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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. GPT-6 AstraOpenAIRemove
  2. Gemini 3.1 Flash Image (Google Vertex AI)GoogleEnterprise & partner accessRemove
  3. Gemini 4 ArgonGoogleRemove
gpt-6-astra vs gemini-3.1-flash-image-vertex vs gemini-4-argon
AttributeGPT-6 Astragpt-6-astraGemini 3.1 Flash Image (Google Vertex AI)gemini-3.1-flash-image-vertexGemini 4 Argongemini-4-argon
Pricing
Input$10.00 / 1M—$2.00 / 1M
Output$50.00 / 1M—$10.00 / 1M
Cache Write (5m)$10.00 / 1MNot applicable$2.00 / 1M
Cache Write (1h)$10.00 / 1MNot applicable$2.00 / 1M
Cache Read$10.00 / 1MNot applicable$2.00 / 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
ProviderOpenAIGoogleGoogle
Categorychatimagechat
Charge typePay As You GoPay Per RequestPay As You Go
Released———
Description
SummaryGPT-6 Astra is OpenAI's flagship model for demanding end-to-end professional work, designed for advanced analysis, software engineering, deep research, scientific tasks, and document creation. It is particularly strong in long-horizon agentic workflows, including tasks that require sustained reasoning, tool orchestration, and computer and browser use, making it well suited for complex autonomous workflows and production-grade knowledge work.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.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.