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[ARTICLE · art-151892] src=runinfra.ai ↗ pub= topic=large-language-models verified=true sentiment=· neutral

The fastest DeepSeek v4.1 flash 1017 – Day 0

RunInfra has listed DeepSeek V4.1 Flash 1017 (deepseek-ai/DeepSeek-V4.1-Flash-1017) in its Model APIs, serving the model at $0.15 per 1M input tokens, $0.01 per 1M cached input tokens and $0.60 per 1M output tokens. The listing specifies a 1,048,576-token context window, a 3.5 MB maximum request size, text and image input, tool calling, JSON mode and streaming, with OpenAI-compatible chat completions and Anthropic-compatible Messages via POST /v1/messages. RunInfra states zero data retention by default and that customer data is never used for training, with automatic prefix caching billed at the cached input rate.

read2 min views1 publishedOct 17, 2026
The fastest DeepSeek v4.1 flash 1017 – Day 0
Image: source

deepseek-ai/DeepSeek-V4.1-Flash-1017 DeepSeek V4.1 Flash 1017 is an LLM listed in RunInfra Model APIs. RunInfra serves it as deepseek-ai/DeepSeek-V4.1-Flash-1017 at $0.15 per 1M input tokens and $0.60 per 1M output tokens. Its context window is 1,048,576 tokens. The API provides OpenAI-compatible chat completions and Anthropic-compatible Messages, POST /v1/messages.

Pricing #

USD, pay per token

  • per 1M input tokens
  • $0.15
  • per 1M cached input tokens
  • $0.01
  • per 1M output tokens
  • $0.60

Measured performance #

Access #

Confirm how your client reaches this model.

Capacity #

Check the limits your workload must fit.

  • Context window
  • 1,048,576 tokens
  • Maximum request size
  • 3.5 MB per request
  • Maximum generated output
  • 1,048,576 tokens

Capabilities #

See which request modes the API supports.

  • Tool calling
  • Supported
  • JSON mode
  • Supported
  • Streaming
  • Supported

View full spec #

  • Gateway compatibility
  • OpenAI- and Anthropic-compatible APIs for compatible clients, tools, and gateways
  • Prefix caching
  • Automatic prefix caching runs on every replica, and requests from the same session or conversation are routed back to the replica that holds their cached prefix. A stable session hint or prompt_cache_key provides explicit grouping; otherwise RunInfra derives it from the start of the conversation or, failing that, the workspace. Hits remain best effort until eviction or a serve restart, never guaranteed. Cached input is billed at the cached input rate. Responses report cached token counts.
  • Cache retention
  • Prefix cache is held on the serving GPU and evicted under memory pressure. Retention is best effort.
  • Upstream model
  • View model
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