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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 Vertex AI)GoogleEnterprise & partner accessRemove
  2. Muse Spark OpenMetaRemove
  3. Gemini 4 ArgonGoogleRemove
gemini-3-pro-image-vertex vs muse-spark-open vs gemini-4-argon
AttributeGemini 3 Pro Image (Google Vertex AI)gemini-3-pro-image-vertexMuse Spark Openmuse-spark-openGemini 4 Argongemini-4-argon
Pricing
Request$0.134 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$1.25 / 1M$2.00 / 1M
Cache Write (1h)Not applicable$1.25 / 1M$2.00 / 1M
Cache ReadNot applicable$1.25 / 1M$2.00 / 1M
Input—$1.25 / 1M$2.00 / 1M
Output—$4.25 / 1M$10.00 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleMetaGoogle
Categoryimagechatchat
Charge typePay Per RequestPay As You GoPay As You Go
Released———
Description
SummaryGemini 3 Pro Image served from Google Vertex AI. 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.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.