Context Window
131,072
Tokens
Model Cost Profile
Meta: Llama 3.1 70B Instruct
Developer: meta-llama
Pricing updated Mar 11, 2026
Input rank: #187Output rank: #103
Meta: Llama 3.1 70B Instruct is designed for complex instruction-following tasks, making it suitable for applications in customer support automation and content generation. With a context window of 131,072 tokens, this model can handle extensive dialogues and large documents, providing teams with the ability to maintain context over longer interactions. The pricing structure at $0.40 per million tokens for both input and output allows organizations to budget effectively while scaling their usage based on project demands.
🔧 Tool Calling📋 Structured Output
Input Price / 1M
$0.4000
Prompt tokens
Output Price / 1M
$0.4000
Completion tokens
Intelligence (MMLU)
Benchmark Pending
Massive Multitask Language Understanding
Side-by-Side Pricing Table
| Usage Type | Price / 1M Tokens |
|---|---|
| Input (Prompt) | $0.4000 |
| Output (Completion) | $0.4000 |
Price History
Meta: Llama 3.1 70B Instruct Pricing Trend
Input / 1M tokens0.0%Output / 1M tokens0.0%
Mar 7 — Mar 11
Current Input / 1M
$0.4000
Current Output / 1M
$0.4000
Cost Calculator
Estimate monthly spend for Meta: Llama 3.1 70B Instruct based on your workload.
01.0B
0500M
Estimated Monthly Cost
$15
25M input + 12M output tokens
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FAQ
Common pricing and benchmark questions for Meta: Llama 3.1 70B Instruct.
How much does Meta: Llama 3.1 70B Instruct cost per 1M input tokens?
Meta: Llama 3.1 70B Instruct input pricing is $0.4000 per 1M tokens based on the latest synced provider data.
How much does Meta: Llama 3.1 70B Instruct cost per 1M output tokens?
Meta: Llama 3.1 70B Instruct output pricing is $0.4000 per 1M tokens based on the latest synced provider data.
What context window does Meta: Llama 3.1 70B Instruct support?
Meta: Llama 3.1 70B Instruct supports a context window of 131,072 tokens.
How can I compare Meta: Llama 3.1 70B Instruct with cheaper alternatives?
Use the comparison links on this page to open direct model-vs-model pricing and benchmark pages, then evaluate monthly spend projections for your workload.