How much does the Meta Llama API cost?

Meta Llama API pricing via Together.ai: Llama 4 Scout costs $0.11/$0.34 per 1M tokens (input/output), Llama 4 Maverick costs $0.20/$0.60, Llama 3.1 70B costs $0.88/$0.88, and Llama 3.1 8B costs $0.10/$0.10. Llama 4 Scout and Maverick offer 10M context windows — the largest available.

Is Llama cheaper than GPT-5?

Yes, significantly. Llama 4 Scout ($0.11/$0.34 per 1M tokens) is 91% cheaper than GPT-5 ($1.25/$10.00) for input tokens and 97% cheaper for output tokens. Even the largest Llama model, Maverick ($0.20/$0.60), is 84% cheaper for input and 94% cheaper for output.

How much does Llama cost per month?

Monthly Llama costs depend on usage: 100 requests/day (~3K/mo) costs ~$1/month on Llama 4 Scout and ~$2/month on Maverick. 1,000 requests/day (~30K/mo) costs ~$10/month on Scout and ~$20/month on Maverick. At 10,000 requests/day, costs are ~$100/month on Scout and ~$200/month on Maverick.

What is Llama 4 Scout used for?

Llama 4 Scout ($0.11/$0.34 per 1M tokens) is ideal for chatbots, content generation, summarization, code assistance, and high-volume production workloads. Its 10M context window makes it perfect for RAG pipelines, document analysis, and long-context tasks.

Meta Llama API Cost Calculator

Estimate your Llama spend across Scout, Maverick, and Llama 3.1. Open-source AI at the lowest prices — from $0.10/1M tokens. 10M context windows on Llama 4.

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All Llama Models — Cost Comparison

See how your costs compare across all Meta Llama models with your current settings

Cheaper Alternatives from Other Providers

These models from other providers offer similar capabilities at lower prices:

Model	Provider	Input/1M	Output/1M	Your Cost/Req	Savings vs Selected

Meta Llama API Pricing Explained

Meta's Llama models are available as managed APIs through Together.ai. Llama 4 Scout ($0.11/$0.34 per 1M tokens) is the cheapest Llama 4 model with an enormous 10M context window. Llama 4 Maverick ($0.20/$0.60) offers higher quality with the same 10M context. Llama 3.1 70B ($0.88/$0.88) and Llama 3.1 8B ($0.10/$0.10) round out the lineup for production workloads.

When to Use Each Llama Model

Llama 4 Scout ($0.11/$0.34): Best value for high-volume workloads — chatbots, summarization, classification, content generation. 10M context window for RAG and document analysis. Cheapest Llama 4 model.
Llama 4 Maverick ($0.20/$0.60): Higher quality reasoning and code generation. Same 10M context window. Best for tasks requiring stronger accuracy.
Llama 3.1 70B ($0.88/$0.88): Proven production model. Balanced cost and quality. 128K context. Great for general-purpose workloads.
Llama 3.1 8B ($0.10/$0.10): Ultra-budget option for simple tasks. 128K context. Ideal for classification, extraction, and lightweight chat.

Llama vs Proprietary Models

Llama's biggest advantage is open-source pricing. Llama 4 Scout costs 91% less than GPT-5 for input tokens while delivering competitive quality. The 10M context window on Llama 4 models is the largest available from any provider. For self-hosting, you can run Llama models at near-zero marginal cost — but managed API via Together.ai is more practical for most teams.

How to Reduce Your Llama API Costs

Use Scout for simple tasks: Route classification, summarization, and simple chat to Scout ($0.11/$0.34) and reserve Maverick ($0.20/$0.60) for complex reasoning. Saves 45%+.
Leverage the 10M context window: Include all relevant context in a single request instead of chunking and making multiple calls.
Self-host for highest volume: If you exceed 1M requests/day, self-hosting Llama can reduce costs to near-zero marginal cost.
Set token limits: Control output length with max_tokens to avoid surprise costs on verbose responses.
Batch requests: Process multiple items in a single prompt to reduce per-request overhead.

Self-Hosting vs API

Llama is open-source, so you can self-host on your own infrastructure. API via Together.ai is best for teams that want zero ops overhead and pay-as-you-go pricing. Self-hosting is better for high-volume workloads (1M+ requests/day) or strict data privacy requirements. The break-even point is typically around 500K-1M requests/day depending on your infrastructure costs.

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