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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 4 ArgonGoogleRemove
  2. GPT-6 Luna ProOpenAIRemove
  3. Muse WonderMetaRemove
gemini-4-argon vs gpt-6-luna-pro vs muse-wonder
AttributeGemini 4 Argongemini-4-argonGPT-6 Luna Progpt-6-luna-proMuse Wondermuse-wonder
Pricing
Input$2.00 / 1M$0.10 / 1M$4.00 / 1M
Output$10.00 / 1M$0.50 / 1M$20.00 / 1M
Cache Write (5m)$2.00 / 1M$0.10 / 1M$4.00 / 1M
Cache Write (1h)$2.00 / 1M$0.10 / 1M$4.00 / 1M
Cache Read$2.00 / 1M$0.10 / 1M$4.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1.1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleOpenAIMeta
Categorychatchatchat
Charge typePay As You GoPay As You GoPay 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.GPT-6 Luna Pro uses the same underlying model as GPT-6 Luna, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. It combines Luna's speed and cost efficiency with deeper reasoning and more reliable multi-step execution, making it well suited for advanced coding, computer use, and agentic workflows where higher solution quality is needed without moving to a larger GPT-6 tier.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.