Llama 3.1 vs Llama 3.3 comparison
VERDICT
| Model | Context window | Speed | Cost/1M tokens | Best for | Free tier |
|---|---|---|---|---|---|
| Llama 3.1-405b | 32K tokens | Fast | Moderate | General-purpose, moderate context | No |
| Llama 3.3-70b | 64K tokens | Moderate | Higher | Long-context, complex reasoning | No |
| Llama 3.3-70b-versatile | 64K tokens | Moderate | Higher | Versatile tasks, instruction-following | No |
| Llama 3.1-8b | 8K tokens | Very fast | Low | Lightweight, low-latency apps | No |
Key differences
Llama 3.3 extends the context window up to 64K tokens, doubling Llama 3.1's max context of 32K tokens for large document understanding. It also improves instruction-following and reasoning capabilities, making it better suited for complex tasks. Performance-wise, Llama 3.1 models are generally faster and cheaper, ideal for lower-latency or cost-sensitive applications.
Side-by-side example
Here is a Python example using the OpenAI-compatible SDK to query Llama 3.1-405b for a summarization task:
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ["GROQ_API_KEY"], base_url="https://api.groq.com/openai/v1")
response = client.chat.completions.create(
model="llama-3.1-405b",
messages=[{"role": "user", "content": "Summarize the key points of the US Constitution."}]
)
print(response.choices[0].message.content)The US Constitution establishes the framework of the federal government, defines the separation of powers, protects individual rights, and outlines the amendment process.
Llama 3.3 equivalent
The same summarization task using Llama 3.3-70b model, which supports longer context and improved reasoning:
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ["GROQ_API_KEY"], base_url="https://api.groq.com/openai/v1")
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[{"role": "user", "content": "Summarize the key points of the US Constitution."}]
)
print(response.choices[0].message.content)The US Constitution establishes the federal government structure, ensures checks and balances among branches, protects civil liberties, and provides a process for amendments to adapt over time.
When to use each
Use Llama 3.3 when your application requires handling very long documents, complex instructions, or nuanced reasoning. Choose Llama 3.1 for faster, cost-effective solutions with moderate context needs.
| Scenario | Recommended Model | Reason |
|---|---|---|
| Long document summarization | Llama 3.3-70b | Supports 64K token context window |
| Chatbots with complex instructions | Llama 3.3-70b-versatile | Better instruction-following and reasoning |
| Low-latency applications | Llama 3.1-8b | Faster inference and lower cost |
| Prototyping and experimentation | Llama 3.1-405b | Good balance of speed and capability |
Pricing and access
Both Llama 3.1 and Llama 3.3 are accessible via third-party providers like Groq and Together AI using OpenAI-compatible APIs. Pricing varies by provider and model size, with larger Llama 3.3 models costing more per million tokens.
| Option | Free | Paid | API access |
|---|---|---|---|
| Groq API | No | Yes | Yes, OpenAI-compatible |
| Together AI | No | Yes | Yes, OpenAI-compatible |
| Ollama (local) | Yes | No | Local only, no API |
| Fireworks AI | No | Yes | Yes, OpenAI-compatible |
Key Takeaways
- Llama 3.3 doubles context window size over Llama 3.1, enabling better long-document tasks.
- Llama 3.1 models offer faster inference and lower cost, suitable for lightweight applications.
- Use OpenAI-compatible SDKs with third-party providers like Groq or Together AI to access both models.
- Llama 3.3 excels at complex reasoning and instruction-following compared to Llama 3.1.
- Choose model based on your application's context length, speed, and cost requirements.