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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. Claude Sonnet 5.5AnthropicRemove
  3. Qwen3.8 27BAlibabaRemove
gemini-3.1-flash-lite-image-aistudio vs claude-sonnet-5.5 vs qwen3.8-27b
AttributeGemini 3.1 Flash Lite Image (Google AI Studio)gemini-3.1-flash-lite-image-aistudioClaude Sonnet 5.5claude-sonnet-5.5Qwen3.8 27Bqwen3.8-27b
Pricing
Request$0.0336 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$2.50 / 1M$0.45 / 1M
Cache Write (1h)Not applicable$4.00 / 1M$0.45 / 1M
Cache ReadNot applicable$0.10 / 1M$0.45 / 1M
Input—$2.00 / 1M$0.45 / 1M
Output—$10.00 / 1M$3.20 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A1M262K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleAnthropicAlibaba
Categoryimagechatchat
Charge typePay Per RequestPay As You GoPay 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.Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, serving as a direct upgrade to Sonnet 5. It excels at feature development, bug fixing, and creating polished documents, presentations, and spreadsheets, while offering clearer writing and communication than its predecessor.Qwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.