DeepSeek-V4.1-Flash Is Now on DeepInfra

DeepInfra has launched DeepSeek-V4.1-Flash, an open-weight MIT-licensed multimodal model designed to reduce the cost of long-context and agentic inference through a Causal Encoder-Decoder architecture, sparse conditional memory, and compressed attention caching. Although its backbone contains 552 billion parameters, it activates 8 billion during input prefill and 16 billion during decoding, while its 890-byte-per-token global KV cache is reported to be four times smaller than V4-Flash’s and supports a 1,048,576-token context window. DeepInfra and DeepSeek report strong results in coding and agent benchmarks, including leading comparison-table scores for Codeforces, Terminal-Bench 2.1, CyberGym, AutomationBench, and Agent’s Last Exam, though it trails some competing models on GPQA Diamond and newer Terminal-Bench versions. The model supports text-and-image input, JSON mode, function calling, and adjustable reasoning effort, and DeepInfra offers it through an OpenAI-compatible API with Standard, Priority, and lower-cost Flex pricing tiers, including especially low prices for cached input tokens.

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