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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. GPT Image 2OpenAIRemove
  2. Muse WonderMetaRemove
  3. Muse Spark OpenMetaRemove
  4. Gemini 4 ArgonGoogleRemove

4 is the maximum. Remove one to add another.

gpt-image-2 vs muse-wonder vs muse-spark-open vs gemini-4-argon
AttributeGPT Image 2gpt-image-2Muse Wondermuse-wonderMuse Spark Openmuse-spark-openGemini 4 Argongemini-4-argon
Pricing
Request$0.04 / request———
BillingPay Per Request———
Cache Write (5m)Not applicable$4.00 / 1M$1.25 / 1M$2.00 / 1M
Cache Write (1h)Not applicable$4.00 / 1M$1.25 / 1M$2.00 / 1M
Cache ReadNot applicable$4.00 / 1M$1.25 / 1M$2.00 / 1M
Input—$4.00 / 1M$1.25 / 1M$2.00 / 1M
Output—$20.00 / 1M$4.25 / 1M$10.00 / 1M
Web Search—$0 / 1M$0 / 1M$0 / 1M
Context
Max context272K1M1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesYes
Function CallingNoYesYesYes
JSON ModeNoYesYesYes
StreamingNoYesYesYes
Catalogue
ProviderOpenAIMetaMetaGoogle
Categoryimagechatchatchat
Charge typePay Per RequestPay As You GoPay As You GoPay As You Go
Released————
Description
SummaryGPT Image 2 combines OpenAI's GPT-5.4 with advanced image generation capabilities from GPT Image 2, enabling fully integrated multimodal workflows. It allows users to seamlessly transition between reasoning, coding, and visual generation within a single interaction, making it well suited for creative, development, and agent-driven applications that require both intelligence and visual output.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.Gemini 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.