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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. GPT-5.6 TerraOpenAIRemove
  2. Claude Sonnet 5.5AnthropicRemove
  3. GPT-6.1 SolOpenAIRemove
gpt-5.6-terra vs claude-sonnet-5.5 vs gpt-6.1-sol
AttributeGPT-5.6 Terragpt-5.6-terraClaude Sonnet 5.5claude-sonnet-5.5GPT-6.1 Solgpt-6.1-sol
Pricing
Input$2.00 / 1M$2.00 / 1M$2.00 / 1M
Output$12.00 / 1M$10.00 / 1M$10.00 / 1M
Cache Write (5m)$2.00 / 1M$2.50 / 1M$2.00 / 1M
Cache Write (1h)$2.00 / 1M$4.00 / 1M$2.00 / 1M
Cache Read$2.00 / 1M$0.20 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderOpenAIAnthropicOpenAI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryGPT-5.6 Terra is the balanced model in OpenAI's GPT-5.6 series, positioned between the flagship Sol tier and the cost-efficient Luna tier. It is designed for everyday coding, reasoning, and agentic workflows, delivering strong performance while balancing capability and cost. Offering near-flagship quality at approximately half the cost of Sol, GPT-5.6 Terra is well suited for production applications that require reliable reasoning, software development, and scalable agent execution.Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, serving as a direct upgrade to Sonnet 5. It excels at feature development, bug fixing, and creating polished documents, presentations, and spreadsheets, while offering clearer writing and communication than its predecessor.GPT-6.1 Sol is an upgraded high-end model in OpenAI's GPT-6 series, positioned below the flagship GPT-6 Astra. It is optimized for agentic coding, computer use, document-heavy professional work, and multi-step business automation, delivering near-Astra-level capability at significantly lower cost. Compared with GPT-6 Sol, it offers improved factual reliability and stronger adherence to explicit constraints and user intent, making it well suited for complex, long-running agentic workflows where accurate and dependable execution is critical.