Model Cost Profile

Meta: Llama 3.3 70B Instruct (free)

Developer: meta-llama

Pricing updated Mar 11, 2026

Input rank: #11Output rank: #11

Live Pricing

Input: $0.0000

Output: $0.0000

Pricing via OpenRouter API ยท Last synced Mar 11, 2026

Meta: Llama 3.3 70B Instruct is a powerful AI model designed for tasks such as natural language understanding, text generation, and conversational agents, making it suitable for a variety of applications in customer service and content creation. With an extensive context window of 128,000 tokens, teams can utilize this model for complex tasks that require understanding long documents or maintaining context over extended interactions. As a free API model, it offers significant cost savings for teams, eliminating input and output charges, which can enhance budget flexibility for projects requiring high-volume data processing.

๐Ÿ”ง Tool Calling

Context Window

128,000

Tokens

Input Price / 1M

$0.0000

Prompt tokens

Output Price / 1M

$0.0000

Completion tokens

Intelligence (MMLU)

Benchmark Pending

Massive Multitask Language Understanding

Price History

Meta: Llama 3.3 70B Instruct (free) Pricing Trend

Input / 1M tokensOutput / 1M tokens
Mar 7 โ€” Mar 11
$0.000000$0.000000$0.000000Mar 7Mar 8Mar 9Mar 10Mar 11

Current Input / 1M

$0.000000

Current Output / 1M

$0.000000

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FAQ

Common pricing and benchmark questions for Meta: Llama 3.3 70B Instruct (free).

How much does Meta: Llama 3.3 70B Instruct (free) cost per 1M input tokens?

Meta: Llama 3.3 70B Instruct (free) input pricing is $0.0000 per 1M tokens based on the latest synced provider data.

How much does Meta: Llama 3.3 70B Instruct (free) cost per 1M output tokens?

Meta: Llama 3.3 70B Instruct (free) output pricing is $0.0000 per 1M tokens based on the latest synced provider data.

What context window does Meta: Llama 3.3 70B Instruct (free) support?

Meta: Llama 3.3 70B Instruct (free) supports a context window of 128,000 tokens.

How can I compare Meta: Llama 3.3 70B Instruct (free) 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.