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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 4 ArgonGoogleRemove
  3. Claude Opus 4.7AnthropicRemove
gpt-6.1-terra vs gemini-4-argon vs claude-opus-4-7
AttributeGPT-6.1 Terragpt-6.1-terraGemini 4 Argongemini-4-argonClaude Opus 4.7claude-opus-4-7
Pricing
Input$1.00 / 1M$2.00 / 1M$5.00 / 1M
Output$5.00 / 1M$10.00 / 1M$25.00 / 1M
Cache Write (5m)$1.00 / 1M$2.00 / 1M$6.25 / 1M
Cache Write (1h)$1.00 / 1M$2.00 / 1M$10.00 / 1M
Cache Read$1.00 / 1M$2.00 / 1M$0.50 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingNoYesYes
Catalogue
ProviderOpenAIGoogleAnthropic
Categorychatchatchat
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 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.Opus 4.7 is the next generation of Anthropic's Opus family, designed for long-running, asynchronous agent workflows. Building on Opus 4.6, it delivers stronger performance on complex, multi-step tasks and more reliable execution across extended pipelines such as large codebases, multi-stage debugging, and end-to-end project orchestration. Beyond coding, Opus 4.7 enhances knowledge work capabilities, including document drafting, presentation creation, and data analysis. With strong coherence over long outputs and sessions, it is well suited for tasks requiring persistence, judgment, and sustained execution.