Llama model sizes comparison
Llama family includes models from 7 billion to 70 billion parameters, with larger models like llama-3.3-70b offering better accuracy and longer context windows but at higher compute cost. Smaller models such as llama-3.1-7b provide faster inference and lower cost, suitable for lightweight applications.VERDICT
llama-3.3-70b for highest accuracy and long-context tasks; use llama-3.1-7b for cost-effective, faster inference in production.| Model | Parameters | Context window | Speed | Cost/1M tokens | Best for | Free tier |
|---|---|---|---|---|---|---|
| llama-3.1-7b | 7B | 8K tokens | Fast | Low | Lightweight apps, prototyping | No |
| llama-3.1-13b | 13B | 8K tokens | Moderate | Moderate | Balanced accuracy and speed | No |
| llama-3.3-33b | 33B | 16K tokens | Slower | High | Complex tasks, longer context | No |
| llama-3.3-70b | 70B | 32K tokens | Slowest | Highest | High accuracy, long documents | No |
Key differences
The llama-3.1-7b and llama-3.1-13b models have 8K token context windows and are optimized for speed and cost efficiency. The llama-3.3-33b and llama-3.3-70b models support extended context windows of 16K and 32K tokens respectively, enabling better handling of long documents and complex reasoning. Larger models provide higher accuracy but require more compute resources and incur higher costs.
Side-by-side example
Generating a summary of a long document using llama-3.1-7b with 8K context window:
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["GROQ_API_KEY"], base_url="https://api.groq.com/openai/v1")
messages = [{"role": "user", "content": "Summarize the following document: <long document text>"}]
response = client.chat.completions.create(
model="llama-3.1-7b",
messages=messages
)
print(response.choices[0].message.content)Summary of the document...
70B model equivalent
Using llama-3.3-70b for the same task with extended 32K token context window for longer documents:
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["GROQ_API_KEY"], base_url="https://api.groq.com/openai/v1")
messages = [{"role": "user", "content": "Summarize the following long document with detailed insights: <very long document text>"}]
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=messages
)
print(response.choices[0].message.content)Detailed summary with insights...
When to use each
Use llama-3.1-7b for fast, cost-effective inference on smaller tasks or prototypes. Choose llama-3.1-13b for a balance of speed and accuracy. Opt for llama-3.3-33b when you need longer context and better reasoning. Use llama-3.3-70b for highest accuracy, complex tasks, and very long context windows.
| Model | Use case | Context window | Cost sensitivity |
|---|---|---|---|
| llama-3.1-7b | Prototyping, lightweight apps | 8K tokens | Low cost |
| llama-3.1-13b | Balanced tasks | 8K tokens | Moderate cost |
| llama-3.3-33b | Long documents, complex reasoning | 16K tokens | Higher cost |
| llama-3.3-70b | High accuracy, long context | 32K tokens | Highest cost |
Pricing and access
Llama models are available via providers like Groq and Together AI using OpenAI-compatible APIs. Pricing scales with model size and token usage. No free tier is available for these large models.
| Option | Free | Paid | API access |
|---|---|---|---|
| Groq API | No | Yes | Yes, via OpenAI SDK with base_url override |
| Together AI | No | Yes | Yes, via OpenAI SDK with base_url override |
| Local (Ollama/vLLM) | Yes (local only) | No | No |
| Meta official API | No | No | No public API available |
Key Takeaways
- Larger Llama models offer longer context windows and higher accuracy at increased cost and slower speed.
- Use smaller Llama models for fast, cost-effective inference on simpler tasks.
- Llama models require third-party providers for API access; no official Meta-hosted API exists.
- Choose model size based on your application's context length needs and budget constraints.