Skip to content

Compare models

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. Gemini 3 Flash PreviewGoogleRemove
deepseek-v4.1-flash-1017 vs gemini-3-flash-preview
AttributeDeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017Gemini 3 Flash Previewgemini-3-flash-preview
Pricing
Input$0.10 / 1M$0.25 / 1M
Output$0.50 / 1M$1.50 / 1M
Cache Write (5m)$0.10 / 1M$0.25 / 1M
Cache Write (1h)$0.10 / 1M$0.25 / 1M
Cache Read$0.10 / 1M$0.25 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context1M1.0M
Max outputN/AN/A
Capabilities
VisionYesYes
Function CallingYesYes
JSON ModeYesYes
StreamingYesYes
Catalogue
ProviderDeepSeekGoogle
Categorychatchat
Charge typePay As You GoPay As You Go
Released——
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.Gemini 3 Flash Preview is a fast, cost-efficient reasoning model designed for agent workflows, multi-turn chat, and coding assistance. It offers near-Pro level reasoning and tool-use performance with significantly lower latency than larger Gemini models, making it ideal for interactive development and long-running agent loops. It improves on Gemini 2.5 Flash with stronger reasoning, multimodal understanding, and reliability. The model supports a 1M-token context window and multimodal inputs (text, images, audio, video, PDFs) with text outputs. It provides configurable reasoning levels, structured output formats, tool use, and automatic context caching—optimized for users seeking strong agentic reasoning without the cost or latency of full frontier-scale models.