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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. Gemini 3.1 Flash Image (Google Vertex AI)GoogleEnterprise & partner accessRemove
  3. Muse Spark 1.3MetaRemove
gemini-4-argon vs gemini-3.1-flash-image-vertex vs muse-spark-1.3
AttributeGemini 4 Argongemini-4-argonGemini 3.1 Flash Image (Google Vertex AI)gemini-3.1-flash-image-vertexMuse Spark 1.3muse-spark-1.3
Pricing
Input$2.00 / 1M—$1.25 / 1M
Output$10.00 / 1M—$4.25 / 1M
Cache Write (5m)$2.00 / 1MNot applicable$1.25 / 1M
Cache Write (1h)$2.00 / 1MNot applicable$1.25 / 1M
Cache Read$2.00 / 1MNot applicable$1.25 / 1M
Web Search$0 / 1M—$0 / 1M
Request—$0.067 / 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 3.1 Flash Image served from Google Vertex AI. 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. 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 Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows.