Avatar
openscale/deepseek-v41-flashProtected
Circuit
Versions
API
RUNNING
The circuit is running and is healthy.
85Total Runs
5.1sAvg Response Time
87Avg Tokens per Second
0.02Avg Spend per Run
81.6%Input Cache Hit Rate
2%Error Rate
Control Panel
Login to ModelWorksLogin to ModelWorks to use this circuit. To get started, get an API key.
Explore endpointsModelWorks allows apps to access LLMs and MCP servers worldwide easily in any datacenter, securely and privately.
SecretsOverride default secrets and set up your own for better billing and security.
Circuit Card

OpenScale (DeepSeek V4.1 Flash)

Overview

DeepSeek V4.1 Flash is a reasoning model from DeepSeek optimized for fast inference. It supports text and image input with function calling and structured output. The model handles a 1M token context window with up to 384K output tokens, served via OpenScale from Singapore at aggressive prices built for high-volume agent traffic. This circuit provides an OpenAI-compatible chat completion endpoint backed by the deepseek/deepseek-v4.1-flash model.


Pricing

Per 1M tokens
Input$0.06
Cached input$0.003
Output$0.24

Inputs

1. Messages (messages)

  • Description: A list of messages comprising the conversation so far. For image input, content must be an array of content parts (e.g., [{type: text, text: ...}, {type: image_url, image_url: {url: ...}}]).
  • Type: array
  • Required: Yes

2. Attachment (attachment)

  • Description: Optional image attachment (blob ID or URL). When provided, the image is injected into the first message's content array as an image_url content part.
  • Type: blob
  • Default Value: null
  • Required: No

3. Max Tokens (max_tokens)

  • Description: The maximum number of tokens to generate in the completion. DeepSeek V4.1 Flash supports up to 384000.
  • Type: number
  • Default Value: null (model maximum)
  • Required: No

4. Temperature (temperature)

  • Description: Sampling temperature, controls randomness. Range [0.0, 2.0]. Lower values are more deterministic.
  • Type: number
  • Default Value: 1
  • Required: No

5. Top-p (top_p)

  • Description: Nucleus sampling. Range [0.01, 1.0]. Recommended to use either this or temperature, not both.
  • Type: number
  • Default Value: 0.95
  • Required: No

6. Tools (tools)

  • Description: A list of tools the model may call. Supports function-type tools.
  • Type: array
  • Default Value: []
  • Required: No

7. Tool Choice (tool_choice)

  • Description: Controls how the model selects a tool. auto or none.
  • Type: string
  • Default Value: auto
  • Required: No

Outputs

1. Chat Completion Output (chat_completion_output)

  • Description: The generated chat completion text, the model's final answer.
  • Value: Accumulated from choices[0].delta.content stream chunks.

2. Reasoning Output (reasoning_output)

  • Description: The chain-of-thought reasoning content, the model's thinking process, separate from the final answer.
  • Value: Accumulated from choices[0].delta.reasoning_content stream chunks.

3. Chat Completion Tool Calls (chat_completion_tool_calls)

  • Description: Tool calls made by the model (if any).
  • Value: Accumulated from choices[0].delta.tool_calls stream chunks.

Try It

You can try this circuit directly in the Playground, no setup required.


Notes

  1. Multimodal Input: DeepSeek V4.1 Flash supports both text and image input. Use the attachment input to inject an image into the first message's content array as an image_url content part.
  2. Reasoning Always On: Reasoning is always on for DeepSeek V4.1 Flash and cannot be disabled.
  3. Long Context: Supports a 1M token context window with up to 384K output tokens, ideal for very long generations.
  4. Function Calling: Supports function calling and structured output via the tools and tool_choice inputs.
  5. Also available directly: The same model is available on the OpenScale API at openscale.so.
About
DeepSeek V4.1 Flash on OpenScale: a fast 1M-context reasoning model with aggressive cache pricing, built for high-volume agent traffic. Text and image input, function calling and structured output.
About the Author
The cheapest and fastest way to run open models. Intelligence too cheap to meter.
AFG Ventura Teknologi Pte. Ltd.
Additional Information
Pricing Structure

The cost model for running this circuit. Charges may be fixed per execution or based on token usage.

Token-based Pricing
UsageLocal
Input tokens0.0₅6 credits
Cache Input Tokens0.0₆3 credits
Output tokens0.0₄24 credits
Token pricing display: Per TokenPer Token