DeepSeek V4.1 Flash

DeepSeek V4.1 Flash is DeepSeek’s model for reasoning, coding, and tool-driven work. Mixlayer serves it in FP8 with text input and output.

Model details

• Identifier: deepseek/deepseek-v4.1-flash
• Context window: 256K tokens (262,144)
• Capabilities: Text · Reasoning · Tools
• Precision: FP8
• Architecture: Mixture of experts with 552B backbone parameters; 8B active during input processing and 16B during output generation

Best for

• Coding agents and multi-step tool use
• Understanding large repositories and document collections
• Reasoning tasks with substantial input context

Platform defaults

When sampling parameters are omitted, Mixlayer applies:

{
  "temperature": 1.0,
  "top_p": 0.95
}

Reasoning behavior

DeepSeek V4.1 Flash defaults to thinking with native High effort. The model uses a sliding reasoning-effort scale from 1 to 100. Mixlayer maps standardized effort values to this scale:

Set the value through reasoning_effort in Chat Completions or reasoning.effort in Responses. You can use a standardized effort name or pass an integer from 1 to 100 directly. Higher values increase reasoning effort; these values are not fixed reasoning-token budgets.

For Chat Completions:

{ "reasoning_effort": 60 }

For Responses:

{ "reasoning": { "effort": 60 } }

See Reasoning for more examples.

Budget enough output tokens for both reasoning and the final answer, especially for long agentic tasks.

Working with long context

Provide relevant material, identify authoritative sources, and state the expected outcome. Break long tasks into verifiable milestones so tool results and intermediate changes can be checked before continuing.

See the official model card and launch announcement for upstream architecture and capabilities.