrics, Llama 4 Scout consistently demonstrates strong results. It achieves an Artificial Analysis Intelligence Index score of 28, placing it among the top models and significantly above the average for its class. This high intelligence is complemented by a remarkable output speed of 137 tokens per second, ensuring efficient processing for demanding workloads. While its verbosity is notable, generating 13 million tokens during intelligence evaluation, this often translates to comprehensive and detailed outputs, which can be a distinct advantage in certain use cases.
From a cost perspective, Llama 4 Scout presents a compelling value proposition. With an input token price of $0.14 per million and an output token price of $0.54 per million, it maintains a moderately priced profile within the market. The open license further reduces total cost of ownership by eliminating proprietary vendor lock-in and fostering community-driven innovation. Its substantial 10 million token context window allows for processing extensive inputs, crucial for applications requiring deep contextual understanding, such as long-form content generation, complex data analysis, or sophisticated conversational AI.
The model's knowledge cutoff in July 2024 ensures it is equipped with relatively up-to-date information, making it suitable for current events analysis and applications requiring recent knowledge. The combination of its multimodal input, robust performance, and open-source nature positions Llama 4 Scout as a versatile and powerful tool for developers looking to build advanced, intelligent systems without incurring the prohibitive costs often associated with closed-source, top-tier models.
28 (#5 / 33 / 33)
137.3 tokens/s
$0.14 per 1M tokens
$0.54 per 1M tokens
13M tokens
0.25 s (TTFT)
| Spec | Details |
|---|---|
| Owner | Meta |
| License | Open |
| Context Window | 10M tokens |
| Knowledge Cutoff | July 2024 |
| Input Modalities | Text, Image |
| Output Modalities | Text |
| Intelligence Index | 28 (Rank #5/33) |
| Output Speed | 137.3 tokens/s (Rank #4/33) |
| Input Price | $0.14 / 1M tokens (Rank #13/33) |
| Output Price | $0.54 / 1M tokens (Rank #17/33) |
| Verbosity | 13M tokens (Rank #12/33) |
| Best Latency (TTFT) | 0.25s (Groq) |
Choosing the right API provider for Llama 4 Scout can significantly impact performance and cost. Our benchmarks highlight distinct advantages across various providers, allowing you to optimize for your specific priorities.
Below is a curated selection of providers based on common optimization goals, leveraging the detailed performance data for Llama 4 Scout.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Speed & Low Latency | Groq | Groq offers unparalleled output speed (411 t/s) and the lowest latency (0.25s TTFT), making it ideal for real-time, interactive applications. | Higher blended price ($0.17/M) compared to the absolute cheapest options. |
| Cost-Optimized (Blended) | CompactifAI | CompactifAI provides the most cost-effective blended price ($0.11/M), offering excellent value for budget-conscious deployments. | Lower output speed and higher latency compared to top-tier performance providers. |
| Cost-Optimized (Input) | GMI / Deepinfra | Both GMI and Deepinfra offer the lowest input token prices ($0.08/M), beneficial for applications with heavy input processing. | Output token prices are higher for GMI ($0.50/M) and Deepinfra ($0.30/M) compared to CompactifAI. |
| Cost-Optimized (Output) | CompactifAI | CompactifAI leads with the lowest output token price ($0.14/M), crucial for applications generating extensive responses. | Not the fastest or lowest latency provider. |
| Balanced Performance & Price | Google Vertex | Google Vertex offers a strong balance with good speed (181 t/s), reasonable latency (0.38s), and competitive pricing, backed by enterprise-grade reliability. | Not the absolute best in any single category, but a solid all-rounder. |
| Enterprise Scale & Support | Microsoft Azure | Azure provides robust infrastructure and enterprise support, with strong output speed (216 t/s), suitable for large-scale, mission-critical deployments. | Higher overall pricing compared to more specialized or budget-focused providers. |
Note: Provider performance and pricing can fluctuate. Always verify current rates and benchmark with your specific workloads.
