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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 Vertex AI)GoogleRemove
  2. Muse WonderMetaRemove
  3. Qwen3.5 397B A17BAlibabaRemove
gemini-3.1-flash-lite-image-vertex vs muse-wonder vs qwen3.5-397b-a17b
AttributeGemini 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertexMuse Wondermuse-wonderQwen3.5 397B A17Bqwen3.5-397b-a17b
Pricing
Request$0.0336 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$4.00 / 1M$0.55 / 1M
Cache Write (1h)Not applicable$4.00 / 1M$0.55 / 1M
Cache ReadNot applicable$4.00 / 1M$0.55 / 1M
Input—$4.00 / 1M$0.55 / 1M
Output—$20.00 / 1M$3.50 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A1M256K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleMetaAlibaba
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.Qwen3.5-397B-A17B is a native vision-language model built on a hybrid architecture that combines linear attention mechanisms with a sparse Mixture-of-Experts (MoE) design to achieve higher inference efficiency at large scale. It delivers state-of-the-art performance across a broad range of tasks, including language understanding, logical reasoning, code generation, agent-based workflows, image and video understanding, and GUI interaction. With strong coding and agent capabilities, Qwen3.5-397B-A17B demonstrates robust generalization across diverse multimodal and agentic scenarios, making it well suited for advanced applications that require integrated reasoning across text, vision, and interactive environments.