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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. Gemini 3.1 Flash Lite Image (Google AI Studio)GoogleEnterprise & partner accessRemove
  2. DeepSeek V4.1 Flash 1017DeepSeekRemove
gemini-3.1-flash-lite-image-aistudio vs deepseek-v4.1-flash-1017
AttributeGemini 3.1 Flash Lite Image (Google AI Studio)gemini-3.1-flash-lite-image-aistudioDeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017
Pricing
Request$0.0336 / request—
BillingPay Per Request—
Cache Write (5m)Not applicable$0.10 / 1M
Cache Write (1h)Not applicable$0.10 / 1M
Cache ReadNot applicable$0.10 / 1M
Input—$0.10 / 1M
Output—$0.50 / 1M
Web Search—$0 / 1M
Context
Max contextN/A1M
Max outputN/AN/A
Capabilities
VisionYesYes
Function CallingNoYes
JSON ModeNoYes
StreamingNoYes
Catalogue
ProviderGoogleDeepSeek
Categoryimagechat
Charge typePay Per RequestPay As You Go
Released——
Description
SummaryGemini 3.1 Flash Lite Image served from Google AI Studio. Billed per image at US$0.0336. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 1K. A request for a larger size returns a 1K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size.DeepSeek 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.