Nscale

Use Nscale's Serverless Inference API for OpenAI-compatible chat, completion, embedding, and image requests.

Setup

Set your Nscale service token as an environment variable:

export NSCALE_SERVICE_TOKEN=your_service_token_here

Alternatively, you can add it to your .env file:

NSCALE_SERVICE_TOKEN=your_service_token_here

You can also supply config.apiKey or select a credential variable with config.apiKeyEnvar. Nscale does not fall back to OPENAI_API_KEY by default; set apiKeyEnvar: OPENAI_API_KEY to use that variable explicitly. The same credential rules apply when you set a custom apiBaseUrl.

Obtaining Credentials

You can obtain service tokens by:

1. Signing up at Nscale
2. Navigating to your account settings
3. Going to "Service Tokens" section

Configuration

To use Nscale models in your promptfoo configuration, use the nscale: prefix followed by the model name:

providers:
  - nscale:openai/gpt-oss-120b
  - nscale:meta-llama/Llama-3.3-70B-Instruct
  - nscale:Qwen/Qwen3-235B-A22B-Instruct-2507

Model IDs are the upstream Hugging Face repository IDs and are case-sensitive.

Model Types

Nscale supports different types of models through specific endpoint formats:

Chat Completion Models (Default)

For chat completion models, you can use either format:

providers:
  - nscale:chat:<model-id>
  - nscale:<model-id> # Defaults to chat

Completion Models

For text completion models:

providers:
  - nscale:completion:<model-id>

Embedding Models

For embedding models:

providers:
  - nscale:embedding:Qwen3-Embedding-8B
  - nscale:embeddings:Qwen3-Embedding-8B # Alternative format

Text-to-Image Models

For image generation models:

providers:
  - nscale:image:black-forest-labs/FLUX.1-schnell

Popular Models

The authoritative list for your account is GET https://inference.api.nscale.com/v1/models, which also returns pricing and context length:

curl -fsS https://inference.api.nscale.com/v1/models \
  -H "Authorization: Bearer $NSCALE_SERVICE_TOKEN"

Text Generation Models

Use a returned id after the nscale: or nscale:chat: prefix.

Model	Provider Format	Use Case
GPT OSS 120B	nscale:openai/gpt-oss-120b	General-purpose reasoning and tasks
GPT OSS 20B	nscale:openai/gpt-oss-20b	Lightweight general-purpose model
Kimi K2.5	nscale:moonshotai/Kimi-K2.5	Large-scale agentic reasoning
Qwen 3 235B A22B	nscale:Qwen/Qwen3-235B-A22B	Large-scale language understanding
Qwen 3 235B A22B Instruct 2507	nscale:Qwen/Qwen3-235B-A22B-Instruct-2507	Qwen 3 235B 2507 variant
Qwen 3 4B Instruct 2507	nscale:Qwen/Qwen3-4B-Instruct-2507	Lightweight instruction following
Qwen 3 4B Thinking 2507	nscale:Qwen/Qwen3-4B-Thinking-2507	Reasoning and thinking tasks
Qwen 3 8B	nscale:Qwen/Qwen3-8B	Mid-size general-purpose model
Qwen 3 14B	nscale:Qwen/Qwen3-14B	Enhanced reasoning capabilities
Qwen 3 32B	nscale:Qwen/Qwen3-32B	Large-scale reasoning and analysis
Qwen 2.5 Coder 3B Instruct	nscale:Qwen/Qwen2.5-Coder-3B-Instruct	Lightweight code generation
Qwen 2.5 Coder 7B Instruct	nscale:Qwen/Qwen2.5-Coder-7B-Instruct	Code generation and programming
Qwen 2.5 Coder 32B Instruct	nscale:Qwen/Qwen2.5-Coder-32B-Instruct	Advanced code generation
Qwen QwQ 32B	nscale:Qwen/QwQ-32B	Specialized reasoning model
Llama 3.3 70B Instruct	nscale:meta-llama/Llama-3.3-70B-Instruct	High-quality instruction following
Llama 3.1 8B Instruct	nscale:meta-llama/Llama-3.1-8B-Instruct	Efficient instruction following
Llama 3.2 11B Vision Instruct	nscale:meta-llama/Llama-3.2-11B-Vision-Instruct	Vision-language tasks
Llama 4 Scout 17B	nscale:meta-llama/Llama-4-Scout-17B-16E-Instruct	Image-Text-to-Text capabilities
DeepSeek R1 Distill Llama 70B	nscale:deepseek-ai/DeepSeek-R1-Distill-Llama-70B	Efficient reasoning model
DeepSeek R1 Distill Llama 8B	nscale:deepseek-ai/DeepSeek-R1-Distill-Llama-8B	Lightweight reasoning model
DeepSeek R1 Distill Qwen 1.5B	nscale:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B	Ultra-lightweight reasoning
DeepSeek R1 Distill Qwen 7B	nscale:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B	Compact reasoning model
DeepSeek R1 Distill Qwen 14B	nscale:deepseek-ai/DeepSeek-R1-Distill-Qwen-14B	Mid-size reasoning model
DeepSeek R1 Distill Qwen 32B	nscale:deepseek-ai/DeepSeek-R1-Distill-Qwen-32B	Large reasoning model
Devstral Small 2505	nscale:mistralai/Devstral-Small-2505	Code generation and development
Mixtral 8x22B Instruct	nscale:mistralai/Mixtral-8x22B-Instruct-v0.1	Large mixture-of-experts model

