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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-5 ProOpenAIRemove
  2. GPT-6.1 TerraOpenAIRemove
  3. Gemini 4 ArgonGoogleRemove
gpt-5-pro vs gpt-6.1-terra vs gemini-4-argon
AttributeGPT-5 Progpt-5-proGPT-6.1 Terragpt-6.1-terraGemini 4 Argongemini-4-argon
Pricing
Input$7.50 / 1M$1.00 / 1M$2.00 / 1M
Output$60.00 / 1M$5.00 / 1M$10.00 / 1M
Cache Write (5m)$7.50 / 1M$1.00 / 1M$2.00 / 1M
Cache Write (1h)$7.50 / 1M$1.00 / 1M$2.00 / 1M
Cache Read$7.50 / 1M$1.00 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context400K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesNoYes
Catalogue
ProviderOpenAIOpenAIGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryGPT-5 Pro is OpenAI's top model, optimized for complex, high-stakes tasks that require careful step-by-step reasoning and precise instruction following. It delivers stronger code quality, clearer writing, and better factual reliability, with support for test-time routing and intent cues like “think hard about this.” It also reduces hallucinations and sycophancy while improving performance across coding, writing, and health-related workloads.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.