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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 Image (Google AI Studio)GoogleEnterprise & partner accessRemove
  2. Muse Spark OpenMetaRemove
  3. Muse WonderMetaRemove
gemini-3.1-flash-image-aistudio vs muse-spark-open vs muse-wonder
AttributeGemini 3.1 Flash Image (Google AI Studio)gemini-3.1-flash-image-aistudioMuse Spark Openmuse-spark-openMuse Wondermuse-wonder
Pricing
Request$0.067 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$1.25 / 1M$4.00 / 1M
Cache Write (1h)Not applicable$1.25 / 1M$4.00 / 1M
Cache ReadNot applicable$1.25 / 1M$4.00 / 1M
Input—$1.25 / 1M$4.00 / 1M
Output—$4.25 / 1M$20.00 / 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 3.1 Flash Image served from Google AI Studio. Billed per image at US$0.067. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI.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.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.