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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. DeepSeek V4.1 Flash 1017DeepSeekRemove
  2. Perceptron Mk1.5PerceptronRemove
  3. DeepSeek V4.1 FlashDeepSeekRemove
deepseek-v4.1-flash-1017 vs perceptron-mk1.5 vs deepseek-v4.1-flash
AttributeDeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017Perceptron Mk1.5perceptron-mk1.5DeepSeek V4.1 Flashdeepseek-v4.1-flash
Pricing
Input$0.10 / 1M$0.15 / 1M$0.30 / 1M
Output$0.50 / 1M$1.50 / 1M$1.20 / 1M
Cache Write (5m)$0.10 / 1M$0.15 / 1M$0.30 / 1M
Cache Write (1h)$0.10 / 1M$0.15 / 1M$0.30 / 1M
Cache Read$0.10 / 1M$0.15 / 1M$0.30 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M36.9K1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingYesYesYes
Catalogue
ProviderDeepSeekPerceptronDeepSeek
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released—2026-09-25—
Description
SummaryDeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek’s cost-efficient Mixture-of-Experts (MoE) model, built on its innovative Causal Encoder-Decoder (CED) architecture. Featuring 552B total parameters with 8B activated for input and 16B for output, it combines efficient inference with native multimodal understanding through jointly trained text and visual representations. Optimized for coding, terminal and computer-use agents, and long-horizon workflows, the model introduces compressed KV caching that reduces cache memory usage to approximately one-quarter of the previous Flash generation. DeepSeek reports that it surpasses V4 Pro in performance, inference speed, and task completion time, making it well suited for cost-efficient autonomous agents, complex software engineering, and long-context analysis.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.DeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion.