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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 Pro Image (Google AI Studio)GoogleEnterprise & partner accessRemove
  2. Muse WonderMetaRemove
  3. Muse Spark OpenMetaRemove
gemini-3-pro-image-aistudio vs muse-wonder vs muse-spark-open
AttributeGemini 3 Pro Image (Google AI Studio)gemini-3-pro-image-aistudioMuse Wondermuse-wonderMuse Spark Openmuse-spark-open
Pricing
Request$0.134 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$4.00 / 1M$1.25 / 1M
Cache Write (1h)Not applicable$4.00 / 1M$1.25 / 1M
Cache ReadNot applicable$4.00 / 1M$1.25 / 1M
Input—$4.00 / 1M$1.25 / 1M
Output—$20.00 / 1M$4.25 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleMetaMeta
Categoryimagechatchat
Charge typePay Per RequestPay As You GoPay As You Go
Released———
Description
SummaryGemini 3 Pro Image served from Google AI Studio. Billed per image at US$0.134. 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 2K. A request for a larger size, such as 4K or 4096x4096, returns a 2K 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, for example 4096x4096->2048x2048.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.Muse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential.