
nvidia/nemotron-3-ultra-550b-a55b
Nebius AI/🇪🇺 EU/chat
Nemotron 3 Ultra is a 550B hybrid MoE model from NVIDIA, optimized for the most demanding multi-agent AI and complex reasoning tasks.MoreLess
Which id to call
nebius/nvidia/nemotron-3-ultra-550b-a55bThis exact deployment on Nebius AI, with no routing and no failover. Send it as the model field.
Input /1M
$1.00
Nebius AI
Output /1M
$3.00
3.0x input
Context
128K
tokens
Paid /1M
$1.02
measured, cache included
Capabilities 3/8
Provider rates
What nvidia/nemotron-3-ultra-550b-a55b costs
Provider prices per 1M tokens, updated September 16, 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.
Input /1M
$1.00
Output /1M
$3.00
Cache write /1M
-
Cache read /1M
-
What a workload costs
- 100K input + 10K output
- $0.13
- 1M input + 100K output
- $1.30
- 10M input + 1M output
- $13.00
At the rates above. This endpoint does not offer prompt caching, so repeated context is billed as new input every time.
model=
Which id to call
One base url, https://router.requesty.ai/v1, and one key for every id here.
nebius/nvidia/nemotron-3-ultra-550b-a55bThis exact deployment on Nebius AI, with no routing and no failover. Send it as the model field.
OpenAI compatible
Call it in three lines
Change the base url, use your Requesty key, set the model to any id on the left. 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/nvidia/nemotron-3-ultra-550b-a55b", messages=[ {"role": "user", "content": "Explain quantum computing in one paragraph."}, ], ) print(response.choices[0].message.content)
Live from production
Nebius AI on nvidia/nemotron-3-ultra-550b-a55b, measured
What this provider was measured doing on nvidia/nemotron-3-ultra-550b-a55b across Requesty traffic.moreless
Whole-window figures, because that is the grain published per provider. They cover Nebius AI serving this model in every region it serves it from, so a region-pinned deployment shares them with its siblings. A figure is absent where no qualifying sample exists.
First token
2.73s
median
p95 wait
4.20s
slowest 5%
Output speed
204/s
median tokens
Paid /1M
$1.02
blended, cache included
Cache hit
19.7%
of input tokens
Compare these against the other 1 provider serving nemotron-3-ultra-550b-a55b.
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
nvidia/nemotron-3-ultra-550b-a55b questions
How much does nvidia/nemotron-3-ultra-550b-a55b cost?
nvidia/nemotron-3-ultra-550b-a55b is priced at $1.00 per million input tokens and $3.00 per million output tokens when accessed via Requesty. 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 nvidia/nemotron-3-ultra-550b-a55b?
nvidia/nemotron-3-ultra-550b-a55b has a context window of 128K tokens. That's roughly 171 words of input you can fit in a single prompt.
What can nvidia/nemotron-3-ultra-550b-a55b do?
nvidia/nemotron-3-ultra-550b-a55b supports tool calling, extended reasoning, structured outputs (JSON schema). You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use nvidia/nemotron-3-ultra-550b-a55b 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/nvidia/nemotron-3-ultra-550b-a55b". The Quickstart above shows Python, JavaScript and cURL snippets.
Can I run nvidia/nemotron-3-ultra-550b-a55b through Requesty?
Yes. nvidia/nemotron-3-ultra-550b-a55b 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/nvidia/nemotron-3-ultra-550b-a55b", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.
Call nvidia/nemotron-3-ultra-550b-a55b through one endpoint
One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.
