Model Cost Profile

Meta: Llama 3 70B Instruct

Developer: meta-llama

Pricing updated Mar 11, 2026

Input rank: #208Output rank: #145

Live Pricing

Input: $0.5100

Output: $0.7400

Pricing via OpenRouter API ยท Last synced Mar 11, 2026

Meta: Llama 3 70B Instruct is designed for advanced natural language processing tasks, making it suitable for applications in customer support automation and content generation. With a context window of 8192 tokens, it allows for handling longer conversations and complex queries, which is beneficial for teams needing detailed interactions. The pricing structure, at $0.51 for input and $0.74 for output per 1M tokens, can impact budget considerations for teams planning extensive usage in their projects.

๐Ÿ“‹ Structured Output

Context Window

8,192

Tokens

Input Price / 1M

$0.5100

Prompt tokens

Output Price / 1M

$0.7400

Completion tokens

Intelligence (MMLU)

Benchmark Pending

Massive Multitask Language Understanding

Price History

Meta: Llama 3 70B Instruct Pricing Trend

Input / 1M tokens0.0%Output / 1M tokens0.0%
Mar 7 โ€” Mar 11
$0.5100$0.6250$0.7400Mar 7Mar 8Mar 9Mar 10Mar 11

Current Input / 1M

$0.5100

Current Output / 1M

$0.7400

Cheaper Alternatives to Compare

Quick links for cost-down decisions before production rollout.

FAQ

Common pricing and benchmark questions for Meta: Llama 3 70B Instruct.

How much does Meta: Llama 3 70B Instruct cost per 1M input tokens?

Meta: Llama 3 70B Instruct input pricing is $0.5100 per 1M tokens based on the latest synced provider data.

How much does Meta: Llama 3 70B Instruct cost per 1M output tokens?

Meta: Llama 3 70B Instruct output pricing is $0.7400 per 1M tokens based on the latest synced provider data.

What context window does Meta: Llama 3 70B Instruct support?

Meta: Llama 3 70B Instruct supports a context window of 8,192 tokens.

How can I compare Meta: Llama 3 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.