Embedding Models

Nscale's embedding API reference demonstrates Qwen3-Embedding-8B. Confirm it in your organization's /v1/models response before running an eval.

Text-to-Image Models

Nscale's image API reference demonstrates nscale:image:black-forest-labs/FLUX.1-schnell. Confirm it in your organization's catalog before running an eval.

Model	Provider Format	Use Case
Flux.1 Schnell	nscale:image:black-forest-labs/FLUX.1-schnell	Fast image generation
Stable Diffusion XL	nscale:image:stabilityai/stable-diffusion-xl-base-1.0	High-quality image generation
SDXL Lightning	nscale:image:ByteDance/SDXL-Lightning	Ultra-fast image generation

Configuration Options

Nscale supports standard OpenAI-compatible parameters:

providers:
  - id: nscale:meta-llama/Llama-4-Scout-17B-16E-Instruct
    config:
      temperature: 0.7
      max_tokens: 1024
      top_p: 0.9
      frequency_penalty: 0.1
      presence_penalty: 0.2
      stop: ['END', 'STOP']
      seed: 42

Supported Parameters

• temperature: Controls randomness (0.0 to 2.0). Defaults to 0 unless set.
• max_tokens: Maximum number of tokens to generate. Defaults to 1024 unless set.
• top_p: Nucleus sampling parameter
• frequency_penalty: Reduces repetition based on frequency
• presence_penalty: Reduces repetition based on presence
• stop: Stop sequences to halt generation
• seed: Deterministic sampling seed

Any other parameter is forwarded to the Nscale API unchanged.

note

Streaming is not supported. Promptfoo reads each response as a single JSON body, so setting stream: true produces a response it cannot parse.

Example Configuration

Here's a complete example configuration:

promptfooconfig.yaml

# yaml-language-server: $schema=https://promptfoo.dev/config-schema.json
providers:
  - id: nscale:openai/gpt-oss-120b
    label: nscale-gpt-oss
    config:
      temperature: 0.7
      max_tokens: 512
  - id: nscale:meta-llama/Llama-3.3-70B-Instruct
    label: nscale-llama
    config:
      temperature: 0.5
      max_tokens: 1024

prompts:
  - 'Explain {{concept}} in simple terms'
  - 'What are the key benefits of {{concept}}?'

tests:
  - vars:
      concept: quantum computing
    assert:
      - type: contains
        value: 'quantum'

Pricing

Nscale prices text generation and embeddings per token, and images per megapixel. Check Nscale's model endpoint API for current rates available to your organization.

Promptfoo has no Nscale-specific token price table. For chat, completion, or embedding estimates, set cost or inputCost/outputCost in USD per token. Image estimates use fixed model rates multiplied by the number of images; they do not adjust for resolution or use those token-cost settings.

Key Features

Nscale hosts the models and exposes an OpenAI-compatible API. See Nscale's documentation for throughput, rate limits, and available regions.

Error Handling

The Nscale provider includes built-in error handling for common issues:

• Network timeouts and retries
• Rate limiting
• Invalid API key errors
• Model availability issues

Support

For support with the Nscale provider:

• Nscale Documentation
• Nscale Community Discord
• promptfoo GitHub Issues