Understanding the real-world cost implications of Llama 4 Scout requires looking beyond raw token prices. Here, we break down estimated costs for common AI workloads, considering the model's characteristics and typical usage patterns.
These scenarios provide a practical perspective on how Llama 4 Scout's intelligence, verbosity, and pricing translate into operational expenses for various applications.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Long-Form Content Generation | 5,000 input tokens (briefing) | 15,000 output tokens (article) | Generating detailed articles, reports, or creative content from a prompt. | ~$0.088 |
| Complex Document Summarization | 50,000 input tokens (document) | 1,000 output tokens (summary) | Condensing lengthy technical papers or legal documents into concise summaries. | ~$0.075 |
| Multimodal Product Description | 1,000 input tokens (text) + 1 image | 2,000 output tokens (description) | Generating product descriptions based on product features and an image. | ~$0.012 (excluding image processing cost, which varies by provider) |
| Advanced Chatbot Interaction | 2,000 input tokens (conversation history) | 500 output tokens (response) | Handling complex customer service queries requiring deep context and detailed responses. | ~$0.0055 per turn |
| Code Generation/Refactoring | 10,000 input tokens (existing code/request) | 3,000 output tokens (new/refactored code) | Assisting developers with writing or improving code snippets and functions. | ~$0.029 |
| Data Extraction & Structuring | 20,000 input tokens (unstructured text) | 2,000 output tokens (JSON output) | Extracting specific entities or structuring data from large blocks of text. | ~$0.039 |
Llama 4 Scout's competitive pricing and large context window make it highly efficient for tasks involving substantial input processing, while its verbosity means output token management is key for cost control in generation tasks.
Optimizing costs with Llama 4 Scout involves strategic choices in prompting, output management, and provider selection. Here are key strategies to maximize efficiency and minimize expenses.
Crafting concise yet effective prompts is crucial. While Llama 4 Scout has a large context window, unnecessary input tokens still add to costs. Focus on providing only the essential information.
Llama 4 Scout's verbosity can be a double-edged sword. While it provides detailed responses, generating more tokens than necessary directly increases output costs. Implement strategies to control output length.
max_tokens parameter in your API calls to prevent overly long responses.The choice of API provider can dramatically alter your operational costs and performance. Leverage the benchmark data to align provider capabilities with your application's priorities.
For workloads that don't require immediate responses, batching multiple requests can improve efficiency and potentially reduce costs, especially with providers that optimize for throughput.
Llama 4 Scout is an advanced, open-weight multimodal AI model developed by Meta. It excels in intelligence and speed, capable of processing both text and image inputs to generate detailed text outputs, making it highly versatile for a wide range of applications.
Its primary strengths include high intelligence (scoring 28 on the Intelligence Index), exceptional output speed (137.3 tokens/s), multimodal input capabilities, a large 10M token context window, and a competitive cost structure, especially given its open-weight license.
Llama 4 Scout is moderately priced, with input tokens at $0.14/M and output tokens at $0.54/M. Its open-weight nature also contributes to overall cost-effectiveness by allowing flexible deployment and avoiding proprietary licensing fees.
Yes, Llama 4 Scout is a multimodal model that supports both text and image inputs, allowing for more complex and context-rich interactions, such as generating descriptions from images or answering questions about visual content.
Llama 4 Scout features a substantial 10 million token context window. This allows it to process and understand very long documents, extensive conversation histories, or complex datasets, maintaining coherence and relevance over extended interactions.
For the fastest response and lowest latency, Groq is the top performer, offering 411 tokens/s output speed and a Time To First Token (TTFT) of just 0.25 seconds. This makes it ideal for real-time and interactive applications.
To manage costs related to verbosity, you should use prompt engineering to request concise outputs, set a max_tokens parameter in your API calls, and consider post-processing or truncating outputs if they are still too long. Clearly instructing the model to be brief can significantly help.