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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 Nano Banana 2.1 (Google AI Studio)GoogleEnterprise & partner accessRemove
  2. GPT-6.1 TerraOpenAIRemove
  3. Gemini 4 ArgonGoogleRemove
gemini-nano-banana-2.1-aistudio vs gpt-6.1-terra vs gemini-4-argon
AttributeGemini Nano Banana 2.1 (Google AI Studio)gemini-nano-banana-2.1-aistudioGPT-6.1 Terragpt-6.1-terraGemini 4 Argongemini-4-argon
Pricing
Request$0.0336 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$1.00 / 1M$2.00 / 1M
Cache Write (1h)Not applicable$1.00 / 1M$2.00 / 1M
Cache ReadNot applicable$1.00 / 1M$2.00 / 1M
Input—$1.00 / 1M$2.00 / 1M
Output—$5.00 / 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
StreamingNoNoYes
Catalogue
ProviderGoogleOpenAIGoogle
Categoryimagechatchat
Charge typePay Per RequestPay As You GoPay As You Go
Released———
Description
SummaryGemini Nano Banana 2.1 served from Google AI Studio. Billed per image at US$0.0336 (up to 1K).GPT-6.1 Terra is OpenAI’s mid-tier model in the GPT-6.1 series, designed to balance capability, speed, and cost efficiency. Positioned below GPT-6.1 Sol, it delivers strong performance across everyday agentic workflows, coding, and professional writing, with particular strengths in screenshot understanding and on-screen content analysis. Optimized for high-volume production workloads, GPT-6.1 Terra is well suited for coding assistants, visual interface analysis, workflow automation, and scalable agentic applications that require reliable performance at an accessible cost.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.