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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. Gemini 3.1 Flash Lite Image (Google Vertex AI)GoogleRemove
  2. Muse WonderMetaRemove
  3. KAT-Coder-Pro V1KwaipilotRemove
gemini-3.1-flash-lite-image-vertex vs muse-wonder vs kat-coder-pro
AttributeGemini 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertexMuse Wondermuse-wonderKAT-Coder-Pro V1kat-coder-pro
Pricing
Request$0.0336 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$4.00 / 1M$0.005 / 1M
Cache Write (1h)Not applicable$4.00 / 1M$0.005 / 1M
Cache ReadNot applicable$4.00 / 1M$0.005 / 1M
Input—$4.00 / 1M$0.005 / 1M
Output—$20.00 / 1M$0.005 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A1M256K
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleMetaKwaipilot
Categoryimagechatchat
Charge typePay Per RequestPay As You GoPay As You Go
Released———
Description
SummaryGemini 3.1 Flash Lite Image served from Google Vertex AI. 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.Muse Wonder is Meta’s frontier multimodal reasoning model, designed for complex personal and professional agentic workflows. It supports text, image, video, audio, and PDF inputs with text output and features a 1M-token context window for sustained reasoning across long-running tasks. Optimized for autonomous execution and multi-agent orchestration, Muse Wonder can plan, delegate to subagents, coordinate parallel tool use, recover from errors, and operate browsers and applications across desktop and mobile environments. With cross-conversation context retrieval and its Active Focus training approach, it is well suited for complex software engineering, multi-file refactoring, computer use, and end-to-end workflows requiring persistence, adaptability, and minimal supervision.KAT-Coder-Pro V1 is KwaiKAT’s most advanced agentic coding model, built specifically for real-world software engineering. It achieves a 73.4% solve rate on SWE-Bench Verified and is optimized for tool use, multi-turn workflows, instruction following, and generalization through a multi-stage training pipeline that includes SFT, RFT, and large-scale agentic RL.