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Nebius Token Factory, formerly Nebius AI Studio, serves open-weight models such as DeepSeek, Qwen and Llama behind an OpenAI-compatible API. What sets it apart is where the model runs: dedicated endpoints let you pick the deployment region, which matters when call transcripts must stay in a given jurisdiction.

Set up Nebius

1

Create a Token Factory account

2

Get an API key

Open API keys in Token Factory, click Create API key, name it and click Create. Copy the key right away: it’s shown only once.
3

Choose a model and copy its ID

Browse the text models and copy the full model ID, for example nvidia/Nemotron-3_5-Lightning. For voice, prefer an instruct model over a reasoning model such as DeepSeek R1: reasoning models spend time thinking before the first spoken word.If the model has a fast flavour, prefer it for voice. It gives the same outputs with lower latency, at a higher price per token. Use it by adding -fast to the model ID.
4

Optional: create a dedicated endpoint

If you need a fixed region or reserved throughput, create a dedicated endpoint for a supported model and choose its region. Use the model identifier Token Factory shows for that endpoint as the model name in Bolna.

Register it in Bolna

1

Choose Nebius in the Add custom model dialog

Open Settings → Integrations → LLM → Custom LLM → Add model, or choose + Add custom LLM at the bottom of the LLM Provider dropdown on an agent’s Intelligence tab. Select Nebius.
Bolna Add custom model dialog with Nebius selected and the Token Factory endpoint shown as placeholder text

Nebius selected: the Token Factory endpoint is shown as a placeholder

2

Enter the endpoint and model

  • Model name: the Token Factory model ID, for example nvidia/Nemotron-3_5-Lightning
  • Endpoint URL: https://api.tokenfactory.nebius.com/v1
  • API key: your Token Factory key
  • Family: for example nemotron, qwen or deepseek
Nebius’s own examples write the base URL with a trailing slash (.../v1/). Enter it without the slash in Bolna, as shown above.
3

Pick it on your agent

Choose custom as the provider on the Intelligence tab, select the Nebius model, save, and run a test call.

Things to know

  • Bolna checks the key and the model ID when you save. Token Factory publishes its model list, so a wrong key or a model ID it doesn’t serve is refused with an error.
  • Models are retired. Nebius removes serverless models on a published schedule, and every call on a retired model fails. For example, Llama 3.3 70B Instruct was removed on 31 August 2026. Watch the deprecation notices and move your agents to the replacement model in time.
  • Tool calling. Check that a model supports tools before you use it on an agent that calls them. The model list shows each model’s supported_features:
  • Latency from India. Public endpoints run in Nebius’s global region, with no fixed location. For callers in India, the extra network distance adds latency to every reply, so measure the first reply on a web call before going live.
  • Rate limits. Your account’s limits are on the rate limits page. A request over the limit gets a 429, which leaves the agent silent for that turn. Ask Nebius to raise your limits before a large campaign.

Choosing a region for compliance

Public endpoints may run in whichever region has capacity, so they don’t guarantee where a request is processed. If your contracts or regulators require processing in a specific region, create a dedicated endpoint in that region and register it in Bolna as its own custom model. Keep separate model names per region, for example by registering each endpoint under the identifier Token Factory gives it, so agents for different markets point at the right one.

Troubleshooting

You’re likely using a reasoning model such as DeepSeek R1. Switch to an instruct model, or turn thinking off with Extra request params if the model supports it.
Public endpoints have dynamic rate limits. Move high-volume agents to a dedicated endpoint, which has no standard rate limits.
Use the full ID with the organisation prefix, exactly as the Token Factory catalog shows it. If it worked before, the model may have been retired; check the deprecation notices.