DeepSeek v4.1 Flash at 378 tok/s 99.7% Cache hit rate

deepseek-ai/DeepSeek-V4.1-Flash DeepSeek V4.1 Flash is an LLM listed in RunInfra Model APIs. RunInfra serves it as deepseek-ai/DeepSeek-V4.1-Flash at $0.10 per 1M input tokens until Oct 13, 2026, 5:44 AM UTC, with standard rates resuming automatically. 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
• $0.10
• per 1M cached input tokens
• $0.02
• per 1M output tokens
• $0.43

Normally $0.14

Normally $0.03

Normally $0.58

Input and output are 25% off until .

05days

22hrs

55min

38sec

Ends in 5 days 22 hours Standard rates resume automatically when the window ends.

Measured performance #

Access #

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• Provider

• DeepSeek

• API compatibility

• OpenAI-compatible chat completions

• Anthropic compatibility

• Anthropic-compatible Messages, POST /v1/messages

• Accepted input

• Text only

• Availability

• Available

• Data retention

• Zero data retention by default. Never used for training.

Capacity #

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

Capabilities #

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• 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