DeepSeek V4.1 Flash 1017
by DeepSeek · 552B (A8B/A16B) parameters
Today's price · per 1M tokens
Input
$0.300
per 1M tokens
Output
$1.20
per 1M tokens
Blended
$0.525
blended $/1M — 3:1 weighted input:output
Cached input
$0.006
2% of input — prompt caching
How this price is scoped: DeepSeek bills this model at TWO rates depending on the hour. The tracked figure is the PEAK rate: $0.30 input (cache miss) / $0.006 input (cache hit) / $1.20 output per 1M tokens. Off-peak is exactly half: $0.15 / $0.003 / $0.60. Peak hours are 01:00-04:00 and 06:00-10:00 UTC, Monday through Friday; every other hour, and all of Saturday and Sunday, is off-peak. Peak is the headline here because DeepSeek defines off-peak as half of peak rather than the other way round, so peak is the list rate, and because a caller who does not schedule around the clock needs the published number to be a ceiling rather than a floor. Halve it for a workload that runs entirely outside those hours. Third-party catalogues that quote this model at $0.15/$0.60 per 1M are printing the OFF-PEAK tier, not a different rate card.
Price receipt
We read DeepSeek's own pricing page on and found DeepSeek-V4.1-Flash-1017 listed with a price on the same column — that read is where the number above comes from, recorded as a new model. We keep the page text we read; its content hash is 87d1681893.
Overview
DeepSeek's 2026-10-17 release of V4.1 Flash: new open weights (MIT, on Hugging Face and ModelScope) on the same 552B-parameter causal encoder-decoder MoE (8B parameters active for input, 16B for output), with native visual understanding, a 1M-token context and 384K max output. It took over the deepseek-flash API name from the September build, which is no longer served, at the same price. DeepSeek reports a Pareto improvement in performance and cost: Terminal-Bench 4.0 31.2 to 42.3, Humanity's Last Exam without tools 36.8 to 42.6, DeepSWE v1.1 74.2 to 77.0, with faster task completion and shorter outputs, so a task costs fewer output tokens at unchanged per-token rates; it was trained with reinforcement learning from self-feedback for end-to-end project building. $0.30/$1.20 per 1M tokens at peak (01:00-04:00 and 06:00-10:00 UTC, Monday to Friday); half that in every other hour.
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Capabilities
struck through = not supportedBenchmark performance
accuracy % · higher is betterEvery per-benchmark score we hold for this model is reported by its own vendor, so none is counted toward an accuracy average or any ranking.
Source: DeepSeek model card (HF README, HLE w/o tools, text-only subset) · updated · See full rankings →
Specifications
- Provider
- DeepSeek
- Context window
- 1M tokens
- Modality
- text, image
- Parameters
- 552B (A8B/A16B)
- Open source
- Yes — open weights available
- Released
- Status
- Current
- Last updated
- Tags
Availability verified: — listed on DeepSeek's own page