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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 4 ArgonGoogleRemove
  2. Gemini 3 Pro Image (Google AI Studio)GoogleEnterprise & partner accessRemove
  3. Muse WonderMetaRemove
gemini-4-argon vs gemini-3-pro-image-aistudio vs muse-wonder
AttributeGemini 4 Argongemini-4-argonGemini 3 Pro Image (Google AI Studio)gemini-3-pro-image-aistudioMuse Wondermuse-wonder
Pricing
Input$2.00 / 1M—$4.00 / 1M
Output$10.00 / 1M—$20.00 / 1M
Cache Write (5m)$2.00 / 1MNot applicable$4.00 / 1M
Cache Write (1h)$2.00 / 1MNot applicable$4.00 / 1M
Cache Read$2.00 / 1MNot applicable$4.00 / 1M
Web Search$0 / 1M—$0 / 1M
Request—$0.134 / request—
Billing—Pay Per Request—
Context
Max context1MN/A1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingYesNoYes
Catalogue
ProviderGoogleGoogleMeta
Categorychatimagechat
Charge typePay As You GoPay Per RequestPay As You Go
Released———
Description
SummaryGemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.Gemini 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.