DeepSeek V4.1 Flash 1017 launches with 552B CED backbone, 1M context, $0.15/$0.60 per 1M tokens

DeepSeek has released DeepSeek V4.1 Flash 1017, an October 17, 2026 snapshot of its V4.1 Flash model. It is the first DeepSeek model built on the company's Causal Encoder-Decoder (CED) architecture. It has a 1.0M-token context window and is priced at $0.15 per 1M input tokens and $0.60 per 1M output tokens, according to its OpenRouter listing.

Key specifications

• Architecture: Sparse mixture-of-experts on a 552B-parameter backbone, using the CED design
• Active parameters: 8B on input and 16B on output, an asymmetric split DeepSeek says keeps per-token compute low relative to total size
• Context window: 1.0M tokens
• Pricing (DeepSeek endpoint): $0.15 input / $0.60 output per 1M tokens; cache reads $0.003 per 1M tokens
• API model name: deepseek-flash; OpenRouter ID deepseek/deepseek-v4.1-flash-1017
• Release date: October 17, 2026
• Benchmark scores and training cutoff: not yet disclosed in the source material

Native vision and compressed caching

Image understanding is built into the architecture. Visual and text embeddings were trained jointly from the start of pre-training. The earlier experimental V4 Flash Vision Exp (13B active out of 284B total) added vision to an existing text model instead.

DeepSeek also says compressed KV caching cuts cache memory to roughly a quarter of the previous Flash generation. The company claims this significantly lowers costs on agentic workloads. No independent measurements have been published.

Intended use and company claims

DeepSeek positions the model as the cost-efficient tier of the V4.1 family. Its stated targets are:

• Coding, terminal and computer-use agents
• Long-horizon tasks that must run across many steps
• Long-context analysis

DeepSeek claims the model exceeds V4 Pro on performance, speed and task completion time. No benchmark figures accompany the claim, so it cannot be verified yet. V4 Pro 0423 had 1.6T total and 49B active parameters, so the claim covers a model with far fewer active parameters than its predecessor.

Availability

The snapshot is served by DeepSeek and listed on OpenRouter, which reports 99.48% availability over the last three days. It is not currently available through EU in-region routing. OpenRouter also lists generic V4.1 Flash entries at different rates, $0.0219 / $0.6246 and $0.05 / $0.15 per 1M tokens. The source does not say whether those reflect other providers or other configurations. Developers who want a stable target should pin the dated snapshot, since DeepSeek also offers "Flash Latest" aliases that redirect to the newest Flash model.

What this means

The 552B backbone is nearly double the 284B total of V4 Flash 0731, while active parameters are 8B on input and 16B on output versus 13B before. DeepSeek is spending more on stored capacity and less on per-token compute, which suits high-volume agent loops where cost scales with tokens processed.

The cache-memory reduction may matter as much as the headline price. Long-running agents spend much of their budget re-reading accumulated context, so a cache a quarter the size could lower serving costs for those workloads. DeepSeek's cache-read price of $0.003 per 1M tokens points the same way.

Treat the V4 Pro comparison cautiously until DeepSeek publishes benchmark data. The asymmetric input/output activation also makes direct comparison with dense or symmetric MoE models harder, so teams should test on their own agent workloads before migrating.