Available now
Meta
llama-3.3-70b
The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model...
Text128KTools
InputFree
OutputFree
Context128K
Endpointsopenai
Capabilities
ToolsStructured
Modalities
Input
text
Output
text
Quick stats
Context window128K
Max output24K
Modechat
TokenizerLlama3
Knowledge cutoff2023
Quantizationbf16
Hugging Facemeta-llama/Llama-3.3-70B-Instruct
Performance
Loading performance data...
Supported parameters
| Parameter | Always | Default |
|---|---|---|
| frequency_penalty | - | - |
| logit_bias | - | - |
| max_tokens | - | |
| min_p | - | - |
| presence_penalty | - | - |
| repetition_penalty | - | - |
| response_format | - | - |
| seed | - | - |
| stop | - | - |
| structured_outputs | - | - |
| temperature | - | |
| tool_choice | - | - |
| tools | - | - |
| top_k | - | - |
| top_p | - |
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Pricing
| Input price | $0.00 · 1M tokens |
| Output price | $0.00 · 1M tokens |
| Context window | 128K tokens |
| Compatible endpoints | openai |
| Vendor | Meta |
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Call llama-3.3-70b from your code
Point any OpenAI-compatible SDK at UnoRouter and request the model by name. Replace YOUR_API_KEY with a real key from your dashboard.
bash
curl https://api.unorouter.ai/v1/chat/completions \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "llama-3.3-70b",
"messages": [{"role": "user", "content": "Hello!"}]
}'§ 03
Frequently asked questions
How much does llama-3.3-70b cost per 1M tokens?
Input is priced at $0.00 per 1M tokens, output at $0.00 per 1M tokens. Billing is per token, no rounding to batch sizes.
How do I access llama-3.3-70b via API?
Send requests to the UnoRouter /v1/chat/completions endpoint with model=llama-3.3-70b. Any OpenAI-compatible client library works. Authentication uses a standard Bearer token.
What is the context window of llama-3.3-70b?
llama-3.3-70b supports a context window of 128K tokens, shared between your prompt and the model's response.
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