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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. Gemini Embedding 2GoogleRemove
deepseek-v4.1-flash-1017 vs gemini-embedding-2-preview
AttributeDeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017Gemini Embedding 2gemini-embedding-2-preview
Pricing
Input$0.20 / 1M$0.60 / 1M
Output$0.80 / 1M$2.40 / 1M
Cache Write (5m)$0.20 / 1M$0.60 / 1M
Cache Write (1h)$0.20 / 1M$0.60 / 1M
Cache Read$0.20 / 1M$0.60 / 1M
Web Search$0 / 1M—
Context
Max context1M8.2K
Max outputN/AN/A
Capabilities
VisionYesYes
Function CallingYesYes
JSON ModeYesNo
StreamingYesNo
Catalogue
ProviderDeepSeekGoogle
Categorychatembedding
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 Embedding 2 is Google's advanced text embedding model designed for high-accuracy semantic representation across large-scale retrieval and understanding tasks. It converts text into dense vector embeddings optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. Built for production use, it offers strong multilingual support, improved semantic similarity accuracy, and efficient embedding generation, making it well suited for large knowledge indexing pipelines and enterprise-scale retrieval applications.