Llama 4 Scout

Llama 4 Scout by Meta excels in the Coding category, where it ranks #161 with a composite score of 54/100. Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input... It is particularly strong in areas highlighted by its top benchmark performance and adoption metrics, making it suitable for both individual developers and enterprise teams looking for a reliable coding solution.

Llama 4 Scout is priced at $0.08 per million input tokens and $0.30 per million output tokens (USD). Contact the provider for volume discounts and enterprise pricing. Pricing is competitive within the coding category and reflects the model's quality-to-cost ratio.

In the Coding category, Llama 4 Scout holds rank #161 out of 340 models tracked. Its quality rank is #161 and adoption rank is #161. You can use our comparison tool at /compare to see detailed side-by-side metrics with specific alternatives. Key differentiators include its composite scoring across benchmarks, community sentiment, and real-world adoption rates.

Llama 4 Scout has been evaluated across 6 different signals. Its strongest areas include Capabilities (67/100), Benchmarks (52/100), Pricing (100/100). These scores are derived from industry-standard benchmarks, community ratings, and real-world performance metrics. The composite score of 54/100 reflects a weighted combination of all tracked signals.

Llama 4 Scout is a paid model, though some providers may offer trial credits or limited free tiers for evaluation. Check Meta's website for current free tier availability and promotional offers.

Llama 4 Scout supports a 328K token context window (327,680 tokens total). That translates to roughly 245,760 words in a single prompt. This is large enough to process entire codebases, research papers, or long conversation histories in one shot.

Llama 4 Scout can generate up to 16K output tokens (16,384 tokens) per response. That is roughly 12,288 words. This is enough for generating complete code files, detailed reports, or long-form content in a single response.

Llama 4 Scout supports image understanding (vision), function/tool calling, structured JSON output, streaming responses. Function calling lets you integrate it with external APIs and tools programmatically. Vision support means it can analyze images, screenshots, and diagrams alongside text. These capabilities determine which workflows and integrations the model can handle natively.

Yes, Llama 4 Scout is an open-source model. You can download the weights, run it locally, fine-tune it for your use case, or deploy it on your own infrastructure. Many cloud providers also offer hosted versions if you prefer not to manage the infrastructure yourself. Self-hosting gives you full control over data privacy and eliminates per-token API costs.

Llama 4 Scout was developed by Meta. It was released on April 5, 2025. You can access it through Meta's API or download the model weights directly. Check our provider page for all models from Meta and how they compare against each other.

Pick Llama 4 Scout when you need a budget-friendly option for high-volume, simpler tasks where you prioritize cost over peak performance. If your task is straightforward text completion or classification, a cheaper model might give you 90% of the quality at a fraction of the price. Run a quick benchmark on your actual use case before committing.

You can access Llama 4 Scout through Meta's API using standard HTTP requests or their official SDK. Most providers support OpenAI-compatible endpoints, so switching between models often requires changing just the model name in your API call. Streaming is supported for real-time token-by-token output. For production use, implement proper error handling, rate limiting, and cost monitoring.