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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 FlashDeepSeekRemove
  2. Gemini 3.8 FlashGoogleRemove
  3. DeepSeek V4 ProDeepSeekRemove
deepseek-v4.1-flash vs gemini-3.8-flash vs deepseek-v4-pro
AttributeDeepSeek V4.1 Flashdeepseek-v4.1-flashGemini 3.8 Flashgemini-3.8-flashDeepSeek V4 Prodeepseek-v4-pro
Pricing
Input$0.30 / 1M$0.75 / 1M$0.435 / 1M
Output$1.20 / 1M$3.75 / 1M$0.87 / 1M
Cache Write (5m)$0.30 / 1M$0.75 / 1M$0.435 / 1M
Cache Write (1h)$0.30 / 1M$0.75 / 1M$0.435 / 1M
Cache Read$0.30 / 1M$0.75 / 1M$0.435 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1.0M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderDeepSeekGoogleDeepSeek
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryDeepSeek 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.Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.DeepSeek V4 Pro is a large-scale Mixture-of-Experts (MoE) model with 1.6T total parameters and 49B activated per token, supporting a 1M-token context window for advanced reasoning and long-horizon workflows. It delivers strong performance across knowledge, mathematics, and software engineering tasks, making it suitable for complex, real-world applications. Built on a hybrid attention architecture for efficient long-context processing, the model supports configurable reasoning modes to balance speed and depth. It is well suited for full codebase analysis, multi-step automation, and large-scale information synthesis, where both capability and efficiency are essential.https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro