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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-6.1 TerraOpenAIRemove
  2. Gemini 2.5 Flash Image (Nano Banana)GoogleRemove
  3. Gemini 4 ArgonGoogleRemove
gpt-6.1-terra vs gemini-2.5-flash-image vs gemini-4-argon
AttributeGPT-6.1 Terragpt-6.1-terraGemini 2.5 Flash Image (Nano Banana)gemini-2.5-flash-imageGemini 4 Argongemini-4-argon
Pricing
Input$1.00 / 1M$0.30 / 1M$2.00 / 1M
Output$5.00 / 1M$2.50 / 1M$10.00 / 1M
Cache Write (5m)$1.00 / 1MNot applicable$2.00 / 1M
Cache Write (1h)$1.00 / 1MNot applicable$2.00 / 1M
Cache Read$1.00 / 1MNot applicable$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1MN/A1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingNoYesYes
Catalogue
ProviderOpenAIGoogleGoogle
Categorychatimagechat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryGPT-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 2.5 Flash Image (“Nano Banana”) is now generally available. It’s a state-of-the-art image generation model with strong contextual understanding, supporting image creation, editing, and multi-turn conversational workflows around visuals.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.