Meta AI API: models, pricing and limits
A practical guide to meta, using data from the AI & LLM Index. The goal is a shortlist and a decision, not a tour of every option.
The Meta AI lineup
Meta AI has 28 models in the index. Meta's AI research group and publisher of Llama. The lineup spans 2 categories, from multimodal to small open-weight.
The strongest model by average score is llama-4-maverick-instruct. The cheapest by input price is Llama 3.1 1.5B.
Pricing
Across the lineup, the average input price is $3.00 per million tokens and the average output price is $13.44. That average hides a wide range: individual models run from $0.53 to $5.36 on input.
Use the average as a starting point, then check the specific model you plan to deploy. Output price and cached input pricing can change the ranking for long or repetitive workloads.
Strengths and limits
28 of the 28 models are open weights. That gives you a self-hosting path for part of the lineup, while the proprietary models remain API-only.
The best benchmark result in the lineup is 81.7 on the index average. Compare that with the best model from other providers before treating Meta AI as the default.
How to choose
- Start with the task, not the brand.
- Pick the cheapest model that clears your evaluation set.
- Keep a second provider as a fallback for outages and price changes.
- Check licence and data-handling terms for every model you shortlist.
Meta AI models at a glance
| Model | Input / 1M | Context | Avg score |
|---|---|---|---|
| Llama 4 Maverick Instruct | $5.33 | 200,000 | 81.7 |
| Llama 4 Maverick | $5.36 | 200,000 | 81.6 |
| Llama 4 Maverick Chat | $5.09 | 128,000 | 79.7 |
| Llama 4 Scout | $4.90 | 200,000 | 79.3 |
| Llama 4 Maverick Base | $4.68 | 128,000 | 79.1 |
| Llama 4 Scout Instruct | $4.59 | 32,768 | 79.1 |
| Llama 3.3 | $4.56 | 128,000 | 78.2 |
| Llama 4 Scout Chat | $4.66 | 32,768 | 78.0 |
| Llama 3.3 Chat | $4.18 | 32,768 | 76.9 |
| Llama 3.3 Instruct | $4.33 | 128,000 | 76.6 |
Related
FAQ
Which Meta AI model should I start with?
Start with llama-4-maverick-instruct for quality-sensitive work and Llama 3.1 1.5B for high-volume tasks.
What is the most important factor in meta?
Fit to the task beats raw benchmark rank. Define the failure modes, pick the benchmarks that resemble them, then test a small shortlist on your own examples.
How many models should I evaluate?
Three is usually enough: a quality candidate, a value candidate and a fallback. More candidates add work without changing the decision.
How do I control cost?
Compare blended 3:1 price, watch output tokens, use caching where available and route simple requests to a cheaper model.