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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. GPT-6.1 TerraOpenAIRemove
  3. GPT-5.1-Codex-MaxOpenAIRemove
gemini-4-argon vs gpt-6.1-terra vs gpt-5.1-codex-max
AttributeGemini 4 Argongemini-4-argonGPT-6.1 Terragpt-6.1-terraGPT-5.1-Codex-Maxgpt-5.1-codex-max
Pricing
Input$2.00 / 1M$1.00 / 1M$0.375 / 1M
Output$10.00 / 1M$5.00 / 1M$3.00 / 1M
Cache Write (5m)$2.00 / 1M$1.00 / 1M$0.375 / 1M
Cache Write (1h)$2.00 / 1M$1.00 / 1M$0.375 / 1M
Cache Read$2.00 / 1M$1.00 / 1M$0.375 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M400K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesNoYes
Catalogue
ProviderGoogleOpenAIOpenAI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay 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.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.GPT-5.1 Codex Max is OpenAI's advanced agentic coding model, built for long-running, high-context development work. Using an upgraded 5.1 reasoning stack and training on real engineering workflows, it delivers faster performance, stronger reasoning, and better token efficiency across the full software lifecycle.