What is the LLaMA 3.1 context window size?

All LLaMA 3.1 models (8B, 70B, and 405B) support a 128,000-token context window — the same as GPT-4o. LLaMA 3.2 models (1B and 3B) support a smaller 128K context. Self-hosted deployments may configure a shorter context to save GPU memory.

Does self-hosting LLaMA change the context window?

Yes. When running LLaMA locally with tools like Ollama or llama.cpp, the effective context window depends on your available GPU memory and configuration. Many local setups default to 4K or 8K to reduce VRAM usage.

How do LLaMA inference APIs price context usage?

Providers like Together AI and Fireworks charge per token for LLaMA models. LLaMA 3.1 70B typically costs $0.88 per million input tokens and $0.88 per million output tokens — significantly cheaper than GPT-4o or Claude.

LLaMA Context Calculator

Calculate token usage for LLaMA 3.1 models with 128K context.

Model

System prompt

User prompt

Expected output tokensMax output for LLaMA 3.1 70B: 4,096 tokens

Total context usage

5000.4% of 128.0K

System tokens0

User tokens0

Output tokens500

Remaining127,500

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FAQ

What is the LLaMA 3.1 context window size?: All LLaMA 3.1 models (8B, 70B, and 405B) support a 128,000-token context window — the same as GPT-4o. LLaMA 3.2 models (1B and 3B) support a smaller 128K context. Self-hosted deployments may configure a shorter context to save GPU memory.
Does self-hosting LLaMA change the context window?: Yes. When running LLaMA locally with tools like Ollama or llama.cpp, the effective context window depends on your available GPU memory and configuration. Many local setups default to 4K or 8K to reduce VRAM usage.
How do LLaMA inference APIs price context usage?: Providers like Together AI and Fireworks charge per token for LLaMA models. LLaMA 3.1 70B typically costs $0.88 per million input tokens and $0.88 per million output tokens — significantly cheaper than GPT-4o or Claude.

Check whether your prompts fit within LLaMA 3.1 context windows (128K tokens for 8B, 70B, and 405B models). Useful for planning prompts when self-hosting or using LLaMA inference APIs like Together AI or Fireworks.