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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. S1Fish AudioRemove
  3. Gemini 4 CarbonGoogleRemove
gpt-6.1-terra vs s1 vs gemini-4-carbon
AttributeGPT-6.1 Terragpt-6.1-terraS1s1Gemini 4 Carbongemini-4-carbon
Pricing
Input$1.00 / 1M— Not priced per input token$2.00 / 1M
Output$5.00 / 1M— Not priced per output token$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
VisionYesNoYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingNoNoYes
Catalogue
ProviderOpenAIFish AudioGoogle
Categorychatvoicechat
Charge typePay As You GoPay As You GoPay As You Go
Released—2026-07-29—
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.Fish Audio S1 text-to-speech. Billed per UTF-8 byte of input text.Gemini 4 Carbon is Google's frontier multimodal reasoning model, designed for advanced coding, agentic workflows, and complex multi-step problem solving. It supports text, image, audio, video, and file inputs, enabling integrated reasoning and analysis across diverse modalities. Optimized for long-horizon execution and demanding professional workloads, Gemini 4 Carbon supports up to 1M output tokens through continuation and configurable reasoning effort levels (low, medium, high) to balance reasoning depth, latency, and cost. It is well suited for autonomous agents, software engineering, multimodal analysis, and large-scale workflow automation.