meta-llama/Llama-3.3-70B-Instruct
Nebius AI/🇪🇺 EU/chat10% off
A lightweight and ultra-fast variant of Llama 3.3 70B, for use when quick response times are needed most.
Which id to call
nebius/meta-llama/Llama-3.3-70B-InstructThis exact deployment on Nebius AI, with no routing and no failover. Send it as the model field.
Input /1M
$0.12
$0.13 list
Nebius AI
Output /1M
$0.36
$0.40 list
3.1x input
Context
128K
tokens
Added
Dec 2024
chat
Capabilities 2/8
Provider rates
What meta-llama/Llama-3.3-70B-Instruct costs
Provider prices per 1M tokens, updated August 24, 2026.moreless
These are the upstream provider rates. Pay as you go adds 5%, or 0% if you bring your own keys, and there is no per-request fee. Prompt caching and routing change what you pay against these rates, not the rates themselves.
This endpoint is discounted. List is $0.13 per 1M input and $0.40 per 1M output, and the rates below are what you pay. The discount applies to every request on this endpoint, with nothing to claim or enter.
Input /1M
$0.12
$0.13 list
Output /1M
$0.36
$0.40 list
Cache write /1M
-
Cache read /1M
-
What a workload costs
- 100K input + 10K output
- $0.0153
- 1M input + 100K output
- $0.15
- 10M input + 1M output
- $1.53
At the rates above. This endpoint does not offer prompt caching, so repeated context is billed as new input every time.
OpenAI compatible
Call it in three lines
Change the base url, use your Requesty key, set the model to nebius/meta-llama/Llama-3.3-70B-Instruct. Existing OpenAI SDK code needs no other edit.
123456789101112131415from openai import OpenAI client = OpenAI( api_key="YOUR_REQUESTY_API_KEY", base_url="https://router.requesty.ai/v1", ) response = client.chat.completions.create( model="nebius/meta-llama/Llama-3.3-70B-Instruct", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ], ) print(response.choices[0].message.content)
Reference
Specs and data terms
What the catalog reports for this deployment, and what the provider does with the traffic.
Public leaderboards
Benchmark scores
Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.
No benchmarks are published for this exact variant yet.
Region-specific deployments and highspeed tiers usually share scores with their base model. Try the base model page or the Nebius AI models overview.
Same provider
More from Nebius AI
Newest first, on the same provider and the same key.
Reference
meta-llama/Llama-3.3-70B-Instruct questions
How much does meta-llama/Llama-3.3-70B-Instruct cost?
meta-llama/Llama-3.3-70B-Instruct is priced at $0.12 per million input tokens and $0.36 per million output tokens when accessed via Requesty. Those figures include a 10% discount on this endpoint, off a list rate of $0.13 per million input tokens and $0.40 per million output tokens. Those are the upstream provider rates: pay as you go adds 5% on top, or 0% if you bring your own provider keys.
What is the context window of meta-llama/Llama-3.3-70B-Instruct?
meta-llama/Llama-3.3-70B-Instruct has a context window of 128K tokens. That's roughly 171 words of input you can fit in a single prompt.
What can meta-llama/Llama-3.3-70B-Instruct do?
meta-llama/Llama-3.3-70B-Instruct supports tool calling, structured outputs (JSON schema). You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use meta-llama/Llama-3.3-70B-Instruct with the OpenAI SDK?
Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "nebius/meta-llama/Llama-3.3-70B-Instruct". The Quickstart above shows Python, JavaScript and cURL snippets.
Can I run meta-llama/Llama-3.3-70B-Instruct through Requesty?
Yes. meta-llama/Llama-3.3-70B-Instruct runs through Requesty's OpenAI-compatible API, served from Nebius AI. You do not host the model yourself: point base_url at Requesty, set the model to "nebius/meta-llama/Llama-3.3-70B-Instruct", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.
Call meta-llama/Llama-3.3-70B-Instruct through one endpoint
One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.